


'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :		Dict		     =	logging.get_logger()



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					nn.Module
	_lowerCamelCase   :					List[nn.Module]			      = field(default_factory=a							)
	_lowerCamelCase   :					list			      = field(default_factory=a							)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= len(list(m.modules()    )    ) == 1 or isinstance(snake_case__ ,					nn.Convad    ) or isinstance(snake_case__ ,					nn.BatchNormad    )
		if has_not_submodules:
			self.traced.append(snake_case__    )



	def __call__(					self ,					snake_case__    ):
		for m in self.module.modules():
			self.handles.append(m.register_forward_hook(self._forward_hook    )    )
		self.module(snake_case__    )
		[x.remove() for x in self.handles]
		return self



	@property
	def 				lowercase     (					self    ):
		# check the len of the state_dict keys to see if we have learnable params
		return list(filter(lambda snake_case__    : len(list(x.state_dict().keys()    )    ) > 0 ,					self.traced    )    )



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					nn.Module
	_lowerCamelCase   :					nn.Module
	_lowerCamelCase   :					int			      = 0
	_lowerCamelCase   :					List			      = field(default_factory=a							)
	_lowerCamelCase   :					List			      = field(default_factory=a							)



	def __call__(					self ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= Tracker(self.dest    )(snake_case__    ).parametrized
		lowerCAmelCase					:    Optional[Any]      							= Tracker(self.src    )(snake_case__    ).parametrized

		lowerCAmelCase					:    Optional[Any]      							= list(filter(lambda snake_case__    : type(snake_case__    ) not in self.src_skip ,					snake_case__    )    )
		lowerCAmelCase					:    List[Any]      							= list(filter(lambda snake_case__    : type(snake_case__    ) not in self.dest_skip ,					snake_case__    )    )

		if len(snake_case__    ) != len(snake_case__    ):
			raise Exception(
			    f"Numbers of operations are different. Source module has {len(snake_case__    )} operations while"
			    f" destination module has {len(snake_case__    )}."    )

		for dest_m, src_m in zip(snake_case__ ,					snake_case__    ):
			dest_m.load_state_dict(src_m.state_dict()    )
			if self.verbose == 1:
				print(f"Transfered from={src_m} to={dest_m}"    )


def 			__UpperCamelCase    (    _A    :				str					,   _A    :				ResNetConfig					,   _A    :				Path					,   _A    :				bool = True				)    ->      Optional[Any]:




	"""simple docstring"""





	print(F"Converting {name}..."				)
	with torch.no_grad():
		lowerCAmelCase					:    Any      							= timm.create_model(_A					,   pretrained=_A				).eval()
		lowerCAmelCase					:    Optional[int]      							= ResNetForImageClassification(_A				).eval()
		lowerCAmelCase					:    Optional[Any]      							= ModuleTransfer(src=_A					,   dest=_A				)
		lowerCAmelCase					:    List[Any]      							= torch.randn((1, 3, 2_24, 2_24)				)
		module_transfer(_A				)

	assert torch.allclose(from_model(_A				)					,   our_model(_A				).logits				), "The model logits don't match the original one."

	lowerCAmelCase					:    Union[str, Any]      							= F"resnet{'-'.join(name.split('resnet'				)				)}"
	print(_A				)

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name					,   commit_message='Add model'					,   use_temp_dir=_A					,   )

		# we can use the convnext one
		lowerCAmelCase					:    Dict      							= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name					,   commit_message='Add image processor'					,   use_temp_dir=_A					,   )

		print(F"Pushed {checkpoint_name}"				)

def 			__UpperCamelCase    (    _A    :				Path					,   _A    :				str = None					,   _A    :				bool = True				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    Dict      							= 10_00
	lowerCAmelCase					:    List[Any]      							= (1, num_labels)

	lowerCAmelCase					:    Any      							= 'huggingface/label-files'
	lowerCAmelCase					:    Tuple      							= num_labels
	lowerCAmelCase					:    List[Any]      							= json.load(open(hf_hub_download(_A					,   _A					,   repo_type='dataset'				)					,   'r'				)				)
	lowerCAmelCase					:    Any      							= {int(_A				): v for k, v in idalabel.items()}

	lowerCAmelCase					:    Tuple      							= idalabel
	lowerCAmelCase					:    Dict      							= {v: k for k, v in idalabel.items()}

	lowerCAmelCase					:    Dict      							= partial(_A					,   num_labels=_A					,   idalabel=_A					,   labelaid=_A				)

	lowerCAmelCase					:    str      							= {
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]					,   hidden_sizes=[64, 1_28, 2_56, 5_12]					,   layer_type='basic'				),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]					,   hidden_sizes=[64, 1_28, 2_56, 5_12]					,   layer_type='basic'				),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]					,   hidden_sizes=[2_56, 5_12, 10_24, 20_48]					,   layer_type='bottleneck'				),
	}

	if model_name:
		convert_weight_and_push(_A					,   names_to_config[model_name]					,   _A					,   _A				)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(_A					,   _A					,   _A					,   _A				)
	return config, expected_shape


if __name__ == "__main__":
				_lowerCAmelCase  :		Any		     =	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default=None,
				    type=str,
				    help=(
				        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
				        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=Path,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    default=True,
				    type=bool,
				    required=False,
				    help='If True, push model and image processor to the hub.',
				)

				_lowerCAmelCase  :		List[Any]		     =	parser.parse_args()
				_lowerCAmelCase  :		Path		     =	args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCAmelCase  :		List[Any]		     =	logging.getLogger(__name__)

def 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= argparse.ArgumentParser(
	    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'				)
	parser.add_argument(
	    '--dataset_name'					,   type=_A					,   default='wikitext'					,   help='Name of the training. Explore datasets at: hf.co/datasets.'					,   )
	parser.add_argument(
	    '--dataset_config'					,   type=_A					,   default='wikitext-103-raw-v1'					,   help='Configuration name of the dataset.'				)
	parser.add_argument(
	    '--tokenizer_name_or_path'					,   type=_A					,   default='sayakpaul/unigram-tokenizer-wikitext'					,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'					,   )
	parser.add_argument(
	    '--shard_size'					,   type=_A					,   default=10_00					,   help='Number of entries to go in a single shard.'					,   )
	parser.add_argument('--split'					,   type=_A					,   default='train'					,   choices=['train', 'test', 'validation']				)
	parser.add_argument(
	    '--limit'					,   default=_A					,   type=_A					,   help='Limit the number of shards (used for debugging).'					,   )
	parser.add_argument(
	    '--max_length'					,   type=_A					,   default=5_12					,   help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
	    ' sequence length that is a multiple of 8.'					,   )
	parser.add_argument(
	    '--output_dir'					,   default='tf-tpu'					,   type=_A					,   help='Output directory where the TFRecord shards will be saved. If the'
	    ' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
	    ' shards will be directly saved to a Google Cloud Storage bucket.'					,   )

	lowerCAmelCase					:    Any      							= parser.parse_args()
	return args

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      int:




	"""simple docstring"""





	def fn(_A    :				Tuple				):
		return tokenizer(examples['text']				)

	return fn

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for i in range(len(tokenized_data['input_ids']				)				):
		lowerCAmelCase					:    Optional[Any]      							= {
		    'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i]				)				),
		    'attention_mask': tf.train.Feature(
		        intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i]				)				),
		}
		lowerCAmelCase					:    Any      							= tf.train.Features(feature=_A				)
		lowerCAmelCase					:    List[str]      							= tf.train.Example(features=_A				)
		lowerCAmelCase					:    Tuple      							= example.SerializeToString()
		records.append(_A				)
	return records

def 			__UpperCamelCase    (    _A    :				int				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= datasets.load_dataset(args.dataset_name					,   args.dataset_config					,   split=args.split				)

	if args.limit is not None:
		lowerCAmelCase					:    Optional[Any]      							= min(len(_A				)					,   args.limit				)
		lowerCAmelCase					:    Dict      							= dataset.select(range(_A				)				)
		print(F"Limiting the dataset to {args.limit} entries."				)

	lowerCAmelCase					:    str      							= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

	# Handle output directory creation.
	# For serializing into a Google Cloud Storage Bucket, one needs to first
	# create a bucket.
	if "gs" not in args.output_dir:
		if not os.path.exists(args.output_dir				):
			os.makedirs(args.output_dir				)
		lowerCAmelCase					:    Any      							= os.path.join(args.output_dir					,   args.split				)
		if not os.path.exists(_A				):
			os.makedirs(_A				)
	else:
		lowerCAmelCase					:    List[Any]      							= os.path.join(args.output_dir					,   args.split				)

	# Tokenize the whole dataset at once.
	lowerCAmelCase					:    Any      							= tokenize_function(_A				)
	lowerCAmelCase					:    Optional[int]      							= dataset.map(_A					,   batched=_A					,   num_proc=4					,   remove_columns=['text']				)

	# We need to concatenate all our texts together, and then split the result
	# into chunks of a fixed size, which we will call block_size. To do this, we
	# will use the map method again, with the option batched=True. When we use batched=True,
	# the function we pass to map() will be passed multiple inputs at once, allowing us
	# to group them into more or fewer examples than we had in the input.
	# This allows us to create our new fixed-length samples. The advantage of this
	# method is that we don't lose a whole lot of content from the dataset compared to the
	# case where we simply tokenize with a pre-defined max_length.

	def group_texts(_A    :				str				):
		# Concatenate all texts.
		lowerCAmelCase					:    Optional[int]      							= {k: sum(examples[k]					,   []				) for k in examples.keys()}
		lowerCAmelCase					:    str      							= len(concatenated_examples[list(examples.keys()				)[0]]				)
		# We drop the small remainder, though you could add padding instead if the model supports it
		# In this, as in all things, we advise you to follow your heart 🫀
		lowerCAmelCase					:    List[Any]      							= (total_length // args.max_length) * args.max_length
		# Split by chunks of max_len.
		lowerCAmelCase					:    str      							= {
		    k: [t[i : i + args.max_length] for i in range(0					,   _A					,   args.max_length				)]
		    for k, t in concatenated_examples.items()
		}
		return result

	lowerCAmelCase					:    List[Any]      							= dataset_tokenized.map(_A					,   batched=_A					,   batch_size=10_00					,   num_proc=4				)

	lowerCAmelCase					:    Union[str, Any]      							= 0
	lowerCAmelCase					:    Tuple      							= 0
	for shard in range(0					,   len(_A				)					,   args.shard_size				):
		lowerCAmelCase					:    Optional[Any]      							= grouped_dataset[shard : shard + args.shard_size]
		lowerCAmelCase					:    List[str]      							= len(dataset_snapshot['input_ids']				)
		lowerCAmelCase					:    Union[str, Any]      							= os.path.join(_A					,   F"dataset-{shard_count}-{records_containing}.tfrecord"				)
		lowerCAmelCase					:    List[Any]      							= get_serialized_examples(_A				)

		with tf.io.TFRecordWriter(_A				) as out_file:
			for i in range(len(_A				)				):
				lowerCAmelCase					:    Union[str, Any]      							= serialized_examples[i]
				out_file.write(_A				)
			print('Wrote file {} containing {} records'.format(_A					,   _A				)				)

		shard_count += 1
		total_records += records_containing

	with open(F"split-{args.split}-records-count.txt"					,   'w'				) as f:
		print(F"Total {args.split} records: {total_records}"					,   file=_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	parse_args()
				main(args)
