

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()
UpperCamelCase			    =     logging.get_logger()



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				nn.Module
			__snake_case    :				List[nn.Module]									=							field(default_factory=_UpperCAmelCase   )
			__snake_case    :				list									=							field(default_factory=_UpperCAmelCase   )







			def 							UpperCamelCase				(     self:  Tuple				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Tensor				,     UpperCAmelCase_:  Tensor					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  len(list(m.modules()					)					) == 1 or isinstance(UpperCAmelCase_				,     nn.Convad					) or isinstance(UpperCAmelCase_				,     nn.BatchNormad					)
									if has_not_submodules:
															self.traced.append(UpperCAmelCase_					)







			def __call__(     self:  Union[str, Any]				,     UpperCAmelCase_:  Tensor					):







									'''simple docstring'''




									for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook					)					)
									self.module(UpperCAmelCase_					)
									[x.remove() for x in self.handles]
									return self







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									return list(filter(lambda UpperCAmelCase_					: len(list(x.state_dict().keys()					)					) > 0				,     self.traced					)					)



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				nn.Module
			__snake_case    :				nn.Module
			__snake_case    :				int									=							0
			__snake_case    :				List									=							field(default_factory=_UpperCAmelCase   )
			__snake_case    :				List									=							field(default_factory=_UpperCAmelCase   )







			def __call__(     self:  Any				,     UpperCAmelCase_:  Tensor					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  Tracker(self.dest					)(UpperCAmelCase_					).parametrized
									_SCREAMING_SNAKE_CASE     				=  Tracker(self.src					)(UpperCAmelCase_					).parametrized

									_SCREAMING_SNAKE_CASE     				=  list(filter(lambda UpperCAmelCase_					: type(UpperCAmelCase_					) not in self.src_skip				,     UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  list(filter(lambda UpperCAmelCase_					: type(UpperCAmelCase_					) not in self.dest_skip				,     UpperCAmelCase_					)					)

									if len(UpperCAmelCase_					) != len(UpperCAmelCase_					):
															raise Exception(
															    F'Numbers of operations are different. Source module has {len(UpperCAmelCase_					)} operations while'
															    F' destination module has {len(UpperCAmelCase_					)}.'					)

									for dest_m, src_m in zip(UpperCAmelCase_				,     UpperCAmelCase_					):
															dest_m.load_state_dict(src_m.state_dict()					)
															if self.verbose == 1:
																					print(F'Transfered from={src_m} to={dest_m}'					)

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__ = True		)							->  Tuple:






						"""simple docstring"""





						print(F'Converting {name}...'		)
						with torch.no_grad():
												_SCREAMING_SNAKE_CASE     				=  timm.create_model(snake_case__	,pretrained=snake_case__		).eval()
												_SCREAMING_SNAKE_CASE     				=  ResNetForImageClassification(snake_case__		).eval()
												_SCREAMING_SNAKE_CASE     				=  ModuleTransfer(src=snake_case__	,dest=snake_case__		)
												_SCREAMING_SNAKE_CASE     				=  torch.randn((1, 3, 2_24, 2_24)		)
												module_transfer(snake_case__		)

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

						_SCREAMING_SNAKE_CASE     				=  F'resnet{"-".join(name.split("resnet"		)		)}'
						print(snake_case__		)

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

												# we can use the convnext one
												_SCREAMING_SNAKE_CASE     				=  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=snake_case__	,)

												print(F'Pushed {checkpoint_name}'		)

def 	__lowerCamelCase      (		snake_case__	,snake_case__ = None	,snake_case__ = True		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """imagenet-1k-id2label.json"""
						_SCREAMING_SNAKE_CASE     				=  10_00
						_SCREAMING_SNAKE_CASE     				=  (1, num_labels)

						_SCREAMING_SNAKE_CASE     				=  """huggingface/label-files"""
						_SCREAMING_SNAKE_CASE     				=  num_labels
						_SCREAMING_SNAKE_CASE     				=  json.load(open(hf_hub_download(snake_case__	,snake_case__	,repo_type="""dataset"""		)	,"""r"""		)		)
						_SCREAMING_SNAKE_CASE     				=  {int(snake_case__		): v for k, v in idalabel.items()}

						_SCREAMING_SNAKE_CASE     				=  idalabel
						_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in idalabel.items()}

						_SCREAMING_SNAKE_CASE     				=  partial(snake_case__	,num_labels=snake_case__	,idalabel=snake_case__	,labelaid=snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  {
						    """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(snake_case__	,names_to_config[model_name]	,snake_case__	,snake_case__		)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						return config, expected_shape


if __name__ == "__main__":
		UpperCamelCase			    =     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.''',
		)

		UpperCamelCase			    =     parser.parse_args()
		UpperCamelCase			    =     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)









import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					)					)
									with open(self.merges_file				,     """w"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""This is a simple input 1""", """This is a simple input 2"""),
																					    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																					]

																					# Simple input tests
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass

