



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_								=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_								=     []
for i in range(6):
							# 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}.cross_attn.out_proj.weight',
							        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
							    )
							)
							rename_keys.append(
							    (
							        F'transformer.decoder.layers.{i}.cross_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'))

							# q, k, v projections in self/cross-attention in decoder for conditional DETR
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
							)
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
							)
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
							)
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
							)
							rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
							)
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
							)
							rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
							)

							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
							)
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
							)
							rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
							rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
							rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
							)
							rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
							rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
							rename_keys.append(
							    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
							)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				val



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									lowercase__					  =				key.replace("backbone.0.body"						,  "backbone.conv_encoder.model"  )
									lowercase__					  =				value
						else:
									lowercase__					  =				value

			return new_state_dict



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				""
			if is_panoptic:
						lowercase__					  =				"conditional_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:]



def __lowerCAmelCase (     ):
			lowercase__					  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowercase__					  =				Image.open(requests.get(SCREAMING_SNAKE_CASE_						,  stream=SCREAMING_SNAKE_CASE_  ).raw  )

			return im



@torch.no_grad()
def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						lowercase__					  =				"resnet101"
			if "dc5" in model_name:
						lowercase__					  =				True
			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(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  repo_type="dataset"  )						,  "r"  )  )
						lowercase__					  =				{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
						lowercase__					  =				idalabel
						lowercase__					  =				{v: k for k, v in idalabel.items()}

			# load image processor
			lowercase__					  =				"coco_panoptic" if is_panoptic else "coco_detection"
			lowercase__					  =				ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE_  )

			# prepare image
			lowercase__					  =				prepare_img()
			lowercase__					  =				image_processor(images=SCREAMING_SNAKE_CASE_						,  return_tensors="pt"  )
			lowercase__					  =				encoding["pixel_values"]

			logger.info(f'''Converting model {model_name}...'''  )

			# load original model from torch hub
			lowercase__					  =				torch.hub.load("DeppMeng/ConditionalDETR"						,  SCREAMING_SNAKE_CASE_						,  pretrained=SCREAMING_SNAKE_CASE_  ).eval()
			lowercase__					  =				conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									lowercase__					  =				"conditional_detr." + src
						rename_key(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				rename_backbone_keys(SCREAMING_SNAKE_CASE_  )
			# query, key and value matrices need special treatment
			read_in_q_k_v(SCREAMING_SNAKE_CASE_						,  is_panoptic=SCREAMING_SNAKE_CASE_  )
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			lowercase__					  =				"conditional_detr.model." if is_panoptic else "model."
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith("conditional_detr"  )
									    and not key.startswith("class_labels_classifier"  )
									    and not key.startswith("bbox_predictor"  )
									):
												lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
												lowercase__					  =				val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
												lowercase__					  =				val
									elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
												continue
									else:
												lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
												lowercase__					  =				val
						else:
									if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
												lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
												lowercase__					  =				val
    # finally, create HuggingFace model and load state dict
			lowercase__					  =				ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE_  ) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE_  )
			model.load_state_dict(SCREAMING_SNAKE_CASE_  )
			model.eval()
			model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE_						,  organization="DepuMeng"						,  commit_message="Add model"  )
			# verify our conversion
			lowercase__					  =				conditional_detr(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )
			assert torch.allclose(outputs.logits						,  original_outputs["pred_logits"]						,  atol=1e-4  )
			assert torch.allclose(outputs.pred_boxes						,  original_outputs["pred_boxes"]						,  atol=1e-4  )
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks						,  original_outputs["pred_masks"]						,  atol=1e-4  )

			# Save model and image processor
			logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''  )
			Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
			model.save_pretrained(SCREAMING_SNAKE_CASE_  )
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_  )


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

							parser.add_argument(
							    """--model_name""",
							    default="""conditional_detr_resnet50""",
							    type=str,
							    help="""Name of the CONDITIONAL_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."""
							)
							lowercase_								=     parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowercase_								=     """<<<<<<< This should probably be modified because it mentions: """

lowercase_								=     """=======
>>>>>>>
"""

lowercase_								=     [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase_								=     [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r"""tfds\.core""", r"""datasets"""),
    (r"""tf\.io\.gfile\.GFile""", r"""open"""),
    (r"""tf\.([\w\d]+)""", r"""datasets.Value('\1')"""),
    (r"""tfds\.features\.Text\(\)""", r"""datasets.Value('string')"""),
    (r"""tfds\.features\.Text\(""", r"""datasets.Value('string'),"""),
    (r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
    (r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
    (r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (r"""tfds\.""", r"""datasets."""),
    (r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
    (r"""self\.builder_config""", r"""self.config"""),
]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			return ConvertCommand(args.tfds_path						,  args.datasets_directory  )




class 				_snake_case			(					lowercase__):




				@staticmethod
				def 							A__      (						__lowercase					:						ArgumentParser						):
							lowercase__					  =				parser.add_parser(
							    "convert",					help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",					)
							train_parser.add_argument(
							    "--tfds_path",					type=__lowercase,					required=__lowercase,					help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",					)
							train_parser.add_argument(
							    "--datasets_directory",					type=__lowercase,					required=__lowercase,					help="Path to the HuggingFace Datasets folder."						)
							train_parser.set_defaults(func=__lowercase						)




				def __init__(						self					:						Tuple,					__lowercase					:						str,					__lowercase					:						str,					*__lowercase					:						Tuple						):
							lowercase__					  =				get_logger("datasets-cli/converting"						)

							lowercase__					  =				tfds_path
							lowercase__					  =				datasets_directory




				def 							A__      (						self					:						Any						):
							if os.path.isdir(self._tfds_path						):
										lowercase__					  =				os.path.abspath(self._tfds_path						)
							elif os.path.isfile(self._tfds_path						):
										lowercase__					  =				os.path.dirname(self._tfds_path						)
							else:
										raise ValueError("--tfds_path is neither a directory nor a file. Please check path."						)

							lowercase__					  =				os.path.abspath(self._datasets_directory						)

							self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''						)

							lowercase__					  =				[]
							lowercase__					  =				[]
							lowercase__					  =				{}

							if os.path.isdir(self._tfds_path						):
										lowercase__					  =				os.listdir(__lowercase						)
							else:
										lowercase__					  =				[os.path.basename(self._tfds_path						)]

							for f_name in file_names:
										self._logger.info(F'''Looking at file {f_name}'''						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)

										if not os.path.isfile(__lowercase						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
													self._logger.info("Skipping file"						)
													continue

										with open(__lowercase,					encoding="utf-8"						) as f:
													lowercase__					  =				f.readlines()

										lowercase__					  =				[]
										lowercase__					  =				False
										lowercase__					  =				False
										lowercase__					  =				[]
										for line in lines:
													lowercase__					  =				line

													# Convert imports
													if "import tensorflow.compat.v2 as tf" in out_line:
																continue
													elif "@tfds.core" in out_line:
																continue
													elif "builder=self" in out_line:
																continue
													elif "import tensorflow_datasets.public_api as tfds" in out_line:
																lowercase__					  =				"import datasets\n"
													elif "import tensorflow" in out_line:
																# order is important here
																lowercase__					  =				""
																continue
													elif "from absl import logging" in out_line:
																lowercase__					  =				"from datasets import logging\n"
													elif "getLogger" in out_line:
																lowercase__					  =				out_line.replace("getLogger",					"get_logger"						)
													elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																lowercase__					  =				True
																lowercase__					  =				list(filter(lambda __lowercase						: e in out_line,					__lowercase						)						)
																out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowercase						) + "\n"						)
																out_lines.append(__lowercase						)
																out_lines.append(__lowercase						)
																continue
													else:
																for pattern, replacement in TO_CONVERT:
																			lowercase__					  =				re.sub(__lowercase,					__lowercase,					__lowercase						)

                # Take care of saving utilities (to later move them together with main script)
													if "tensorflow_datasets" in out_line:
																lowercase__					  =				re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)",					__lowercase						)
																tfds_imports.extend(imp.strip() for imp in match.group(1						).split(","						)						)
																lowercase__					  =				"from . import " + match.group(1						)

													# Check we have not forget anything
													if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																raise ValueError(F'''Error converting {out_line.strip()}'''						)

													if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																lowercase__					  =				True
													out_lines.append(__lowercase						)

										if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__					  =				f_name.replace(".py",					""						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													os.makedirs(__lowercase,					exist_ok=__lowercase						)
													self._logger.info(F'''Adding directory {output_dir}'''						)
													imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}						)
										else:
													# Utilities will be moved at the end
													utils_files.append(__lowercase						)

										if needs_manual_update:
													with_manual_update.append(__lowercase						)

										with open(__lowercase,					"w",					encoding="utf-8"						) as f:
													f.writelines(__lowercase						)
										self._logger.info(F'''Converted in {output_file}'''						)

							for utils_file in utils_files:
										try:
													lowercase__					  =				os.path.basename(__lowercase						)
													lowercase__					  =				imports_to_builder_map[f_name.replace(".py",					""						)]
													self._logger.info(F'''Moving {dest_folder} to {utils_file}'''						)
													shutil.copy(__lowercase,					__lowercase						)
										except KeyError:
													self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''						)

							if with_manual_update:
										for file_path in with_manual_update:
													self._logger.warning(
													    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''						)




