

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_            = logging.get_logger()

@dataclass
class 			a					:
						_lowercase        =     42
						_lowercase        =     field(default_factory=UpperCAmelCase			)
						_lowercase        =     field(default_factory=UpperCAmelCase			)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      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  ,   A_		):







										'''simple docstring'''


										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		):







										'''simple docstring'''


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







@dataclass
class 			a					:
						_lowercase        =     42
						_lowercase        =     42
						_lowercase        =     0
						_lowercase        =     field(default_factory=UpperCAmelCase			)
						_lowercase        =     field(default_factory=UpperCAmelCase			)


						def __call__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      Tracker(self.dest		)(A_		).parametrized
										_UpperCAmelCase    :						List[Any]							=      Tracker(self.src		)(A_		).parametrized

										_UpperCAmelCase    :						Union[str, Any]							=      list(filter(lambda A_		: type(A_		) not in self.src_skip  ,   A_		)		)
										_UpperCAmelCase    :						Optional[Any]							=      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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					ResNetConfig					, lowerCAmelCase:					Path					, lowerCAmelCase:					bool = True	)				->    int:
				print(F'Converting {name}...'	)
				with torch.no_grad():
								_UpperCAmelCase    :						Union[str, Any]							=      timm.create_model(lowerCAmelCase					, pretrained=lowerCAmelCase	).eval()
								_UpperCAmelCase    :						Optional[Any]							=      ResNetForImageClassification(lowerCAmelCase	).eval()
								_UpperCAmelCase    :						Optional[int]							=      ModuleTransfer(src=lowerCAmelCase					, dest=lowerCAmelCase	)
								_UpperCAmelCase    :						Tuple							=      torch.randn((1, 3, 224, 224)	)
								module_transfer(lowerCAmelCase	)

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

				_UpperCAmelCase    :						Dict							=      F'resnet{"-".join(name.split("resnet"	)	)}'
				print(lowerCAmelCase	)

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

								# we can use the convnext one
								_UpperCAmelCase    :						Union[str, 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=lowerCAmelCase					, )

								print(F'Pushed {checkpoint_name}'	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Path					, lowerCAmelCase:					str = None					, lowerCAmelCase:					bool = True	)				->    Optional[Any]:
				_UpperCAmelCase    :						List[Any]							=      "imagenet-1k-id2label.json"
				_UpperCAmelCase    :						Tuple							=      1000
				_UpperCAmelCase    :						str							=      (1, num_labels)

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

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

				_UpperCAmelCase    :						List[str]							=      partial(lowerCAmelCase					, num_labels=lowerCAmelCase					, idalabel=lowerCAmelCase					, labelaid=lowerCAmelCase	)

				_UpperCAmelCase    :						Union[str, Any]							=      {
				    "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, 1024, 2048]					, 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, 1024, 2048]					, layer_type="bottleneck"	),
				    "resnet101": ImageNetPreTrainedConfig(
				        depths=[3, 4, 23, 3]					, hidden_sizes=[256, 512, 1024, 2048]					, layer_type="bottleneck"	),
				    "resnet152": ImageNetPreTrainedConfig(
				        depths=[3, 8, 36, 3]					, hidden_sizes=[256, 512, 1024, 2048]					, layer_type="bottleneck"	),
				}

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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_            = parser.parse_args()
					SCREAMING_SNAKE_CASE_            = 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 warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__(    self  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						Any							=      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,   A_		)


						def __call__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="max_length"  ,   A_="np"  ,   **A_		):







										'''simple docstring'''


										if text is None and query_images is None and images is None:
														raise ValueError(
														    "You have to specify at least one text or query image or image. All three cannot be none."		)

										if text is not None:
														if isinstance(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

																		# Pad all batch samples to max number of text queries
																		for t in text:
																						if len(A_		) != max_num_queries:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)
																						encodings.append(A_		)
														else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"		)

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		_UpperCAmelCase    :						Optional[Any]							=      jnp.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						str							=      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "pt" and is_torch_available():
																		import torch

																		_UpperCAmelCase    :						str							=      torch.cat([encoding["input_ids"] for encoding in encodings]  ,   dim=0		)
																		_UpperCAmelCase    :						Dict							=      torch.cat([encoding["attention_mask"] for encoding in encodings]  ,   dim=0		)

														elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														else:
																		raise ValueError("Target return tensor type could not be returned"		)

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase    :						Any							=      image_features.pixel_values
														return encoding
										elif text is not None or query_images is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_object_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_image_guided_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,   A_  ,   )
										return self.image_processor_class





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,   A_  ,   )
										return self.image_processor


