



"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase       	=						logging.get_logger(__name__)
def        a__     (				lowerCAmelCase__				):
      # initialize config
      if "resnet-50" in model_name:
            UpperCAmelCase_      			=							ResNetConfig.from_pretrained("microsoft/resnet-50"				)
      elif "resnet-101" in model_name:
            UpperCAmelCase_      			=							ResNetConfig.from_pretrained("microsoft/resnet-101"				)
      else:
            raise ValueError("Model name should include either resnet50 or resnet101"				)

      UpperCAmelCase_      			=							DetrConfig(use_timm_backbone=lowerCAmelCase__     ,      backbone_config=lowerCAmelCase__				)

      # set label attributes
      UpperCAmelCase_      			=							"panoptic" in model_name
      if is_panoptic:
            UpperCAmelCase_      			=							250
      else:
            UpperCAmelCase_      			=							91
            UpperCAmelCase_      			=							"huggingface/label-files"
            UpperCAmelCase_      			=							"coco-detection-id2label.json"
            UpperCAmelCase_      			=							json.load(open(hf_hub_download(lowerCAmelCase__     ,      lowerCAmelCase__     ,      repo_type="dataset"				)     ,      "r"				)				)
            UpperCAmelCase_      			=							{int(lowerCAmelCase__				): v for k, v in idalabel.items()}
            UpperCAmelCase_      			=							idalabel
            UpperCAmelCase_      			=							{v: k for k, v in idalabel.items()}

      return config, is_panoptic
def        a__     (				lowerCAmelCase__				):
      # here we list all keys to be renamed (original name on the left, our name on the right)
      UpperCAmelCase_      			=							[]

      # stem
      # fmt: off
      rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")				)
      rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")				)
      rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")				)
      rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")				)
      rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")				)
      # stages
      for stage_idx in range(len(config.backbone_config.depths				)				):
            for layer_idx in range(config.backbone_config.depths[stage_idx]				):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                            )				)
                  # 3 convs
                  for i in range(3				):
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                            )				)
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                            )				)
    # fmt: on

      for i in range(config.encoder_layers				):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (
                    f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
                    f"""encoder.layers.{i}.self_attn.out_proj.weight""",
                )				)
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")				)
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""")				)
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""")				)
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""")				)
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""")				)
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")				)
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""")				)
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""")				)
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""")				)
            # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
            rename_keys.append(
                (
                    f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
                    f"""decoder.layers.{i}.self_attn.out_proj.weight""",
                )				)
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")				)
            rename_keys.append(
                (
                    f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
                    f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
                )				)
            rename_keys.append(
                (
                    f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
                    f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
                )				)
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""")				)
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""")				)
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""")				)
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""")				)
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")				)
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""")				)
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")				)
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")				)
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""")				)
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""")				)

      # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
      rename_keys.extend(
          [
              ("input_proj.weight", "input_projection.weight"),
              ("input_proj.bias", "input_projection.bias"),
              ("query_embed.weight", "query_position_embeddings.weight"),
              ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
              ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
              ("class_embed.weight", "class_labels_classifier.weight"),
              ("class_embed.bias", "class_labels_classifier.bias"),
              ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
              ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
              ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
              ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
              ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
              ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
          ]				)

      return rename_keys
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							state_dict.pop(lowerCAmelCase__				)
      UpperCAmelCase_      			=							val
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=False				):
      UpperCAmelCase_      			=							""
      if is_panoptic:
            UpperCAmelCase_      			=							"detr."

      # first: transformer encoder
      for i in range(6				):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            UpperCAmelCase_      			=							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
            UpperCAmelCase_      			=							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_      			=							in_proj_weight[:256, :]
            UpperCAmelCase_      			=							in_proj_bias[:256]
            UpperCAmelCase_      			=							in_proj_weight[256:512, :]
            UpperCAmelCase_      			=							in_proj_bias[256:512]
            UpperCAmelCase_      			=							in_proj_weight[-256:, :]
            UpperCAmelCase_      			=							in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6				):
            # read in weights + bias of input projection layer of self-attention
            UpperCAmelCase_      			=							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
            UpperCAmelCase_      			=							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_      			=							in_proj_weight[:256, :]
            UpperCAmelCase_      			=							in_proj_bias[:256]
            UpperCAmelCase_      			=							in_proj_weight[256:512, :]
            UpperCAmelCase_      			=							in_proj_bias[256:512]
            UpperCAmelCase_      			=							in_proj_weight[-256:, :]
            UpperCAmelCase_      			=							in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCAmelCase_      			=							state_dict.pop(
                f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
            UpperCAmelCase_      			=							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            UpperCAmelCase_      			=							in_proj_weight_cross_attn[:256, :]
            UpperCAmelCase_      			=							in_proj_bias_cross_attn[:256]
            UpperCAmelCase_      			=							in_proj_weight_cross_attn[256:512, :]
            UpperCAmelCase_      			=							in_proj_bias_cross_attn[256:512]
            UpperCAmelCase_      			=							in_proj_weight_cross_attn[-256:, :]
            UpperCAmelCase_      			=							in_proj_bias_cross_attn[-256:]
def        a__     (				):
      UpperCAmelCase_      			=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_      			=							Image.open(requests.get(lowerCAmelCase__     ,      stream=lowerCAmelCase__				).raw				)

      return im
@torch.no_grad()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=False				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							get_detr_config(lowerCAmelCase__				)

      # load original model from torch hub
      UpperCAmelCase_      			=							{
          "detr-resnet-50": "detr_resnet50",
          "detr-resnet-101": "detr_resnet101",
      }
      logger.info(f"""Converting model {model_name}..."""				)
      UpperCAmelCase_      			=							torch.hub.load("facebookresearch/detr"     ,      model_name_to_original_name[model_name]     ,      pretrained=lowerCAmelCase__				).eval()
      UpperCAmelCase_      			=							detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(lowerCAmelCase__				):
            if is_panoptic:
                  UpperCAmelCase_      			=							"detr." + src
            rename_key(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)
      # query, key and value matrices need special treatment
      read_in_q_k_v(lowerCAmelCase__     ,      is_panoptic=lowerCAmelCase__				)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCAmelCase_      			=							"detr.model." if is_panoptic else "model."
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith("detr"				)
                      and not key.startswith("class_labels_classifier"				)
                      and not key.startswith("bbox_predictor"				)
                  ):
                        UpperCAmelCase_      			=							state_dict.pop(lowerCAmelCase__				)
                        UpperCAmelCase_      			=							val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        UpperCAmelCase_      			=							state_dict.pop(lowerCAmelCase__				)
                        UpperCAmelCase_      			=							val
                  elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
                        continue
                  else:
                        UpperCAmelCase_      			=							state_dict.pop(lowerCAmelCase__				)
                        UpperCAmelCase_      			=							val
            else:
                  if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
                        UpperCAmelCase_      			=							state_dict.pop(lowerCAmelCase__				)
                        UpperCAmelCase_      			=							val

    # finally, create HuggingFace model and load state dict
      UpperCAmelCase_      			=							DetrForSegmentation(lowerCAmelCase__				) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__				)
      model.load_state_dict(lowerCAmelCase__				)
      model.eval()

      # verify our conversion on an image
      UpperCAmelCase_      			=							"coco_panoptic" if is_panoptic else "coco_detection"
      UpperCAmelCase_      			=							DetrImageProcessor(format=lowerCAmelCase__				)

      UpperCAmelCase_      			=							processor(images=prepare_img()     ,      return_tensors="pt"				)
      UpperCAmelCase_      			=							encoding["pixel_values"]

      UpperCAmelCase_      			=							detr(lowerCAmelCase__				)
      UpperCAmelCase_      			=							model(lowerCAmelCase__				)

      assert torch.allclose(outputs.logits     ,      original_outputs["pred_logits"]     ,      atol=1e-3				)
      assert torch.allclose(outputs.pred_boxes     ,      original_outputs["pred_boxes"]     ,      atol=1e-3				)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks     ,      original_outputs["pred_masks"]     ,      atol=1e-4				)
      print("Looks ok!"				)

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
            Path(lowerCAmelCase__				).mkdir(exist_ok=lowerCAmelCase__				)
            model.save_pretrained(lowerCAmelCase__				)
            processor.save_pretrained(lowerCAmelCase__				)

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info("Uploading PyTorch model and image processor to the hub..."				)
            model.push_to_hub(f"""nielsr/{model_name}"""				)
            processor.push_to_hub(f"""nielsr/{model_name}"""				)


if __name__ == "__main__":
  lowerCamelCase       	=						argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""detr-resnet-50""",
      type=str,
      choices=["""detr-resnet-50""", """detr-resnet-101"""],
      help="""Name of the DETR model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
  lowerCamelCase       	=						parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""





import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase       	=						"""src/diffusers"""
lowerCamelCase       	=						"""."""


# This is to make sure the diffusers module imported is the one in the repo.
lowerCamelCase       	=						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCamelCase       	=						spec.loader.load_module()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      return line.startswith(lowerCAmelCase__				) or len(lowerCAmelCase__				) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"     ,      lowerCAmelCase__				) is not None
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							object_name.split("."				)
      UpperCAmelCase_      			=							0

      # First let's find the module where our object lives.
      UpperCAmelCase_      			=							parts[i]
      while i < len(lowerCAmelCase__				) and not os.path.isfile(os.path.join(lowerCAmelCase__     ,      f"""{module}.py"""				)				):
            i += 1
            if i < len(lowerCAmelCase__				):
                  UpperCAmelCase_      			=							os.path.join(lowerCAmelCase__     ,      parts[i]				)
      if i >= len(lowerCAmelCase__				):
            raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""				)

      with open(os.path.join(lowerCAmelCase__     ,      f"""{module}.py"""				)     ,      "r"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
            UpperCAmelCase_      			=							f.readlines()

      # Now let's find the class / func in the code!
      UpperCAmelCase_      			=							""
      UpperCAmelCase_      			=							0
      for name in parts[i + 1 :]:
            while (
                line_index < len(lowerCAmelCase__				) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""     ,      lines[line_index]				) is None
            ):
                  line_index += 1
            indent += "    "
            line_index += 1

      if line_index >= len(lowerCAmelCase__				):
            raise ValueError(f""" {object_name} does not match any function or class in {module}."""				)

      # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
      UpperCAmelCase_      			=							line_index
      while line_index < len(lowerCAmelCase__				) and _should_continue(lines[line_index]     ,      lowerCAmelCase__				):
            line_index += 1
      # Clean up empty lines at the end (if any).
      while len(lines[line_index - 1]				) <= 1:
            line_index -= 1

      UpperCAmelCase_      			=							lines[start_index:line_index]
      return "".join(lowerCAmelCase__				)


lowerCamelCase       	=						re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowerCamelCase       	=						re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowerCamelCase       	=						re.compile(r"""<FILL\s+[^>]*>""")
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							code.split("\n"				)
      UpperCAmelCase_      			=							0
      while idx < len(lowerCAmelCase__				) and len(lines[idx]				) == 0:
            idx += 1
      if idx < len(lowerCAmelCase__				):
            return re.search(r"^(\s*)\S"     ,      lines[idx]				).groups()[0]
      return ""
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							len(get_indent(lowerCAmelCase__				)				) > 0
      if has_indent:
            UpperCAmelCase_      			=							f"""class Bla:\n{code}"""
      UpperCAmelCase_      			=							black.Mode(target_versions={black.TargetVersion.PYaa}     ,      line_length=119     ,      preview=lowerCAmelCase__				)
      UpperCAmelCase_      			=							black.format_str(lowerCAmelCase__     ,      mode=lowerCAmelCase__				)
      UpperCAmelCase_			,   UpperCAmelCase_      			=							style_docstrings_in_code(lowerCAmelCase__				)
      return result[len("class Bla:\n"				) :] if has_indent else result
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=False				):
      with open(lowerCAmelCase__     ,      "r"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
            UpperCAmelCase_      			=							f.readlines()
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							0
      # Not a for loop cause `lines` is going to change (if `overwrite=True`).
      while line_index < len(lowerCAmelCase__				):
            UpperCAmelCase_      			=							_re_copy_warning.search(lines[line_index]				)
            if search is None:
                  line_index += 1
                  continue

            # There is some copied code here, let's retrieve the original.
            UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							search.groups()
            UpperCAmelCase_      			=							find_code_in_diffusers(lowerCAmelCase__				)
            UpperCAmelCase_      			=							get_indent(lowerCAmelCase__				)

            UpperCAmelCase_      			=							line_index + 1 if indent == theoretical_indent else line_index + 2
            UpperCAmelCase_      			=							theoretical_indent
            UpperCAmelCase_      			=							start_index

            # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
            UpperCAmelCase_      			=							True
            while line_index < len(lowerCAmelCase__				) and should_continue:
                  line_index += 1
                  if line_index >= len(lowerCAmelCase__				):
                        break
                  UpperCAmelCase_      			=							lines[line_index]
                  UpperCAmelCase_      			=							_should_continue(lowerCAmelCase__     ,      lowerCAmelCase__				) and re.search(f"""^{indent}# End copy"""     ,      lowerCAmelCase__				) is None
            # Clean up empty lines at the end (if any).
            while len(lines[line_index - 1]				) <= 1:
                  line_index -= 1

            UpperCAmelCase_      			=							lines[start_index:line_index]
            UpperCAmelCase_      			=							"".join(lowerCAmelCase__				)

            # Remove any nested `Copied from` comments to avoid circular copies
            UpperCAmelCase_      			=							[line for line in theoretical_code.split("\n"				) if _re_copy_warning.search(lowerCAmelCase__				) is None]
            UpperCAmelCase_      			=							"\n".join(lowerCAmelCase__				)

            # Before comparing, use the `replace_pattern` on the original code.
            if len(lowerCAmelCase__				) > 0:
                  UpperCAmelCase_      			=							replace_pattern.replace("with"     ,      ""				).split(","				)
                  UpperCAmelCase_      			=							[_re_replace_pattern.search(lowerCAmelCase__				) for p in patterns]
                  for pattern in patterns:
                        if pattern is None:
                              continue
                        UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							pattern.groups()
                        UpperCAmelCase_      			=							re.sub(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)
                        if option.strip() == "all-casing":
                              UpperCAmelCase_      			=							re.sub(obja.lower()     ,      obja.lower()     ,      lowerCAmelCase__				)
                              UpperCAmelCase_      			=							re.sub(obja.upper()     ,      obja.upper()     ,      lowerCAmelCase__				)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
                  UpperCAmelCase_      			=							blackify(lines[start_index - 1] + theoretical_code				)
                  UpperCAmelCase_      			=							theoretical_code[len(lines[start_index - 1]				) :]

            # Test for a diff and act accordingly.
            if observed_code != theoretical_code:
                  diffs.append([object_name, start_index]				)
                  if overwrite:
                        UpperCAmelCase_      			=							lines[:start_index] + [theoretical_code] + lines[line_index:]
                        UpperCAmelCase_      			=							start_index + 1

      if overwrite and len(lowerCAmelCase__				) > 0:
            # Warn the user a file has been modified.
            print(f"""Detected changes, rewriting {filename}."""				)
            with open(lowerCAmelCase__     ,      "w"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
                  f.writelines(lowerCAmelCase__				)
      return diffs
def        a__     (				lowerCAmelCase__ = False				):
      UpperCAmelCase_      			=							glob.glob(os.path.join(lowerCAmelCase__     ,      "**/*.py"				)     ,      recursive=lowerCAmelCase__				)
      UpperCAmelCase_      			=							[]
      for filename in all_files:
            UpperCAmelCase_      			=							is_copy_consistent(lowerCAmelCase__     ,      lowerCAmelCase__				)
            diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
      if not overwrite and len(lowerCAmelCase__				) > 0:
            UpperCAmelCase_      			=							"\n".join(lowerCAmelCase__				)
            raise Exception(
                "Found the following copy inconsistencies:\n"
                + diff
                + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."				)


if __name__ == "__main__":
  lowerCamelCase       	=						argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  lowerCamelCase       	=						parser.parse_args()

  check_copies(args.fix_and_overwrite)




