
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()
SCREAMING_SNAKE_CASE				:Any									=							logging.get_logger()





@dataclass
class 				UpperCAmelCase :




	'''simple docstring'''





	snake_case_          =	42
	snake_case_          =	field(default_factory=__SCREAMING_SNAKE_CASE    )
	snake_case_          =	field(default_factory=__SCREAMING_SNAKE_CASE    )






	def 							UpperCamelCase_    (			self   :					str				,A   :					Dict				,A   :					Tensor				,A   :					Tensor					):
					__A       =					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   :					Dict				,A   :					Tensor					):
					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 							UpperCamelCase_    (			self   :					str					):
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda A					: len(list(x.state_dict().keys()					)					) > 0				,self.traced					)					)





@dataclass
class 				UpperCAmelCase :




	'''simple docstring'''





	snake_case_          =	42
	snake_case_          =	42
	snake_case_          =	0
	snake_case_          =	field(default_factory=__SCREAMING_SNAKE_CASE    )
	snake_case_          =	field(default_factory=__SCREAMING_SNAKE_CASE    )






	def __call__(			self   :					Tuple				,A   :					Tensor					):
					__A       =					Tracker(self.dest					)(A					).parametrized
					__A       =					Tracker(self.src					)(A					).parametrized

					__A       =					list(filter(lambda A					: type(A					) not in self.src_skip				,A					)					)
					__A       =					list(filter(lambda A					: 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 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_ = True		)						->      List[str]:







				"""simple docstring"""



				print(F'''Converting {name}...'''		)
				with torch.no_grad():
								__A       =					timm.create_model(a_						,  pretrained=a_		).eval()
								__A       =					ResNetForImageClassification(a_		).eval()
								__A       =					ModuleTransfer(src=a_						,  dest=a_		)
								__A       =					torch.randn((1, 3, 2_2_4, 2_2_4)		)
								module_transfer(a_		)

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

				__A       =					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
								__A       =					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		)						->      Tuple:







				"""simple docstring"""



				__A       =					"imagenet-1k-id2label.json"
				__A       =					1_0_0_0
				__A       =					(1, num_labels)

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

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

				__A       =					partial(a_						,  num_labels=a_						,  idalabel=a_						,  labelaid=a_		)

				__A       =					{
				    "resnet18": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="basic"		),
				    "resnet26": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="bottleneck"		),
				    "resnet34": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="basic"		),
				    "resnet50": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="bottleneck"		),
				    "resnet101": ImageNetPreTrainedConfig(
				        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="bottleneck"		),
				    "resnet152": ImageNetPreTrainedConfig(
				        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  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__":
					SCREAMING_SNAKE_CASE				:Dict									=							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.',
					)

					SCREAMING_SNAKE_CASE				:Union[str, Any]									=							parser.parse_args()
					SCREAMING_SNAKE_CASE				: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)








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           # For consistency across different places the DisjunctiveConstraint is called,
           # dc.token_ids is a list of integers. It is also initialized only by integers.

           __lowerCAmelCase             =       [[1, 2, 4], [1, 2, 3, 4]]
           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)
           self.assertTrue(isinstance(dc.token_ids       ,    __UpperCamelCase						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           # We can't have constraints that are complete subsets of another. This leads to a preverse
           # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
           # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
           # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
           # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
           __lowerCAmelCase             =       [[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(__UpperCamelCase						)  # fails here


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(3						)
           __lowerCAmelCase             =       stepped is True and completed is True and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(4						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2, 4]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

           dc.reset()

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 3						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 2						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.remaining() == 0						)
           self.assertTrue(dc.current_seq == [1, 2, 5]						)






