




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     List[str]		    =       {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''gptj'''
	A    							= {
	    '''max_position_embeddings''': '''n_positions''',
	    '''hidden_size''': '''n_embd''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=5_0400      ,     SCREAMING_SNAKE_CASE=2048      ,     SCREAMING_SNAKE_CASE=4096      ,     SCREAMING_SNAKE_CASE=28      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=64      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE="gelu_new"      ,     SCREAMING_SNAKE_CASE=0.0      ,     SCREAMING_SNAKE_CASE=0.0      ,     SCREAMING_SNAKE_CASE=0.0      ,     SCREAMING_SNAKE_CASE=1e-5      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=5_0256      ,     SCREAMING_SNAKE_CASE=5_0256      ,     SCREAMING_SNAKE_CASE=False      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						vocab_size
				SCREAMING_SNAKE_CASE_								=						n_positions
				SCREAMING_SNAKE_CASE_								=						n_embd
				SCREAMING_SNAKE_CASE_								=						n_layer
				SCREAMING_SNAKE_CASE_								=						n_head
				SCREAMING_SNAKE_CASE_								=						n_inner
				SCREAMING_SNAKE_CASE_								=						rotary_dim
				SCREAMING_SNAKE_CASE_								=						activation_function
				SCREAMING_SNAKE_CASE_								=						resid_pdrop
				SCREAMING_SNAKE_CASE_								=						embd_pdrop
				SCREAMING_SNAKE_CASE_								=						attn_pdrop
				SCREAMING_SNAKE_CASE_								=						layer_norm_epsilon
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						use_cache

				SCREAMING_SNAKE_CASE_								=						bos_token_id
				SCREAMING_SNAKE_CASE_								=						eos_token_id

				super().__init__(
				    bos_token_id=lowerCamelCase__      ,     eos_token_id=lowerCamelCase__      ,     tie_word_embeddings=lowerCamelCase__      ,     **lowerCamelCase__      )




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = "default"      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     )  ->					List[Any]:




				"""simple docstring"""



				super().__init__(lowerCamelCase__      ,     task=lowerCamelCase__      ,     patching_specs=lowerCamelCase__      ,     use_past=lowerCamelCase__      )
				if not getattr(self._config      ,     'pad_token_id'      ,     lowerCamelCase__      ):
							# TODO: how to do that better?
							SCREAMING_SNAKE_CASE_								=						0





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
				if self.use_past:
							self.fill_with_past_key_values_(lowerCamelCase__      ,     direction='inputs'      )
							SCREAMING_SNAKE_CASE_								=						{0: 'batch', 1: 'past_sequence + sequence'}
				else:
							SCREAMING_SNAKE_CASE_								=						{0: 'batch', 1: 'sequence'}

				return common_inputs





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				return self._config.n_layer





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				return self._config.n_head





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = -1      ,     SCREAMING_SNAKE_CASE = -1      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					Mapping[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						super(lowerCamelCase__      ,     self      ).generate_dummy_inputs(
				    lowerCamelCase__      ,     batch_size=lowerCamelCase__      ,     seq_length=lowerCamelCase__      ,     is_pair=lowerCamelCase__      ,     framework=lowerCamelCase__      )

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE_								=						OrderedDict({'input_ids': common_inputs['input_ids']}      )

				# Need to add the past_keys
				if self.use_past:
							if not is_torch_available():
										raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
							else:
										import torch

										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						common_inputs['input_ids'].shape
										# Not using the same length for past_key_values
										SCREAMING_SNAKE_CASE_								=						seqlen + 2
										SCREAMING_SNAKE_CASE_								=						(
										    batch,
										    self.num_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // self.num_attention_heads,
										)
										SCREAMING_SNAKE_CASE_								=						[
										    (torch.zeros(lowerCamelCase__      ), torch.zeros(lowerCamelCase__      )) for _ in range(self.num_layers      )
										]

				SCREAMING_SNAKE_CASE_								=						common_inputs['attention_mask']
				if self.use_past:
							SCREAMING_SNAKE_CASE_								=						ordered_inputs['attention_mask'].dtype
							SCREAMING_SNAKE_CASE_								=						torch.cat(
							    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__      ,     lowerCamelCase__      ,     dtype=lowerCamelCase__      )]      ,     dim=1      )

				return ordered_inputs





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				return 13






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        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







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


			def __init__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       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__     ,		)
											__lowerCamelCase									=       kwargs.pop('feature_extractor'				)

							__lowerCamelCase									=       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     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__="max_length"     ,		lowerCamelCase__="np"     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''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__				)):
															__lowerCamelCase									=       [self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)]

											elif isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and isinstance(text[0]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       []

															# Maximum number of queries across batch
															__lowerCamelCase									=       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:
																							__lowerCamelCase									=       t + [' '] * (max_num_queries - len(lowerCamelCase__				))

																			__lowerCamelCase									=       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":
															__lowerCamelCase									=       np.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       np.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

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

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

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

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

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

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

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

											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       input_ids
											__lowerCamelCase									=       attention_mask

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

							if images is not None:
											__lowerCamelCase									=       self.image_processor(lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)

							if text is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif query_images is not None and images is not None:
											__lowerCamelCase									=       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  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							return self.image_processor.post_process(*lowerCamelCase__     ,		**lowerCamelCase__				)


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



							'''simple docstring'''
							return self.image_processor.post_process_object_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''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  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor


