





'''simple docstring'''



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							[]
			embed.append(
			    (
			        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
			        f"stage{idx}.patch_embed.proj.weight",
			    )				)
			embed.append(
			    (
			        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
			        f"stage{idx}.patch_embed.proj.bias",
			    )				)
			embed.append(
			    (
			        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
			        f"stage{idx}.patch_embed.norm.weight",
			    )				)
			embed.append(
			    (
			        f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
			        f"stage{idx}.patch_embed.norm.bias",
			    )				)
			return embed




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str				):
			__lowercase										=							[]
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
			        f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
			        f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
			        f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
			        f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
			        f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
			        f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
			        f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
			        f"stage{idx}.blocks.{cnt}.attn.proj.weight",
			    )				)
			attention_weights.append(
			    (
			        f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
			        f"stage{idx}.blocks.{cnt}.attn.proj.bias",
			    )				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")				)
			attention_weights.append(
			    (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")				)
			return attention_weights




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict				):
			__lowercase										=							[]
			token.append((f"cvt.encoder.stages.{idx}.cls_token", '''stage2.cls_token''')				)
			return token




def    _lowerCAmelCase						(				):
			__lowercase										=							[]
			head.append(('''layernorm.weight''', '''norm.weight''')				)
			head.append(('''layernorm.bias''', '''norm.bias''')				)
			head.append(('''classifier.weight''', '''head.weight''')				)
			head.append(('''classifier.bias''', '''head.bias''')				)
			return head




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							'''imagenet-1k-id2label.json'''
			__lowercase										=							1_0_0_0

			__lowercase										=							'''huggingface/label-files'''
			__lowercase										=							num_labels
			__lowercase										=							json.load(open(cached_download(hf_hub_url(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()}

			__lowercase										=							__lowercase										=							CvtConfig(num_labels=lowerCamelCase_		,       idalabel=lowerCamelCase_		,       labelaid=lowerCamelCase_				)

			# For depth size 13 (13 = 1+2+10)
			if cvt_model.rsplit('''/'''		,       1				)[-1][4:6] == "13":
						__lowercase										=							[1, 2, 1_0]

			# For depth size 21 (21 = 1+4+16)
			elif cvt_model.rsplit('''/'''		,       1				)[-1][4:6] == "21":
						__lowercase										=							[1, 4, 1_6]

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						__lowercase										=							[2, 2, 2_0]
						__lowercase										=							[3, 1_2, 1_6]
						__lowercase										=							[1_9_2, 7_6_8, 1_0_2_4]

			__lowercase										=							CvtForImageClassification(lowerCamelCase_				)
			__lowercase										=							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				)
			__lowercase										=							image_size
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location=torch.device('''cpu'''				)				)

			__lowercase										=							OrderedDict()
			__lowercase										=							[]

			for idx in range(len(config.depth				)				):
						if config.cls_token[idx]:
									__lowercase										=							list_of_state_dict + cls_token(lowerCamelCase_				)
						__lowercase										=							list_of_state_dict + embeddings(lowerCamelCase_				)
						for cnt in range(config.depth[idx]				):
									__lowercase										=							list_of_state_dict + attention(lowerCamelCase_		,       lowerCamelCase_				)

			__lowercase										=							list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(lowerCamelCase_				)
			for i in range(len(lowerCamelCase_				)				):
						__lowercase										=							original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)
			image_processor.save_pretrained(lowerCamelCase_				)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	parser.add_argument(
	    '''--cvt_model''',
	    default='''cvt-w24''',
	    type=str,
	    help='''Name of the cvt model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--image_size''',
	    default=3_8_4,
	    type=int,
	    help='''Input Image Size''',
	)
	parser.add_argument(
	    '''--cvt_file_name''',
	    default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
	    type=str,
	    help='''Input Image Size''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)

	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)












'''simple docstring'''



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

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


_SCREAMING_SNAKE_CASE					=    '''<<<<<<< This should probably be modified because it mentions: '''

_SCREAMING_SNAKE_CASE					=    '''=======
>>>>>>>
'''

_SCREAMING_SNAKE_CASE					=    [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

_SCREAMING_SNAKE_CASE					=    [
    # (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						(				lowerCamelCase_			:  Namespace				):
			return ConvertCommand(args.tfds_path		,       args.datasets_directory				)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	@staticmethod
	def 						_UpperCAmelCase     (_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							parser.add_parser(
				    '''convert'''     ,help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''     ,)
				train_parser.add_argument(
				    '''--tfds_path'''     ,type=_lowerCamelCase     ,required=_lowerCamelCase     ,help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''     ,)
				train_parser.add_argument(
				    '''--datasets_directory'''     ,type=_lowerCamelCase     ,required=_lowerCamelCase     ,help='''Path to the HuggingFace Datasets folder.''' )
				train_parser.set_defaults(func=_lowerCamelCase )


	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,*_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							get_logger('''datasets-cli/converting''' )

				__lowercase										=							tfds_path
				__lowercase										=							datasets_directory


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				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(_lowerCamelCase )
				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(_lowerCamelCase     ,_lowerCamelCase )
							__lowercase										=							os.path.join(_lowerCamelCase     ,_lowerCamelCase )

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

							with open(_lowerCamelCase     ,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 _lowerCamelCase : e in out_line     ,_lowerCamelCase ) )
													out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_lowerCamelCase ) + '''\n''' )
													out_lines.append(_lowerCamelCase )
													out_lines.append(_lowerCamelCase )
													continue
										else:
													for pattern, replacement in TO_CONVERT:
																__lowercase										=							re.sub(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )

                # 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]+)'''     ,_lowerCamelCase )
													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(_lowerCamelCase )

							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(_lowerCamelCase     ,_lowerCamelCase )
										__lowercase										=							os.path.join(_lowerCamelCase     ,_lowerCamelCase )
										os.makedirs(_lowerCamelCase     ,exist_ok=_lowerCamelCase )
										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(_lowerCamelCase )

							if needs_manual_update:
										with_manual_update.append(_lowerCamelCase )

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

				for utils_file in utils_files:
							try:
										__lowercase										=							os.path.basename(_lowerCamelCase )
										__lowercase										=							imports_to_builder_map[f_name.replace('''.py'''     ,'''''' )]
										self._logger.info(f"Moving {dest_folder} to {utils_file}" )
										shutil.copy(_lowerCamelCase     ,_lowerCamelCase )
							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'." )





