






"""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            =	logging.get_logger()


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : nn.Module
						_SCREAMING_SNAKE_CASE  : List[nn.Module]												=		field(default_factory=SCREAMING_SNAKE_CASE__     )
						_SCREAMING_SNAKE_CASE  : list												=		field(default_factory=SCREAMING_SNAKE_CASE__     )






						def      __snake_case      (		self  :int			, __magic_name__  :Optional[Any]			, __magic_name__  :List[Any]			, __magic_name__  :List[str]				)      ->int:
										lowercase    :      List[str]       = len(list(m.modules()				)				) == 1 or isinstance(__lowerCAmelCase			, nn.Convad				) or isinstance(__lowerCAmelCase			, nn.BatchNormad				)
										if has_not_submodules:
														self.traced.append(__lowerCAmelCase				)






						def __call__(		self  :Any			, __magic_name__  :List[Any]				)      ->Dict:
										for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook				)				)
										self.module(__lowerCAmelCase				)
										[x.remove() for x in self.handles]
										return self






						@property
						def      __snake_case      (		self  :List[str]				)      ->Tuple:
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda __magic_name__				: len(list(x.state_dict().keys()				)				) > 0			, self.traced				)				)


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : nn.Module
						_SCREAMING_SNAKE_CASE  : nn.Module
						_SCREAMING_SNAKE_CASE  : int												=		0
						_SCREAMING_SNAKE_CASE  : List												=		field(default_factory=SCREAMING_SNAKE_CASE__     )
						_SCREAMING_SNAKE_CASE  : List												=		field(default_factory=SCREAMING_SNAKE_CASE__     )






						def __call__(		self  :Optional[int]			, __magic_name__  :str				)      ->Optional[Any]:
										lowercase    :      Tuple       = Tracker(self.dest				)(__lowerCAmelCase				).parametrized
										lowercase    :      Tuple       = Tracker(self.src				)(__lowerCAmelCase				).parametrized

										lowercase    :      Optional[Any]       = list(filter(lambda __magic_name__				: type(__lowerCAmelCase				) not in self.src_skip			, __lowerCAmelCase				)				)
										lowercase    :      List[str]       = list(filter(lambda __magic_name__				: type(__lowerCAmelCase				) not in self.dest_skip			, __lowerCAmelCase				)				)

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

										for dest_m, src_m in zip(__lowerCAmelCase			, __lowerCAmelCase				):
														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  ,				_A  ,				_A  ,				_A = True )     ->       Tuple:
				print(F"""Converting {name}...""" )
				with torch.no_grad():
								lowercase    :      Tuple       = timm.create_model(a_  ,				pretrained=a_ ).eval()
								lowercase    :      Optional[int]       = ResNetForImageClassification(a_ ).eval()
								lowercase    :      Dict       = ModuleTransfer(src=a_  ,				dest=a_ )
								lowercase    :      List[str]       = torch.randn((1, 3, 224, 224) )
								module_transfer(a_ )

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

				lowercase    :      str       = 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
								lowercase    :      Optional[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=a_  ,				)

								print(F"""Pushed {checkpoint_name}""" )


def      UpperCamelCase       ( _A  ,				_A = None  ,				_A = True )     ->       List[Any]:
				lowercase    :      Dict       = """imagenet-1k-id2label.json"""
				lowercase    :      Tuple       = 1_000
				lowercase    :      Optional[Any]       = (1, num_labels)

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

				lowercase    :      Any       = idalabel
				lowercase    :      int       = {v: k for k, v in idalabel.items()}

				lowercase    :      Tuple       = partial(a_  ,				num_labels=a_  ,				idalabel=a_  ,				labelaid=a_ )

				lowercase    :      Optional[int]       = {
				    """resnet18""": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]  ,				hidden_sizes=[64, 128, 256, 512]  ,				layer_type="""basic""" ),
				    """resnet26""": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]  ,				hidden_sizes=[256, 512, 1_024, 2_048]  ,				layer_type="""bottleneck""" ),
				    """resnet34""": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]  ,				hidden_sizes=[64, 128, 256, 512]  ,				layer_type="""basic""" ),
				    """resnet50""": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]  ,				hidden_sizes=[256, 512, 1_024, 2_048]  ,				layer_type="""bottleneck""" ),
				    """resnet101""": ImageNetPreTrainedConfig(
				        depths=[3, 4, 23, 3]  ,				hidden_sizes=[256, 512, 1_024, 2_048]  ,				layer_type="""bottleneck""" ),
				    """resnet152""": ImageNetPreTrainedConfig(
				        depths=[3, 8, 36, 3]  ,				hidden_sizes=[256, 512, 1_024, 2_048]  ,				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            =	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            =	parser.parse_args()
				_lowerCAmelCase            =	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 unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase      					="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"




class      A     (   unittest.TestCase, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				load_tool("""text-question-answering"""			)
       self.tool.setup()
       UpperCamelCase_					: List[str]            =				load_tool("""text-question-answering"""      ,  remote=__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				self.remote_tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				self.tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.remote_tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)






