{"id":"gen-1793896045-uoDiIjXPn14Z3VnUTQ2X","object":"chat.completion","created":1793896045,"model":"anthropic/claude-sonnet-5.5","provider":"Claude Platform on AWS","system_fingerprint":null,"service_tier":"default","choices":[{"index":0,"logprobs":null,"finish_reason":"length","native_finish_reason":"max_tokens","message":{"role":"assistant","content":null,"refusal":null,"reasoning":"I'm planning a detailed technical explanation in Indonesian, aiming for accuracy without going overboard. I'm recalling the PDF coordinate transform: the CTM maps the unit square to the page, with x' = a·x + c·y + e and y' = b·x + d·y + f, and I'm checking how image space's top-left origin is handled against that.\n\nNow I'm working through how the linear part of the matrix decomposes into rotation, shear, and scale. The rendered width and height come from the column lengths, the rotation from atan2 of the first column, and the sign of the determinant tells me whether there's a flip. I'm also checking the Y-axis flip between PDF's upward coordinates and DOCX's downward ones.\n\nNow I'm working out how the rotation and flip flags map over to DrawingML: the PDF angle runs counterclockwise while `rot` runs clockwise, so the sign flips, and a negative determinant or a negative d term would call for a `flipV`.\n\nI'm now checking how the sign of the scale and the rotation interact: a negative vertical scale means a vertical flip, which in DrawingML is applied before the rotation. I'm also noting that the PDF's counterclockwise angle becomes a negated, clockwise value in DrawingML's y-down space.\n\nNow I'm working out how the image's wp:extent should be set for a rotated picture, since Word expects the bounding box of the rotated shape there and the unrotated size goes in the transform.\n\nNow I'm settling how the rendered size relates to the shape's extent: the unrotated size goes in `cx,cy`, and the extra room the rotated bounding box needs goes in `effectExtent`. DrawingML has no shear in its transform, so any non-zero shear means I'll have to rasterize the image and resample it.\n\nNow I'm noting the unit conversions I'll need for the EMU values: points, pixels, inches, and centimeters, plus twips and half-points for the Word-side settings. Then I'm working out the available text width by subtracting the margins from the page width, since the image sizing depends on it.\n\nNow I'm working out how the available width is computed from the page width minus the left and right margins, converted to EMU, with the indent subtracted. If the image is wider than that, I'll scale it down proportionally, and I'm checking whether the integer arithmetic for the height needs i128 to avoid overflow.\n\nNow I'm looking at floating anchors: they come out of the normal text flow, and their position depends on the anchoring paragraph, the wrap mode, and the relative position settings, along with z-order, overlap rules, and behavior inside table cells or across page breaks. Because the anchor's position and the paragraph height depend on each other, the layout becomes a circular fixed-point problem that Word resolves by iterating, so the result is not always predictable.\n\nNow I'm checking the line spacing rule for the image paragraph: an exact line height would clip inline images, so I'll use auto spacing there. I'm also giving the image paragraph keepNext so it stays with its caption, and I'm weighing whether a figure caption belongs above or below.\n\nNow I'm settling the caption and keep-with-next setup: the image paragraph stays with its caption, the caption uses a SEQ field for numbering, and I'm checking that the image height leaves room for the caption on the page.\n\nNow I'm looking at how the PNG alpha channel and the PDF soft mask relate, since Word can take an RGBA PNG but a JPEG can't carry transparency.\n\nNow I'm looking at how the PDF's soft mask turns into an alpha channel: the grayscale SMask image sets per-pixel opacity, so it has to be resampled to the base image's dimensions. Since Word can't take alpha from a JPEG, I'd decode it to RGB, merge the alpha in, and save the result as a PNG. The other cases to handle are stencil masks, color-key masks, decode-array inversion, and CMYK conversion to sRGB.\n\nNow I'm handling the WASM side, where memory is just a linear buffer that has to hold the decoded pixels while the alpha is merged in.\n\n","reasoning_details":[{"type":"reasoning.text","text":"I'm planning a detailed technical explanation in Indonesian, aiming for accuracy without going overboard. I'm recalling the PDF coordinate transform: the CTM maps the unit square to the page, with x' = a·x + c·y + e and y' = b·x + d·y + f, and I'm checking how image space's top-left origin is handled against that.\n\nNow I'm working through how the linear part of the matrix decomposes into rotation, shear, and scale. The rendered width and height come from the column lengths, the rotation from atan2 of the first column, and the sign of the determinant tells me whether there's a flip. I'm also checking the Y-axis flip between PDF's upward coordinates and DOCX's downward ones.\n\nNow I'm working out how the rotation and flip flags map over to DrawingML: the PDF angle runs counterclockwise while `rot` runs clockwise, so the sign flips, and a negative determinant or a negative d term would call for a `flipV`.\n\nI'm now checking how the sign of the scale and the rotation interact: a negative vertical scale means a vertical flip, which in DrawingML is applied before the rotation. I'm also noting that the PDF's counterclockwise angle becomes a negated, clockwise value in DrawingML's y-down space.\n\nNow I'm working out how the image's wp:extent should be set for a rotated picture, since Word expects the bounding box of the rotated shape there and the unrotated size goes in the transform.\n\nNow I'm settling how the rendered size relates to the shape's extent: the unrotated size goes in `cx,cy`, and the extra room the rotated bounding box needs goes in `effectExtent`. DrawingML has no shear in its transform, so any non-zero shear means I'll have to rasterize the image and resample it.\n\nNow I'm noting the unit conversions I'll need for the EMU values: points, pixels, inches, and centimeters, plus twips and half-points for the Word-side settings. Then I'm working out the available text width by subtracting the margins from the page width, since the image sizing depends on it.\n\nNow I'm working out how the available width is computed from the page width minus the left and right margins, converted to EMU, with the indent subtracted. If the image is wider than that, I'll scale it down proportionally, and I'm checking whether the integer arithmetic for the height needs i128 to avoid overflow.\n\nNow I'm looking at floating anchors: they come out of the normal text flow, and their position depends on the anchoring paragraph, the wrap mode, and the relative position settings, along with z-order, overlap rules, and behavior inside table cells or across page breaks. Because the anchor's position and the paragraph height depend on each other, the layout becomes a circular fixed-point problem that Word resolves by iterating, so the result is not always predictable.\n\nNow I'm checking the line spacing rule for the image paragraph: an exact line height would clip inline images, so I'll use auto spacing there. I'm also giving the image paragraph keepNext so it stays with its caption, and I'm weighing whether a figure caption belongs above or below.\n\nNow I'm settling the caption and keep-with-next setup: the image paragraph stays with its caption, the caption uses a SEQ field for numbering, and I'm checking that the image height leaves room for the caption on the page.\n\nNow I'm looking at how the PNG alpha channel and the PDF soft mask relate, since Word can take an RGBA PNG but a JPEG can't carry transparency.\n\nNow I'm looking at how the PDF's soft mask turns into an alpha channel: the grayscale SMask image sets per-pixel opacity, so it has to be resampled to the base image's dimensions. Since Word can't take alpha from a JPEG, I'd decode it to RGB, merge the alpha in, and save the result as a PNG. The other cases to handle are stencil masks, color-key masks, decode-array inversion, and CMYK conversion to sRGB.\n\nNow I'm handling the WASM side, where memory is just a linear buffer that has to hold the decoded pixels while the alpha is merged in.\n\n","format":"anthropic-claude-v1","index":0,"signature":"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.eyJlbmRwb2ludF9zbHVnIjoiYW50aHJvcGljL2NsYXVkZS1zb25uZXQtNS41LTIwMjYwOTI4fGNsYXVkZS1vbi1hd3MifQ"}]}}],"usage":{"prompt_tokens":889,"completion_tokens":3500,"total_tokens":4389,"cost":0.036778,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.036778,"upstream_inference_prompt_cost":0.001778,"upstream_inference_completions_cost":0.035},"completion_tokens_details":{"reasoning_tokens":3500,"image_tokens":0,"audio_tokens":0}}}