




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
			from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			List[str]=None				,       UpperCamelCase__							:			int=None				,       UpperCamelCase__							:			str=None				,       UpperCamelCase__							:			int=None				,       UpperCamelCase__							:			Tuple=None				,       )    -> List[Any]:
							'''simple docstring'''


							if attention_mask is None:
														__lowercase      =    input_ids.ne(config.pad_token_id				)
							if decoder_attention_mask is None:
														__lowercase      =    decoder_input_ids.ne(config.pad_token_id				)
							if head_mask is None:
														__lowercase      =    torch.ones(config.encoder_layers				,       config.encoder_attention_heads				,       device=UpperCamelCase__				)
							if decoder_head_mask is None:
														__lowercase      =    torch.ones(config.decoder_layers				,       config.decoder_attention_heads				,       device=UpperCamelCase__				)
							if cross_attn_head_mask is None:
														__lowercase      =    torch.ones(config.decoder_layers				,       config.decoder_attention_heads				,       device=UpperCamelCase__				)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}







class a :
						"""simple docstring"""

						def __init__( self :     Optional[int]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     int=13				,							lowerCamelCase__ :     Union[str, Any]=7				,							lowerCamelCase__ :     int=True				,							lowerCamelCase__ :     Tuple=False				,							lowerCamelCase__ :     Any=99				,							lowerCamelCase__ :     Tuple=16				,							lowerCamelCase__ :     str=2				,							lowerCamelCase__ :     Dict=4				,							lowerCamelCase__ :     str=4				,							lowerCamelCase__ :     Optional[Any]="relu"				,							lowerCamelCase__ :     List[Any]=0.1				,							lowerCamelCase__ :     int=0.1				,							lowerCamelCase__ :     Optional[int]=0.0				,							lowerCamelCase__ :     Optional[int]=0.0				,							lowerCamelCase__ :     Dict=20				,							lowerCamelCase__ :     str=2				,							lowerCamelCase__ :     List[str]=1				,							lowerCamelCase__ :     List[Any]=0				,							)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    parent
													__lowercase      =    batch_size
													__lowercase      =    seq_length
													__lowercase      =    is_training
													__lowercase      =    use_labels
													__lowercase      =    vocab_size
													__lowercase      =    hidden_size
													__lowercase      =    num_hidden_layers
													__lowercase      =    num_attention_heads
													__lowercase      =    intermediate_size
													__lowercase      =    hidden_act
													__lowercase      =    hidden_dropout_prob
													__lowercase      =    attention_probs_dropout_prob
													__lowercase      =    encoder_layerdrop
													__lowercase      =    decoder_layerdrop
													__lowercase      =    max_position_embeddings
													__lowercase      =    eos_token_id
													__lowercase      =    pad_token_id
													__lowercase      =    bos_token_id






						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													__lowercase      =    ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size				)
													__lowercase      =    self.eos_token_id  # Eos Token
													__lowercase      =    ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size				)

													# we need to clamp the input ids here to avoid having pad token in between
													# this is because for M2M100 the position_ids are prepared such that
													# all pad tokens have pos id = 2 and rest are between 2..seq_length
													# and the seq_length here is seq_length - num_pad_tokens
													# but when using past, there is no way of knowing if the past input ids had
													# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
													# position_ids being off by num_pad_tokens in past input
													__lowercase      =    input_ids.clamp(self.pad_token_id + 1				)
													__lowercase      =    decoder_input_ids.clamp(self.pad_token_id + 1				)

													__lowercase      =    self.get_config()
													__lowercase      =    prepare_mam_aaa_inputs_dict(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
													return config, inputs_dict






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


													return MaMaaaConfig(
													    vocab_size=self.vocab_size				,							d_model=self.hidden_size				,							encoder_layers=self.num_hidden_layers				,							decoder_layers=self.num_hidden_layers				,							encoder_attention_heads=self.num_attention_heads				,							decoder_attention_heads=self.num_attention_heads				,							encoder_ffn_dim=self.intermediate_size				,							decoder_ffn_dim=self.intermediate_size				,							dropout=self.hidden_dropout_prob				,							attention_dropout=self.attention_probs_dropout_prob				,							encoder_layerdrop=self.encoder_layerdrop				,							decoder_layerdrop=self.decoder_layerdrop				,							max_position_embeddings=self.max_position_embeddings				,							eos_token_id=self.eos_token_id				,							bos_token_id=self.bos_token_id				,							pad_token_id=self.pad_token_id				,							)






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


													__lowercase     ,     __lowercase      =    self.prepare_config_and_inputs()
													return config, inputs_dict






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     List[Any]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    MaMaaaModel(config=lowerCamelCase__				).get_decoder().to(lowerCamelCase__				).eval()
													__lowercase      =    inputs_dict['''input_ids''']
													__lowercase      =    inputs_dict['''attention_mask''']
													__lowercase      =    inputs_dict['''head_mask''']

													# first forward pass
													__lowercase      =    model(lowerCamelCase__				,							attention_mask=lowerCamelCase__				,							head_mask=lowerCamelCase__				,							use_cache=lowerCamelCase__				)

													__lowercase     ,     __lowercase      =    outputs.to_tuple()

													# create hypothetical multiple next token and extent to next_input_ids
													__lowercase      =    ids_tensor((self.batch_size, 3)				,							config.vocab_size				)
													__lowercase      =    ids_tensor((self.batch_size, 3)				,							2				)

													# append to next input_ids and
													__lowercase      =    torch.cat([input_ids, next_tokens]				,							dim=-1				)
													__lowercase      =    torch.cat([attention_mask, next_attn_mask]				,							dim=-1				)

													__lowercase      =    model(lowerCamelCase__				,							attention_mask=lowerCamelCase__				)['''last_hidden_state''']
													__lowercase      =    model(lowerCamelCase__				,							attention_mask=lowerCamelCase__				,							past_key_values=lowerCamelCase__				)[
													    '''last_hidden_state'''
													]

													# select random slice
													__lowercase      =    ids_tensor((1,)				,							output_from_past.shape[-1]				).item()
													__lowercase      =    output_from_no_past[:, -3:, random_slice_idx].detach()
													__lowercase      =    output_from_past[:, :, random_slice_idx].detach()

													self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCamelCase__				,							lowerCamelCase__				,							atol=1e-2				)				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     List[str]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    MaMaaaModel(config=lowerCamelCase__				).to(lowerCamelCase__				).eval()
													__lowercase      =    model(**lowerCamelCase__				)

													__lowercase      =    outputs.encoder_last_hidden_state
													__lowercase      =    outputs.last_hidden_state

													with tempfile.TemporaryDirectory() as tmpdirname:
																				__lowercase      =    model.get_encoder()
																				encoder.save_pretrained(lowerCamelCase__				)
																				__lowercase      =    MaMaaaEncoder.from_pretrained(lowerCamelCase__				).to(lowerCamelCase__				)

													__lowercase      =    encoder(inputs_dict['''input_ids''']				,							attention_mask=inputs_dict['''attention_mask''']				)[
													    0
													]

													self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																				__lowercase      =    model.get_decoder()
																				decoder.save_pretrained(lowerCamelCase__				)
																				__lowercase      =    MaMaaaDecoder.from_pretrained(lowerCamelCase__				).to(lowerCamelCase__				)

													__lowercase      =    decoder(
													    input_ids=inputs_dict['''decoder_input_ids''']				,							attention_mask=inputs_dict['''decoder_attention_mask''']				,							encoder_hidden_states=lowerCamelCase__				,							encoder_attention_mask=inputs_dict['''attention_mask''']				,							)[0]

													self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3				)







@require_torch
class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		(
						    (
						        MaMaaaModel,
						        MaMaaaForConditionalGeneration,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_	:       int       	=		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
						UpperCamelCase_	:       Optional[Any]       	=		(
						    {
						        'conversational': MaMaaaForConditionalGeneration,
						        'feature-extraction': MaMaaaModel,
						        'summarization': MaMaaaForConditionalGeneration,
						        'text2text-generation': MaMaaaForConditionalGeneration,
						        'translation': MaMaaaForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase_	:       Union[str, Any]       	=		True
						UpperCamelCase_	:       Tuple       	=		True
						UpperCamelCase_	:       List[str]       	=		False
						UpperCamelCase_	:       List[Any]       	=		False






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Optional[Any]				)    ->						Optional[int]:
													"""simple docstring"""


													if pipeline_test_casse_name == "TranslationPipelineTests":
																				# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																				# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																				return True

													return False






						def     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    MaMaaaModelTester(self				)
													__lowercase      =    ConfigTester(self				,							config_class=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													self.config_tester.run_common_tests()






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				__lowercase      =    model_class(lowerCamelCase__				)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(lowerCamelCase__				)
																											__lowercase     ,     __lowercase      =    model_class.from_pretrained(lowerCamelCase__				,							output_loading_info=lowerCamelCase__				)
																				self.assertEqual(info['''missing_keys''']				,							[]				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						str:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																				__lowercase      =    model_class(lowerCamelCase__				)
																				model.to(lowerCamelCase__				)
																				model.eval()

																				__lowercase      =    copy.deepcopy(self._prepare_for_class(lowerCamelCase__				,							lowerCamelCase__				)				)

																				if not self.is_encoder_decoder:
																											__lowercase      =    inputs['''input_ids''']
																											del inputs["input_ids"]
																				else:
																											__lowercase      =    inputs['''input_ids''']
																											__lowercase      =    inputs.get('''decoder_input_ids'''				,							lowerCamelCase__				)
																											del inputs["input_ids"]
																											inputs.pop('''decoder_input_ids'''				,							lowerCamelCase__				)

																				__lowercase      =    model.get_input_embeddings()
																				if not self.is_encoder_decoder:
																											__lowercase      =    wte(lowerCamelCase__				)
																				else:
																											__lowercase      =    wte(lowerCamelCase__				)
																											__lowercase      =    wte(lowerCamelCase__				)

																				with torch.no_grad():
																											model(**lowerCamelCase__				)[0]






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs()
													__lowercase      =    input_dict['''input_ids''']
													__lowercase      =    input_ids.ne(1				).to(lowerCamelCase__				)
													__lowercase      =    MaMaaaForConditionalGeneration(lowerCamelCase__				).eval().to(lowerCamelCase__				)
													if torch_device == "cuda":
																				model.half()
													model.generate(lowerCamelCase__				,							attention_mask=lowerCamelCase__				)
													model.generate(num_beams=4				,							do_sample=lowerCamelCase__				,							early_stopping=lowerCamelCase__				,							num_return_sequences=3				)







def 		_A(       UpperCamelCase__							:			List[Any]				)    -> Optional[Any]:
							'''simple docstring'''


							return torch.tensor(UpperCamelCase__				,       dtype=torch.long				,       device=UpperCamelCase__				)


UpperCAmelCase__				     = 1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class a (     unittest.TestCase  ):
						"""simple docstring"""

						@cached_property
						def     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				)






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''				).to(lowerCamelCase__				)
													__lowercase      =    _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]				)
													__lowercase      =    _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]				)
													__lowercase      =    prepare_mam_aaa_inputs_dict(model.config				,							lowerCamelCase__				,							lowerCamelCase__				)
													with torch.no_grad():
																				__lowercase      =    model(**lowerCamelCase__				)[0]
													__lowercase      =    torch.Size((1, 11, 1_024)				)
													self.assertEqual(output.shape				,							lowerCamelCase__				)
													# change to expected output here
													__lowercase      =    torch.tensor(
													    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]				,							device=lowerCamelCase__				)
													self.assertTrue(torch.allclose(output[:, :3, :3]				,							lowerCamelCase__				,							atol=lowerCamelCase__				)				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''				).to(lowerCamelCase__				)

													# change to intended input
													__lowercase      =    _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]				)
													__lowercase      =    _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]				)
													__lowercase      =    prepare_mam_aaa_inputs_dict(model.config				,							lowerCamelCase__				,							lowerCamelCase__				)
													with torch.no_grad():
																				__lowercase      =    model(**lowerCamelCase__				)[0]
													__lowercase      =    torch.Size((1, 11, model.config.vocab_size)				)
													self.assertEqual(output.shape				,							lowerCamelCase__				)
													# change to expected output here
													__lowercase      =    torch.tensor(
													    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]				,							device=lowerCamelCase__				)
													self.assertTrue(torch.allclose(output[:, :3, :3]				,							lowerCamelCase__				,							atol=lowerCamelCase__				)				)






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''				).to(lowerCamelCase__				)
													__lowercase      =    MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				,							src_lang='''fr'''				,							tgt_lang='''en'''				)

													__lowercase      =    [
													    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
													    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
													    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
													    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
													    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
													]

													# The below article tests that we don't add any hypotheses outside of the top n_beams
													__lowercase      =    tokenizer(lowerCamelCase__				,							padding=lowerCamelCase__				,							return_tensors='''pt'''				)

													__lowercase      =    model.generate(
													    input_ids=dct['''input_ids'''].to(lowerCamelCase__				)				,							attention_mask=dct['''attention_mask'''].to(lowerCamelCase__				)				,							num_beams=5				,							forced_bos_token_id=tokenizer.get_lang_id('''en'''				)				,							)

													__lowercase      =    [
													    '''The NSA case highlights the total absence of intelligence debate''',
													    '''I think there are two levels of response from the French government.''',
													    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
													    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
													    ''' communications in France.''',
													]

													__lowercase      =    tokenizer.batch_decode(
													    hypotheses_batch.tolist()				,							clean_up_tokenization_spaces=lowerCamelCase__				,							skip_special_tokens=lowerCamelCase__				)
													assert generated == expected_en












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 (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

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






						def __init__( self :     str				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     List[Any]=None				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Tuple:
													"""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.'''				,							lowerCamelCase__				,							)
																				__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__(lowerCamelCase__				,							lowerCamelCase__				)






						def __call__( self :     List[Any]				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     str="max_length"				,							lowerCamelCase__ :     Union[str, Any]="np"				,							**lowerCamelCase__ :     Dict				)    ->						Union[str, Any]:
													"""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__				)):
																											__lowercase      =    [self.tokenizer(lowerCamelCase__				,							padding=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)]

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

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

																																		__lowercase      =    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":
																											__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(
																				    lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				).pixel_values
																				__lowercase      =    query_pixel_values

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

													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(**lowerCamelCase__				)				,							tensor_type=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Optional[int]				)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     Tuple				,							**lowerCamelCase__ :     Tuple				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     List[str]				,							**lowerCamelCase__ :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     str				,							*lowerCamelCase__ :     Optional[int]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


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






						@property
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""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     UpperCAmelCase_      ( self :     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






