
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()
lowercase__		:Optional[int]   =    logging.get_logger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					# initialize config
					if "resnet-50" in model_name:
										lowercase							=						ResNetConfig.from_pretrained('''microsoft/resnet-50'''		)
					elif "resnet-101" in model_name:
										lowercase							=						ResNetConfig.from_pretrained('''microsoft/resnet-101'''		)
					else:
										raise ValueError('''Model name should include either resnet50 or resnet101'''		)

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

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

					return config, is_panoptic



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					# here we list all keys to be renamed (original name on the left, our name on the right)
					lowercase							=						[]

					# 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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						state_dict.pop(lowerCAmelCase__		)
					lowercase							=						val



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						''''''
					if is_panoptic:
										lowercase							=						'''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)
										lowercase							=						state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
										lowercase							=						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
										lowercase							=						in_proj_weight[:256, :]
										lowercase							=						in_proj_bias[:256]
										lowercase							=						in_proj_weight[256:512, :]
										lowercase							=						in_proj_bias[256:512]
										lowercase							=						in_proj_weight[-256:, :]
										lowercase							=						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
										lowercase							=						state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
										lowercase							=						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
										lowercase							=						in_proj_weight[:256, :]
										lowercase							=						in_proj_bias[:256]
										lowercase							=						in_proj_weight[256:512, :]
										lowercase							=						in_proj_bias[256:512]
										lowercase							=						in_proj_weight[-256:, :]
										lowercase							=						in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										lowercase							=						state_dict.pop(
										    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
										lowercase							=						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
										lowercase							=						in_proj_weight_cross_attn[:256, :]
										lowercase							=						in_proj_bias_cross_attn[:256]
										lowercase							=						in_proj_weight_cross_attn[256:512, :]
										lowercase							=						in_proj_bias_cross_attn[256:512]
										lowercase							=						in_proj_weight_cross_attn[-256:, :]
										lowercase							=						in_proj_bias_cross_attn[-256:]



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase							=						Image.open(requests.get(lowerCAmelCase__ ,			stream=lowerCAmelCase__		).raw		)

					return im



@torch.no_grad()
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						get_detr_config(lowerCAmelCase__		)

					# load original model from torch hub
					lowercase							=						{
					    '''detr-resnet-50''': '''detr_resnet50''',
					    '''detr-resnet-101''': '''detr_resnet101''',
					}
					logger.info(f'Converting model {model_name}...'		)
					lowercase							=						torch.hub.load('''facebookresearch/detr''' ,			model_name_to_original_name[model_name] ,			pretrained=lowerCAmelCase__		).eval()
					lowercase							=						detr.state_dict()
					# rename keys
					for src, dest in create_rename_keys(lowerCAmelCase__		):
										if is_panoptic:
															lowercase							=						'''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
					lowercase							=						'''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'''		)
															):
																				lowercase							=						state_dict.pop(lowerCAmelCase__		)
																				lowercase							=						val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				lowercase							=						state_dict.pop(lowerCAmelCase__		)
																				lowercase							=						val
															elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
																				continue
															else:
																				lowercase							=						state_dict.pop(lowerCAmelCase__		)
																				lowercase							=						val
										else:
															if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
																				lowercase							=						state_dict.pop(lowerCAmelCase__		)
																				lowercase							=						val

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

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

					lowercase							=						processor(images=prepare_img() ,			return_tensors='''pt'''		)
					lowercase							=						encoding['''pixel_values''']

					lowercase							=						detr(lowerCAmelCase__		)
					lowercase							=						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__":
							lowercase__		:str   =    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.")
							lowercase__		:Tuple   =    parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
      from cookiecutter.main import cookiecutter

      lowercase__       =True
except ImportError:
      lowercase__       =False

lowercase__       =logging.get_logger(__name__)  # pylint: disable=invalid-name




def    __UpperCamelCase     (				lowerCAmelCase__							:				Namespace  ):
 return AddNewModelCommand(args.testing  ,   args.testing_file  ,   path=args.path  )



class 		UpperCamelCase__  (       __lowercase    ):
   @staticmethod
   def       lowerCAmelCase			(snake_case_ :					ArgumentParser   ):
    __a :  List[Any]						=  parser.add_parser('''add-new-model'''   )
    add_new_model_parser.add_argument('''--testing'''					,				action='''store_true'''					,				help='''If in testing mode.'''   )
    add_new_model_parser.add_argument('''--testing_file'''					,				type=snake_case_					,				help='''Configuration file on which to run.'''   )
    add_new_model_parser.add_argument(
        '''--path'''					,				type=snake_case_					,				help='''Path to cookiecutter. Should only be used for testing purposes.'''   )
    add_new_model_parser.set_defaults(func=snake_case_   )
   def __init__(self :					Dict					,				snake_case_ :					bool					,				snake_case_ :					str					,				snake_case_ :					Dict=None					,				*snake_case_ :					Optional[Any]   ):
    __a :  Union[str, Any]						=  testing
    __a :  List[Any]						=  testing_file
    __a :  Any						=  path







   def       lowerCAmelCase			(self :					int   ):
    warnings.warn(
        '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
        '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
        '''checks, you should use `transformers-cli add-new-model-like` instead.'''   )
    if not _has_cookiecutter:
     raise ImportError(
         '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
         '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''   )
    # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
    __a :  Union[str, Any]						=  [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:2_2]]
    if len(snake_case_   ) > 0:
     raise ValueError(
         '''Several directories starting with `cookiecutter-template-` in current working directory. '''
         '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
         '''change your working directory.'''   )

    __a :  Union[str, Any]						=  (
        Path(snake_case_   ).parent.parent.parent.parent if self._path is None else Path(self._path   ).parent.parent
    )
    __a :  Union[str, Any]						=  path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

    # Execute cookiecutter
    if not self._testing:
     cookiecutter(str(snake_case_   )   )
    else:
     with open(self._testing_file					,				'''r'''   ) as configuration_file:
      __a :  List[Any]						=  json.load(snake_case_   )

     cookiecutter(
         str(path_to_cookiecutter if self._path is None else self._path   )					,				no_input=snake_case_					,				extra_context=snake_case_					,				)

    __a :  List[str]						=  [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:2_2]][0]

    # Retrieve configuration
    with open(directory + '''/configuration.json'''					,				'''r'''   ) as configuration_file:
     __a :  Optional[Any]						=  json.load(snake_case_   )

    __a :  str						=  configuration['''lowercase_modelname''']
    __a :  int						=  configuration['''generate_tensorflow_pytorch_and_flax''']
    os.remove(f"{directory}/configuration.json"   )

    __a :  Any						=  '''PyTorch''' in generate_tensorflow_pytorch_and_flax
    __a :  Dict						=  '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
    __a :  Optional[int]						=  '''Flax''' in generate_tensorflow_pytorch_and_flax

    __a :  Dict						=  f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
    os.makedirs(snake_case_					,				exist_ok=snake_case_   )
    os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}"					,				exist_ok=snake_case_   )

    # Tests require submodules as they have parent imports
    with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"					,				'''w'''   ):
     pass

    shutil.move(
        f"{directory}/__init__.py"					,				f"{model_dir}/__init__.py"					,				)
    shutil.move(
        f"{directory}/configuration_{lowercase_model_name}.py"					,				f"{model_dir}/configuration_{lowercase_model_name}.py"					,				)

    def remove_copy_lines(snake_case_ :					Union[str, Any]   ):
     with open(snake_case_					,				'''r'''   ) as f:
      __a :  Union[str, Any]						=  f.readlines()
     with open(snake_case_					,				'''w'''   ) as f:
      for line in lines:
       if "# Copied from transformers." not in line:
        f.write(snake_case_   )

    if output_pytorch:
     if not self._testing:
      remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py"   )

     shutil.move(
         f"{directory}/modeling_{lowercase_model_name}.py"					,				f"{model_dir}/modeling_{lowercase_model_name}.py"					,				)

     shutil.move(
         f"{directory}/test_modeling_{lowercase_model_name}.py"					,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"					,				)
    else:
     os.remove(f"{directory}/modeling_{lowercase_model_name}.py"   )
     os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py"   )

    if output_tensorflow:
     if not self._testing:
      remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py"   )

     shutil.move(
         f"{directory}/modeling_tf_{lowercase_model_name}.py"					,				f"{model_dir}/modeling_tf_{lowercase_model_name}.py"					,				)

     shutil.move(
         f"{directory}/test_modeling_tf_{lowercase_model_name}.py"					,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"					,				)
    else:
     os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py"   )
     os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py"   )

    if output_flax:
     if not self._testing:
      remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py"   )

     shutil.move(
         f"{directory}/modeling_flax_{lowercase_model_name}.py"					,				f"{model_dir}/modeling_flax_{lowercase_model_name}.py"					,				)

     shutil.move(
         f"{directory}/test_modeling_flax_{lowercase_model_name}.py"					,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"					,				)
    else:
     os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py"   )
     os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py"   )

    shutil.move(
        f"{directory}/{lowercase_model_name}.md"					,				f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"					,				)

    shutil.move(
        f"{directory}/tokenization_{lowercase_model_name}.py"					,				f"{model_dir}/tokenization_{lowercase_model_name}.py"					,				)

    shutil.move(
        f"{directory}/tokenization_fast_{lowercase_model_name}.py"					,				f"{model_dir}/tokenization_{lowercase_model_name}_fast.py"					,				)

    from os import fdopen, remove
    from shutil import copymode, move
    from tempfile import mkstemp

    def replace(snake_case_ :					str					,				snake_case_ :					str					,				snake_case_ :					List[str]   ):
     # Create temp file
     __a				,       __a :  Tuple						=  mkstemp()
     __a :  Optional[Any]						=  False
     with fdopen(snake_case_					,				'''w'''   ) as new_file:
      with open(snake_case_   ) as old_file:
       for line in old_file:
        new_file.write(snake_case_   )
        if line_to_copy_below in line:
         __a :  Tuple						=  True
         for line_to_copy in lines_to_copy:
          new_file.write(snake_case_   )

     if not line_found:
      raise ValueError(f"Line {line_to_copy_below} was not found in file."   )

     # Copy the file permissions from the old file to the new file
     copymode(snake_case_					,				snake_case_   )
     # Remove original file
     remove(snake_case_   )
     # Move new file
     move(snake_case_					,				snake_case_   )

    def skip_units(snake_case_ :					Any   ):
     return (
         ("generating PyTorch" in line and not output_pytorch)
         or ("generating TensorFlow" in line and not output_tensorflow)
         or ("generating Flax" in line and not output_flax)
     )

    def replace_in_files(snake_case_ :					int   ):
     with open(snake_case_   ) as datafile:
      __a :  List[Any]						=  []
      __a :  int						=  False
      __a :  Tuple						=  False
      for line in datafile:
       if "# To replace in: " in line and "##" not in line:
        __a :  Optional[Any]						=  line.split('''"'''   )[1]
        __a :  Dict						=  skip_units(snake_case_   )
       elif "# Below: " in line and "##" not in line:
        __a :  str						=  line.split('''"'''   )[1]
        __a :  Any						=  skip_units(snake_case_   )
       elif "# End." in line and "##" not in line:
        if not skip_file and not skip_snippet:
         replace(snake_case_					,				snake_case_					,				snake_case_   )

        __a :  str						=  []
       elif "# Replace with" in line and "##" not in line:
        __a :  Optional[int]						=  []
       elif "##" not in line:
        lines_to_copy.append(snake_case_   )

     remove(snake_case_   )

    replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py"   )
    os.rmdir(snake_case_   )
