




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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		['image_processor', 'tokenizer']
	__magic_name__   :       int      	=		'OwlViTImageProcessor'
	__magic_name__   :       Dict      	=		('CLIPTokenizer', 'CLIPTokenizerFast')






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







						"""simple docstring"""







						__UpperCamelCase					:	int             =      None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""					,     lowerCAmelCase					,     )
											__UpperCamelCase					:	Optional[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__(lowerCAmelCase					,     lowerCAmelCase	)






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







						"""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(lowerCAmelCase					,     lowerCAmelCase	) or (isinstance(lowerCAmelCase					,     lowerCAmelCase	) and not isinstance(text[0]					,     lowerCAmelCase	)):
																__UpperCamelCase					:	Union[str, Any]             =      [self.tokenizer(lowerCAmelCase					,     padding=lowerCAmelCase					,     return_tensors=lowerCAmelCase					,     **lowerCAmelCase	)]

											elif isinstance(lowerCAmelCase					,     lowerCAmelCase	) and isinstance(text[0]					,     lowerCAmelCase	):
																__UpperCamelCase					:	Tuple             =      []

																# Maximum number of queries across batch
																__UpperCamelCase					:	Tuple             =      max([len(lowerCAmelCase	) for t in text]	)

																# Pad all batch samples to max number of text queries
																for t in text:
																					if len(lowerCAmelCase	) != max_num_queries:
																										__UpperCamelCase					:	Union[str, Any]             =      t + [""" """] * (max_num_queries - len(lowerCAmelCase	))

																					__UpperCamelCase					:	Union[str, Any]             =      self.tokenizer(lowerCAmelCase					,     padding=lowerCAmelCase					,     return_tensors=lowerCAmelCase					,     **lowerCAmelCase	)
																					encodings.append(lowerCAmelCase	)
											else:
																raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""	)

											if return_tensors == "np":
																__UpperCamelCase					:	int             =      np.concatenate([encoding["""input_ids"""] for encoding in encodings]					,     axis=0	)
																__UpperCamelCase					:	Optional[int]             =      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					:	str             =      jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]					,     axis=0	)
																__UpperCamelCase					:	Optional[int]             =      jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]					,     axis=0	)

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

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

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

																__UpperCamelCase					:	List[Any]             =      tf.stack([encoding["""input_ids"""] for encoding in encodings]					,     axis=0	)
																__UpperCamelCase					:	Tuple             =      tf.stack([encoding["""attention_mask"""] for encoding in encodings]					,     axis=0	)

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

											__UpperCamelCase					:	List[Any]             =      BatchEncoding()
											__UpperCamelCase					:	Dict             =      input_ids
											__UpperCamelCase					:	Union[str, Any]             =      attention_mask

						if query_images is not None:
											__UpperCamelCase					:	Union[str, Any]             =      BatchEncoding()
											__UpperCamelCase					:	List[str]             =      self.image_processor(
											    lowerCAmelCase					,     return_tensors=lowerCAmelCase					,     **lowerCAmelCase	).pixel_values
											__UpperCamelCase					:	List[str]             =      query_pixel_values

						if images is not None:
											__UpperCamelCase					:	Optional[Any]             =      self.image_processor(lowerCAmelCase					,     return_tensors=lowerCAmelCase					,     **lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						Tuple					,     *lowerCAmelCase		:						Optional[Any]					,     **lowerCAmelCase		:						int	)	->       Tuple:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						List[Any]					,     *lowerCAmelCase		:						Dict					,     **lowerCAmelCase		:						Optional[int]	)	->       Optional[Any]:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     *lowerCAmelCase		:						Dict					,     **lowerCAmelCase		:						Tuple	)	->       Optional[int]:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Tuple					,     *lowerCAmelCase		:						Tuple					,     **lowerCAmelCase		:						Optional[Any]	)	->       Optional[Any]:







						"""simple docstring"""







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






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







						"""simple docstring"""







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






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







						"""simple docstring"""







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






	@property
	def 					lowerCamelCase__     (					self		:						Dict	)	->       Optional[int]:







						"""simple docstring"""







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












import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a__		     =     ['''small''', '''medium''', '''large''']

a__		     =     '''lm_head.decoder.weight'''
a__		     =     '''lm_head.weight'''





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
	a__		     =     parser.parse_args()
	for MODEL in DIALOGPT_MODELS:
		a__		     =     os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
		a__		     =     f"./DialoGPT-{MODEL}"
		convert_dialogpt_checkpoint(
		    checkpoint_path,
		    pytorch_dump_folder_path,
		)






