
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    RemBertConfig,
						    RemBertForCausalLM,
						    RemBertForMaskedLM,
						    RemBertForMultipleChoice,
						    RemBertForQuestionAnswering,
						    RemBertForSequenceClassification,
						    RemBertForTokenClassification,
						    RemBertModel,
						)
						from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class      UpperCamelCase_			:







						'''simple docstring'''



						def __init__( self       :int		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Optional[int]=13		,						lowerCAmelCase__       :Optional[int]=7		,						lowerCAmelCase__       :Tuple=True		,						lowerCAmelCase__       :Optional[int]=True		,						lowerCAmelCase__       :Optional[int]=True		,						lowerCAmelCase__       :Dict=True		,						lowerCAmelCase__       :Any=99		,						lowerCAmelCase__       :int=32		,						lowerCAmelCase__       :Any=18		,						lowerCAmelCase__       :List[Any]=43		,						lowerCAmelCase__       :int=5		,						lowerCAmelCase__       :Union[str, Any]=4		,						lowerCAmelCase__       :Optional[Any]=37		,						lowerCAmelCase__       :List[Any]="gelu"		,						lowerCAmelCase__       :List[str]=0.1		,						lowerCAmelCase__       :Dict=0.1		,						lowerCAmelCase__       :Tuple=512		,						lowerCAmelCase__       :Tuple=16		,						lowerCAmelCase__       :Union[str, Any]=2		,						lowerCAmelCase__       :str=0.02		,						lowerCAmelCase__       :str=3		,						lowerCAmelCase__       :Union[str, Any]=4		,						lowerCAmelCase__       :Tuple=None		,						)     ->int:
										lowercase						    =			parent
										lowercase						    =			batch_size
										lowercase						    =			seq_length
										lowercase						    =			is_training
										lowercase						    =			use_input_mask
										lowercase						    =			use_token_type_ids
										lowercase						    =			use_labels
										lowercase						    =			vocab_size
										lowercase						    =			hidden_size
										lowercase						    =			input_embedding_size
										lowercase						    =			output_embedding_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						    =			max_position_embeddings
										lowercase						    =			type_vocab_size
										lowercase						    =			type_sequence_label_size
										lowercase						    =			initializer_range
										lowercase						    =			num_labels
										lowercase						    =			num_choices
										lowercase						    =			scope





						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->List[Any]:
										lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size    )

										lowercase						    =			None
										if self.use_input_mask:
														lowercase						    =			random_attention_mask([self.batch_size, self.seq_length]    )

										lowercase						    =			None
										if self.use_token_type_ids:
														lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size    )

										lowercase						    =			None
										lowercase						    =			None
										lowercase						    =			None
										if self.use_labels:
														lowercase						    =			ids_tensor([self.batch_size]		,						self.type_sequence_label_size    )
														lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						self.num_labels    )
														lowercase						    =			ids_tensor([self.batch_size]		,						self.num_choices    )

										lowercase						    =			RemBertConfig(
										    vocab_size=self.vocab_size		,						hidden_size=self.hidden_size		,						input_embedding_size=self.input_embedding_size		,						output_embedding_size=self.output_embedding_size		,						num_hidden_layers=self.num_hidden_layers		,						num_attention_heads=self.num_attention_heads		,						intermediate_size=self.intermediate_size		,						hidden_act=self.hidden_act		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						max_position_embeddings=self.max_position_embeddings		,						type_vocab_size=self.type_vocab_size		,						is_decoder=lowerCAmelCase__		,						initializer_range=self.initializer_range		,						)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->List[Any]:
										(
										    (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,    
										)						    =			self.prepare_config_and_inputs()

										lowercase						    =			True
										lowercase						    =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
										lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						vocab_size=2    )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Tuple    )     ->str:
										lowercase						    =			RemBertModel(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__    )
										lowercase						    =			model(lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__    )
										lowercase						    =			model(lowerCAmelCase__    )
										self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)    )





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Tuple		,						)     ->str:
										lowercase						    =			True
										lowercase						    =			RemBertModel(lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						encoder_hidden_states=lowerCAmelCase__		,						encoder_attention_mask=lowerCAmelCase__		,						)
										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						encoder_hidden_states=lowerCAmelCase__		,						)
										lowercase						    =			model(lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__    )
										self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :Optional[int]		,						)     ->List[Any]:
										lowercase						    =			RemBertForCausalLM(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						labels=lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size)    )





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :List[str]    )     ->Optional[Any]:
										lowercase						    =			RemBertForMaskedLM(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						labels=lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size)    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Dict		,						)     ->str:
										lowercase						    =			True
										lowercase						    =			True
										lowercase						    =			RemBertForCausalLM(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()

										# first forward pass
										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						encoder_hidden_states=lowerCAmelCase__		,						encoder_attention_mask=lowerCAmelCase__		,						use_cache=lowerCAmelCase__		,						)
										lowercase						    =			outputs.past_key_values

										# 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)		,						vocab_size=2    )

										# append to next input_ids and
										lowercase						    =			torch.cat([input_ids, next_tokens]		,						dim=-1    )
										lowercase						    =			torch.cat([input_mask, next_mask]		,						dim=-1    )

										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						encoder_hidden_states=lowerCAmelCase__		,						encoder_attention_mask=lowerCAmelCase__		,						output_hidden_states=lowerCAmelCase__		,						)["hidden_states"][0]
										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						encoder_hidden_states=lowerCAmelCase__		,						encoder_attention_mask=lowerCAmelCase__		,						past_key_values=lowerCAmelCase__		,						output_hidden_states=lowerCAmelCase__		,						)["hidden_states"][0]

										# 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-3    )    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Dict    )     ->List[Any]:
										lowercase						    =			RemBertForQuestionAnswering(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						start_positions=lowerCAmelCase__		,						end_positions=lowerCAmelCase__		,						)
										self.parent.assertEqual(result.start_logits.shape		,						(self.batch_size, self.seq_length)    )
										self.parent.assertEqual(result.end_logits.shape		,						(self.batch_size, self.seq_length)    )





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Dict    )     ->List[str]:
										lowercase						    =			self.num_labels
										lowercase						    =			RemBertForSequenceClassification(lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						labels=lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :List[str]    )     ->List[str]:
										lowercase						    =			self.num_labels
										lowercase						    =			RemBertForTokenClassification(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			model(lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						labels=lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.num_labels)    )





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :Tuple    )     ->Tuple:
										lowercase						    =			self.num_choices
										lowercase						    =			RemBertForMultipleChoice(config=lowerCAmelCase__    )
										model.to(lowerCAmelCase__    )
										model.eval()
										lowercase						    =			input_ids.unsqueeze(1    ).expand(-1		,						self.num_choices		,						-1    ).contiguous()
										lowercase						    =			token_type_ids.unsqueeze(1    ).expand(-1		,						self.num_choices		,						-1    ).contiguous()
										lowercase						    =			input_mask.unsqueeze(1    ).expand(-1		,						self.num_choices		,						-1    ).contiguous()
										lowercase						    =			model(
										    lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						labels=lowerCAmelCase__		,						)
										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices)    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[int]    )     ->Any:
										lowercase						    =			self.prepare_config_and_inputs()
										(
										    (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,    
										)						    =			config_and_inputs
										lowercase						    =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict





@require_torch
class      UpperCamelCase_			(			__a		,						__a		,						unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase			:     int							      =				(
						    (
						        RemBertModel,
						        RemBertForMaskedLM,
						        RemBertForCausalLM,
						        RemBertForMultipleChoice,
						        RemBertForQuestionAnswering,
						        RemBertForSequenceClassification,
						        RemBertForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase			:     Optional[Any]							      =				(RemBertForCausalLM,) if is_torch_available() else ()
						UpperCamelCase			:     int							      =				(
						    {
						        '''feature-extraction''': RemBertModel,
						        '''fill-mask''': RemBertForMaskedLM,
						        '''question-answering''': RemBertForQuestionAnswering,
						        '''text-classification''': RemBertForSequenceClassification,
						        '''text-generation''': RemBertForCausalLM,
						        '''token-classification''': RemBertForTokenClassification,
						        '''zero-shot''': RemBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->str:
										lowercase						    =			RemBertModelTester(self    )
										lowercase						    =			ConfigTester(self		,						config_class=lowerCAmelCase__		,						hidden_size=37    )





						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->Dict:
										self.config_tester.run_common_tests()





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]    )     ->Tuple:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]    )     ->List[str]:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														lowercase						    =			type
														self.model_tester.create_and_check_model(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Union[str, Any]:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]    )     ->Any:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->str:
										lowercase						    =			self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :str    )     ->Tuple:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->Union[str, Any]:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]    )     ->str:
										lowercase						    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple    )     ->str:
										lowercase						    =			self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->List[str]:
										# This regression test was failing with PyTorch < 1.3
										(
										    (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,  (
										    lowercase  
										)    ,    
										)						    =			self.model_tester.prepare_config_and_inputs_for_decoder()

										lowercase						    =			None

										self.model_tester.create_and_check_model_as_decoder(
										    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						)





						@slow
						def 					SCREAMING_SNAKE_CASE( self       :Tuple    )     ->Dict:
										for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase						    =			RemBertModel.from_pretrained(lowerCAmelCase__    )
														self.assertIsNotNone(lowerCAmelCase__    )





@require_torch
class      UpperCamelCase_			(			unittest.TestCase		):







						'''simple docstring'''



						@slow
						def 					SCREAMING_SNAKE_CASE( self       :Optional[int]    )     ->Optional[int]:
										# Test exact values at the last hidden layer
										lowercase						    =			RemBertModel.from_pretrained("google/rembert"    )
										lowercase						    =			torch.tensor([[312, 56498, 313, 2125, 313]]    )
										lowercase						    =			torch.tensor([[0, 0, 0, 1, 1]]    )
										with torch.no_grad():
														lowercase						    =			model(lowerCAmelCase__		,						token_type_ids=lowerCAmelCase__		,						output_hidden_states=lowerCAmelCase__    )

										lowercase						    =			1152

										lowercase						    =			torch.Size((1, 5, hidden_size)    )
										self.assertEqual(output["last_hidden_state"].shape		,						lowerCAmelCase__    )

										lowercase						    =			torch.tensor(
										    [
										        [
										            [0.07_54, -0.20_22, 0.19_04],
										            [-0.33_54, -0.36_92, -0.47_91],
										            [-0.23_14, -0.67_29, -0.07_49],
										            [-0.03_96, -0.31_05, -0.42_34],
										            [-0.15_71, -0.05_25, 0.53_53],
										        ]
										    ]    )

										# Running on the original tf implementation gives slightly different results here.
										# Not clear why this variations is present
										# TODO: Find reason for discrepancy
										# expected_original_implementation = [[
										#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
										#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
										#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
										#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
										#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
										# ]]

										self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]		,						lowerCAmelCase__		,						atol=1E-4    )    )


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case			:       Tuple      =						logging.get_logger(__name__)


if is_vision_available():
						import PIL





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']





						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
										super().__init__(**lowerCAmelCase__    )

										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# The previous feature extractor size parameter was in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )

										lowercase						    =			do_resize
										lowercase						    =			size
										lowercase						    =			resample
										lowercase						    =			do_thumbnail
										lowercase						    =			do_align_long_axis
										lowercase						    =			do_pad
										lowercase						    =			do_rescale
										lowercase						    =			rescale_factor
										lowercase						    =			do_normalize
										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )

										if data_format is not None:
														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )

										return image





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			size["height"], size["width"]
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )

										lowercase						    =			output_width - input_width
										lowercase						    =			output_height - input_height

										if random_padding:
														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
										else:
														lowercase						    =			delta_height // 2
														lowercase						    =			delta_width // 2

										lowercase						    =			delta_height - pad_top
										lowercase						    =			delta_width - pad_left

										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														lowercase						    =			int(input_width * height / input_height    )
										elif input_width > input_height:
														lowercase						    =			int(input_height * width / input_width    )

										return resize(
										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			min(size["height"]		,						size["width"]    )
										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
										return resized_image





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[int, float]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[Any]		,						)     ->List[Any]:
										return rescale(lowerCAmelCase__		,						scale=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :str		,						)     ->np.ndarray:
										return normalize(lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
										lowercase						    =			do_resize if do_resize is not None else self.do_resize
										lowercase						    =			size if size is not None else self.size
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# Previous feature extractor had size in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			resample if resample is not None else self.resample
										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										lowercase						    =			do_pad if do_pad is not None else self.do_pad
										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
										lowercase						    =			image_mean if image_mean is not None else self.image_mean
										lowercase						    =			image_std if image_std is not None else self.image_std

										lowercase						    =			make_list_of_images(lowerCAmelCase__    )

										if not valid_images(lowerCAmelCase__    ):
														raise ValueError(
														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
														    "torch.Tensor, tf.Tensor or jax.ndarray."    )

										if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize is True."    )

										if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."    )

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad is True."    )

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and std must be specified if do_normalize is True."    )

										# All transformations expect numpy arrays.
										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]

										if do_align_long_axis:
														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_resize:
														lowercase						    =			[self.resize(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__    ) for image in images]

										if do_thumbnail:
														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_pad:
														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]

										if do_rescale:
														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]

										if do_normalize:
														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]

										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]

										lowercase						    =			{"pixel_values": images}
										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
