
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_  :	int 				=							logging.get_logger()

@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Optional[Any]    =	42
			__UpperCamelCase :  str    =	field(default_factory=_A					)
			__UpperCamelCase :  Union[str, Any]    =	field(default_factory=_A					)

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Optional[Any] ):
							UpperCamelCase_:							List[str]		=  len(list(m.modules() ) ) == 1 or isinstance(__a  ,		nn.Convad ) or isinstance(__a  ,		nn.BatchNormad )
							if has_not_submodules:
											self.traced.append(__a )

			def __call__(    self	:    int  ,		snake_case_	:    Union[str, Any] ):
							for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook ) )
							self.module(__a )
							[x.remove() for x in self.handles]
							return self




			@property
			def  lowerCAmelCase__		(    self	:    str ):
							return list(filter(lambda snake_case_ : len(list(x.state_dict().keys() ) ) > 0  ,		self.traced ) )




@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Dict    =	42
			__UpperCamelCase :  Optional[int]    =	42
			__UpperCamelCase :  Optional[int]    =	0
			__UpperCamelCase :  List[str]    =	field(default_factory=_A					)
			__UpperCamelCase :  Any    =	field(default_factory=_A					)

			def __call__(    self	:    Union[str, Any]  ,		snake_case_	:    Union[str, Any] ):
							UpperCamelCase_:							Any		=  Tracker(self.dest )(__a ).parametrized
							UpperCamelCase_:							Optional[Any]		=  Tracker(self.src )(__a ).parametrized

							UpperCamelCase_:							str		=  list(filter(lambda snake_case_ : type(__a ) not in self.src_skip  ,		__a ) )
							UpperCamelCase_:							Optional[Any]		=  list(filter(lambda snake_case_ : type(__a ) not in self.dest_skip  ,		__a ) )

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

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







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase = True  )					->		Any:
				print(F'''Converting {name}...'''  )
				with torch.no_grad():
								UpperCamelCase_:							str		=  timm.create_model(__snake_case       ,      pretrained=__snake_case  ).eval()
								UpperCamelCase_:							Optional[Any]		=  ResNetForImageClassification(__snake_case  ).eval()
								UpperCamelCase_:							Union[str, Any]		=  ModuleTransfer(src=__snake_case       ,      dest=__snake_case  )
								UpperCamelCase_:							str		=  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."

				UpperCamelCase_:							Dict		=  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
								UpperCamelCase_:							List[Any]		=  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     A__   (  lowerCamelCase       ,      lowerCamelCase = None       ,      lowerCamelCase = True  )					->		Union[str, Any]:
				UpperCamelCase_:							List[str]		=  """imagenet-1k-id2label.json"""
				UpperCamelCase_:							int		=  10_00
				UpperCamelCase_:							Optional[Any]		=  (1, num_labels)

				UpperCamelCase_:							Tuple		=  """huggingface/label-files"""
				UpperCamelCase_:							Dict		=  num_labels
				UpperCamelCase_:							Optional[int]		=  json.load(open(hf_hub_download(__snake_case       ,      __snake_case       ,      repo_type="""dataset"""  )       ,      """r"""  )  )
				UpperCamelCase_:							Union[str, Any]		=  {int(__snake_case  ): v for k, v in idalabel.items()}

				UpperCamelCase_:							Any		=  idalabel
				UpperCamelCase_:							Optional[Any]		=  {v: k for k, v in idalabel.items()}

				UpperCamelCase_:							str		=  partial(__snake_case       ,      num_labels=__snake_case       ,      idalabel=__snake_case       ,      labelaid=__snake_case  )

				UpperCamelCase_:							Union[str, Any]		=  {
				    """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__":
							lowerCamelCase_  :	Optional[int] 				=							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_  :	int 				=							parser.parse_args()
							lowerCamelCase_  :	List[Any] 				=							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 unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow

@is_pipeline_test
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@require_torch
			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Any		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""hf-internal-testing/tiny-clap-htsat-unfused""" )
							UpperCamelCase_:							str		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							Optional[int]		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}]  ,		)

			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass

			@slow
			@require_torch
			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[str]		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""laion/clap-htsat-unfused"""  ,		)
							# This is an audio of a dog
							UpperCamelCase_:							Tuple		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							int		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )

							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        {"""score""": 0.999, """label""": """Sound of a dog"""},
							        {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							    ]  ,		)

							UpperCamelCase_:							Any		=  audio_classifier([audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)
							UpperCamelCase_:							Any		=  audio_classifier(
							    [audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  ,		batch_size=5 )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)




			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass






