import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__UpperCAmelCase     							=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}



class   lowerCamelCase				(nn.Module      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase       )     ->							int:
							super().__init__()
							UpperCAmelCase_   :				Any						=						torchvision.models.resnetaaa(pretrained=_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						list(model.children()       )[:-2]
							UpperCAmelCase_   :				Union[str, Any]						=						nn.Sequential(*_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							UpperCAmelCase_   :				Any						=						self.pool(self.model(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Dict						=						torch.flatten(_UpperCamelCase    ,		start_dim=2       )
							UpperCAmelCase_   :				List[Any]						=						out.transpose(1    ,		2       ).contiguous()
							return out  # BxNx2048





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						[json.loads(_UpperCamelCase       ) for l in open(_UpperCamelCase       )]
							UpperCAmelCase_   :				Union[str, Any]						=						os.path.dirname(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						tokenizer
							UpperCAmelCase_   :				List[str]						=						labels
							UpperCAmelCase_   :				Dict						=						len(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						max_seq_length

							UpperCAmelCase_   :				List[Any]						=						transforms



		def __len__(							self       )     ->							Optional[Any]:
							return len(self.data       )



		def __getitem__(							self    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']    ,		add_special_tokens=_UpperCamelCase       )       )
							UpperCAmelCase_   :				Dict						=						sentence[0], sentence[1:-1], sentence[-1]
							UpperCAmelCase_   :				Optional[int]						=						sentence[: self.max_seq_length]

							UpperCAmelCase_   :				Dict						=						torch.zeros(self.n_classes       )
							UpperCAmelCase_   :				Tuple						=						1

							UpperCAmelCase_   :				int						=						Image.open(os.path.join(self.data_dir    ,		self.data[index]['img']       )       ).convert('RGB'       )
							UpperCAmelCase_   :				Optional[Any]						=						self.transforms(_UpperCamelCase       )

							return {
							    "image_start_token": start_token,
							    "image_end_token": end_token,
							    "sentence": sentence,
							    "image": image,
							    "label": label,
							}



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Dict						=						Counter()
							for row in self.data:
												label_freqs.update(row['label']       )
							return label_freqs





def 					lowercase__				(					__snake_case   :							Dict  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[len(row['sentence']  ) for row in batch]
					UpperCAmelCase_   :				Union[str, Any]						=						len(__snake_case  ), max(__snake_case  )

					UpperCAmelCase_   :				List[str]						=						torch.zeros(__snake_case					,	__snake_case					,	dtype=torch.long  )
					UpperCAmelCase_   :				Optional[Any]						=						torch.zeros(__snake_case					,	__snake_case					,	dtype=torch.long  )

					for i_batch, (input_row, length) in enumerate(zip(__snake_case					,	__snake_case  )  ):
										UpperCAmelCase_   :				Any						=						input_row['sentence']
										UpperCAmelCase_   :				List[Any]						=						1

					UpperCAmelCase_   :				int						=						torch.stack([row['image'] for row in batch]  )
					UpperCAmelCase_   :				Optional[Any]						=						torch.stack([row['label'] for row in batch]  )
					UpperCAmelCase_   :				Dict						=						torch.stack([row['image_start_token'] for row in batch]  )
					UpperCAmelCase_   :				Union[str, Any]						=						torch.stack([row['image_end_token'] for row in batch]  )

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def 					lowercase__				(					):


					'''simple docstring'''


					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]






def 					lowercase__				(					):


					'''simple docstring'''


					return transforms.Compose(
					    [
					        transforms.Resize(256  ),
					        transforms.CenterCrop(224  ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]					,	std=[0.12221994, 0.12145835, 0.14380469]					,	),
					    ]  )














import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__UpperCAmelCase     							=						'.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__UpperCAmelCase     							=						[
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						SavedModel()
					UpperCAmelCase_   :				Optional[Any]						=						[]

					with open(os.path.join(__snake_case					,	'utils'					,	'tf_ops'					,	'onnx.json'  )  ) as f:
										UpperCAmelCase_   :				Optional[Any]						=						json.load(__snake_case  )['opsets']

					for i in range(1					,	opset + 1  ):
										onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

					with open(__snake_case					,	'rb'  ) as f:
										saved_model.ParseFromString(f.read()  )

					UpperCAmelCase_   :				List[Any]						=						set()

					# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
					for meta_graph in saved_model.meta_graphs:
										# Add operations in the graph definition
										model_op_names.update(node.op for node in meta_graph.graph_def.node  )

										# Go through the functions in the graph definition
										for func in meta_graph.graph_def.library.function:
															# Add operations in each function
															model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
					UpperCAmelCase_   :				Optional[int]						=						sorted(__snake_case  )
					UpperCAmelCase_   :				int						=						[]

					for op in model_op_names:
										if op not in onnx_ops and op not in INTERNAL_OPS:
															incompatible_ops.append(__snake_case  )

					if strict and len(__snake_case  ) > 0:
										raise Exception(F"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops  )
					elif len(__snake_case  ) > 0:
										print(F"Found the following incompatible ops for the opset {opset}:"  )
										print(*__snake_case					,	sep='\n'  )
					else:
										print(F"The saved model {saved_model_path} can properly be converted with ONNX."  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						argparse.ArgumentParser()
		parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
		parser.add_argument(
		    '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
		)
		parser.add_argument(
		    '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
		)
		parser.add_argument(
		    '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
		)
		__UpperCAmelCase     							=						parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)






