


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__				):
					'''simple docstring'''






					A__     =					['''image_processor''', '''tokenizer''']
					A__     =					'''OwlViTImageProcessor'''
					A__     =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')

					def __init__(self       :		int ,   _UpperCAmelCase       :		Tuple=None ,   _UpperCAmelCase       :		List[str]=None ,   **_UpperCAmelCase       :		Optional[Any]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						None
							if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead.""" ,   _UpperCAmelCase ,   )
									lowercase__	     =						kwargs.pop("""feature_extractor"""						)

							lowercase__	     =						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__(_UpperCAmelCase ,   _UpperCAmelCase						)

					def __call__(self       :		Any ,   _UpperCAmelCase       :		Optional[int]=None ,   _UpperCAmelCase       :		str=None ,   _UpperCAmelCase       :		List[str]=None ,   _UpperCAmelCase       :		Dict="max_length" ,   _UpperCAmelCase       :		Optional[int]="np" ,   **_UpperCAmelCase       :		str						)		->  Tuple:




							"""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(_UpperCAmelCase ,   _UpperCAmelCase						) or (isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) and not isinstance(text[0] ,   _UpperCAmelCase						)):
											lowercase__	     =						[self.tokenizer(_UpperCAmelCase ,   padding=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   **_UpperCAmelCase						)]

									elif isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) and isinstance(text[0] ,   _UpperCAmelCase						):
											lowercase__	     =						[]

											# Maximum number of queries across batch
											lowercase__	     =						max([len(_UpperCAmelCase						) for t in text]						)

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

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

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

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

											lowercase__	     =						jnp.concatenate([encoding["""input_ids"""] for encoding in encodings] ,   axis=0						)
											lowercase__	     =						jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings] ,   axis=0						)

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

											lowercase__	     =						torch.cat([encoding["""input_ids"""] for encoding in encodings] ,   dim=0						)
											lowercase__	     =						torch.cat([encoding["""attention_mask"""] for encoding in encodings] ,   dim=0						)

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

											lowercase__	     =						tf.stack([encoding["""input_ids"""] for encoding in encodings] ,   axis=0						)
											lowercase__	     =						tf.stack([encoding["""attention_mask"""] for encoding in encodings] ,   axis=0						)

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

									lowercase__	     =						BatchEncoding()
									lowercase__	     =						input_ids
									lowercase__	     =						attention_mask

							if query_images is not None:
									lowercase__	     =						BatchEncoding()
									lowercase__	     =						self.image_processor(
									    _UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   **_UpperCAmelCase						).pixel_values
									lowercase__	     =						query_pixel_values

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

							if text is not None and images is not None:
									lowercase__	     =						image_features.pixel_values
									return encoding
							elif query_images is not None and images is not None:
									lowercase__	     =						image_features.pixel_values
									return encoding
							elif text is not None or query_images is not None:
									return encoding
							else:
									return BatchEncoding(data=dict(**_UpperCAmelCase						) ,   tensor_type=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Any ,   *_UpperCAmelCase       :		Optional[int] ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  str:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str ,   *_UpperCAmelCase       :		Optional[int] ,   **_UpperCAmelCase       :		Any						)		->  Any:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str] ,   *_UpperCAmelCase       :		Any ,   **_UpperCAmelCase       :		int						)		->  List[Any]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Any ,   *_UpperCAmelCase       :		List[str] ,   **_UpperCAmelCase       :		Any						)		->  Any:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Dict ,   *_UpperCAmelCase       :		Dict ,   **_UpperCAmelCase       :		int						)		->  int:




							"""simple docstring"""


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

					@property
					def     lowerCamelCase__	(self       :		List[str]						)		->  Dict:




							"""simple docstring"""


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


					@property
					def     lowerCamelCase__	(self       :		List[Any]						)		->  int:




							"""simple docstring"""


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





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
	)
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

