





from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    LayoutLMvaConfig,
						    TFLayoutLMvaForQuestionAnswering,
						    TFLayoutLMvaForSequenceClassification,
						    TFLayoutLMvaForTokenClassification,
						    TFLayoutLMvaModel,
						)

if is_vision_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase=2	,  _lowerCAmelCase=3	,  _lowerCAmelCase=4	,  _lowerCAmelCase=2	,  _lowerCAmelCase=7	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=99	,  _lowerCAmelCase=36	,  _lowerCAmelCase=2	,  _lowerCAmelCase=4	,  _lowerCAmelCase=37	,  _lowerCAmelCase="gelu"	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=512	,  _lowerCAmelCase=16	,  _lowerCAmelCase=2	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=6	,  _lowerCAmelCase=6	,  _lowerCAmelCase=3	,  _lowerCAmelCase=4	,  _lowerCAmelCase=None	,  _lowerCAmelCase=1000	,  ):
										lowerCamelCase__       =       parent
										lowerCamelCase__       =       batch_size
										lowerCamelCase__       =       num_channels
										lowerCamelCase__       =       image_size
										lowerCamelCase__       =       patch_size
										lowerCamelCase__       =       is_training
										lowerCamelCase__       =       use_input_mask
										lowerCamelCase__       =       use_token_type_ids
										lowerCamelCase__       =       use_labels
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       type_sequence_label_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       coordinate_size
										lowerCamelCase__       =       shape_size
										lowerCamelCase__       =       num_labels
										lowerCamelCase__       =       num_choices
										lowerCamelCase__       =       scope
										lowerCamelCase__       =       range_bbox

										# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
										lowerCamelCase__       =       text_seq_length
										lowerCamelCase__       =       (image_size // patch_size) ** 2 + 1
										lowerCamelCase__       =       self.text_seq_length + self.image_seq_length





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       ids_tensor([self.batch_size, self.text_seq_length]	,  self.vocab_size	)

										lowerCamelCase__       =       ids_tensor([self.batch_size, self.text_seq_length, 4]	,  self.range_bbox	)
										lowerCamelCase__       =       bbox.numpy()
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]	):
															for j in range(bbox.shape[1]	):
																				if bbox[i, j, 3] < bbox[i, j, 1]:
																									lowerCamelCase__       =       bbox[i, j, 3]
																									lowerCamelCase__       =       bbox[i, j, 1]
																									lowerCamelCase__       =       tmp_coordinate
																				if bbox[i, j, 2] < bbox[i, j, 0]:
																									lowerCamelCase__       =       bbox[i, j, 2]
																									lowerCamelCase__       =       bbox[i, j, 0]
																									lowerCamelCase__       =       tmp_coordinate
										lowerCamelCase__       =       tf.constant(__UpperCamelCase	)

										lowerCamelCase__       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										lowerCamelCase__       =       None
										if self.use_input_mask:
															lowerCamelCase__       =       random_attention_mask([self.batch_size, self.text_seq_length]	)

										lowerCamelCase__       =       None
										if self.use_token_type_ids:
															lowerCamelCase__       =       ids_tensor([self.batch_size, self.text_seq_length]	,  self.type_vocab_size	)

										lowerCamelCase__       =       None
										lowerCamelCase__       =       None
										if self.use_labels:
															lowerCamelCase__       =       ids_tensor([self.batch_size]	,  self.type_sequence_label_size	)
															lowerCamelCase__       =       ids_tensor([self.batch_size, self.text_seq_length]	,  self.num_labels	)

										lowerCamelCase__       =       LayoutLMvaConfig(
										    vocab_size=self.vocab_size	,  hidden_size=self.hidden_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	,  initializer_range=self.initializer_range	,  coordinate_size=self.coordinate_size	,  shape_size=self.shape_size	,  input_size=self.image_size	,  patch_size=self.patch_size	,  )

										return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       TFLayoutLMvaModel(config=__UpperCamelCase	)

										# text + image
										lowerCamelCase__       =       model(__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  training=__UpperCamelCase	)
										lowerCamelCase__       =       model(
										    __UpperCamelCase	,  bbox=__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  attention_mask=__UpperCamelCase	,  token_type_ids=__UpperCamelCase	,  training=__UpperCamelCase	,  )
										lowerCamelCase__       =       model(__UpperCamelCase	,  bbox=__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  training=__UpperCamelCase	)

										self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)	)

										# text only
										lowerCamelCase__       =       model(__UpperCamelCase	,  training=__UpperCamelCase	)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,  (self.batch_size, self.text_seq_length, self.hidden_size)	)

										# image only
										lowerCamelCase__       =       model({"pixel_values": pixel_values}	,  training=__UpperCamelCase	)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,  (self.batch_size, self.image_seq_length, self.hidden_size)	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.num_labels
										lowerCamelCase__       =       TFLayoutLMvaForSequenceClassification(config=__UpperCamelCase	)
										lowerCamelCase__       =       model(
										    __UpperCamelCase	,  bbox=__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  attention_mask=__UpperCamelCase	,  token_type_ids=__UpperCamelCase	,  labels=__UpperCamelCase	,  training=__UpperCamelCase	,  )
										self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.num_labels
										lowerCamelCase__       =       TFLayoutLMvaForTokenClassification(config=__UpperCamelCase	)
										lowerCamelCase__       =       model(
										    __UpperCamelCase	,  bbox=__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  attention_mask=__UpperCamelCase	,  token_type_ids=__UpperCamelCase	,  labels=__UpperCamelCase	,  training=__UpperCamelCase	,  )
										self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.text_seq_length, self.num_labels)	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       2
										lowerCamelCase__       =       TFLayoutLMvaForQuestionAnswering(config=__UpperCamelCase	)
										lowerCamelCase__       =       model(
										    __UpperCamelCase	,  bbox=__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  attention_mask=__UpperCamelCase	,  token_type_ids=__UpperCamelCase	,  start_positions=__UpperCamelCase	,  end_positions=__UpperCamelCase	,  training=__UpperCamelCase	,  )
										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 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.prepare_config_and_inputs()
										((lowerCamelCase__)    ,		(lowerCamelCase__)    ,		(lowerCamelCase__)    ,		(lowerCamelCase__)    ,		(lowerCamelCase__)    ,		(lowerCamelCase__)    ,		(lowerCamelCase__)    ,		(lowerCamelCase__))       =       config_and_inputs
										lowerCamelCase__       =       {
										    "input_ids": input_ids,
										    "bbox": bbox,
										    "pixel_values": pixel_values,
										    "token_type_ids": token_type_ids,
										    "attention_mask": input_mask,
										}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    (
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					A__				 =    (
					    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					A__				 =    False
					A__				 =    False
					A__				 =    False





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return True





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=False	):
										lowerCamelCase__       =       copy.deepcopy(__UpperCamelCase	)

										if model_class in get_values(__UpperCamelCase	):
															lowerCamelCase__       =       {
															    k: tf.tile(tf.expand_dims(__UpperCamelCase	,  1	)	,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
															    if isinstance(__UpperCamelCase	,  tf.Tensor	) and v.ndim > 0
															    else v
															    for k, v in inputs_dict.items()
															}

										if return_labels:
															if model_class in get_values(__UpperCamelCase	):
																				lowerCamelCase__       =       tf.ones(self.model_tester.batch_size	,  dtype=tf.intaa	)
															elif model_class in get_values(__UpperCamelCase	):
																				lowerCamelCase__       =       tf.zeros(self.model_tester.batch_size	,  dtype=tf.intaa	)
																				lowerCamelCase__       =       tf.zeros(self.model_tester.batch_size	,  dtype=tf.intaa	)
															elif model_class in get_values(__UpperCamelCase	):
																				lowerCamelCase__       =       tf.zeros(self.model_tester.batch_size	,  dtype=tf.intaa	)
															elif model_class in get_values(__UpperCamelCase	):
																				lowerCamelCase__       =       tf.zeros(
																				    (self.model_tester.batch_size, self.model_tester.text_seq_length)	,  dtype=tf.intaa	)

										return inputs_dict





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFLayoutLMvaModelTester(self	)
										lowerCamelCase__       =       ConfigTester(self	,  config_class=__UpperCamelCase	,  hidden_size=37	)





					def 					__magic_name__ ( self	):
										self.config_tester.run_common_tests()





					def 					__magic_name__ ( self	):
										lowerCamelCase__    ,		lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
															lowerCamelCase__       =       model_class(__UpperCamelCase	)
															if getattr(__UpperCamelCase	,  "hf_compute_loss"	,  __UpperCamelCase	):
																				# The number of elements in the loss should be the same as the number of elements in the label
																				lowerCamelCase__       =       self._prepare_for_class(inputs_dict.copy()	,  __UpperCamelCase	,  return_labels=__UpperCamelCase	)
																				lowerCamelCase__       =       prepared_for_class[
																				    sorted(prepared_for_class.keys() - inputs_dict.keys()	,  reverse=__UpperCamelCase	)[0]
																				]
																				lowerCamelCase__       =       added_label.shape.as_list()[:1]

																				# Test that model correctly compute the loss with kwargs
																				lowerCamelCase__       =       self._prepare_for_class(inputs_dict.copy()	,  __UpperCamelCase	,  return_labels=__UpperCamelCase	)
																				lowerCamelCase__       =       prepared_for_class.pop("input_ids"	)

																				lowerCamelCase__       =       model(__UpperCamelCase	,  **__UpperCamelCase	)[0]
																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

																				# Test that model correctly compute the loss when we mask some positions
																				lowerCamelCase__       =       self._prepare_for_class(inputs_dict.copy()	,  __UpperCamelCase	,  return_labels=__UpperCamelCase	)
																				lowerCamelCase__       =       prepared_for_class.pop("input_ids"	)
																				if "labels" in prepared_for_class:
																									lowerCamelCase__       =       prepared_for_class["labels"].numpy()
																									if len(labels.shape	) > 1 and labels.shape[1] != 1:
																														lowerCamelCase__       =       -100
																														lowerCamelCase__       =       tf.convert_to_tensor(__UpperCamelCase	)
																														lowerCamelCase__       =       model(__UpperCamelCase	,  **__UpperCamelCase	)[0]
																														self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)
																														self.assertTrue(not np.any(np.isnan(loss.numpy()	)	)	)

                # Test that model correctly compute the loss with a dict
																				lowerCamelCase__       =       self._prepare_for_class(inputs_dict.copy()	,  __UpperCamelCase	,  return_labels=__UpperCamelCase	)
																				lowerCamelCase__       =       model(__UpperCamelCase	)[0]
																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

																				# Test that model correctly compute the loss with a tuple
																				lowerCamelCase__       =       self._prepare_for_class(inputs_dict.copy()	,  __UpperCamelCase	,  return_labels=__UpperCamelCase	)

																				# Get keys that were added with the _prepare_for_class function
																				lowerCamelCase__       =       prepared_for_class.keys() - inputs_dict.keys()
																				lowerCamelCase__       =       inspect.signature(model.call	).parameters
																				lowerCamelCase__       =       list(signature.keys()	)

																				# Create a dictionary holding the location of the tensors in the tuple
																				lowerCamelCase__       =       {0: "input_ids"}
																				for label_key in label_keys:
																									lowerCamelCase__       =       signature_names.index(__UpperCamelCase	)
																									lowerCamelCase__       =       label_key
																				lowerCamelCase__       =       sorted(tuple_index_mapping.items()	)
																				# Initialize a list with their default values, update the values and convert to a tuple
																				lowerCamelCase__       =       []

																				for name in signature_names:
																									if name != "kwargs":
																														list_input.append(signature[name].default	)

																				for index, value in sorted_tuple_index_mapping:
																									lowerCamelCase__       =       prepared_for_class[value]

																				lowerCamelCase__       =       tuple(__UpperCamelCase	)

																				# Send to model
																				lowerCamelCase__       =       model(tuple_input[:-1]	)[0]

																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)





					def 					__magic_name__ ( self	):
										(
										    (
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,								
										)       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(__UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	)





					def 					__magic_name__ ( self	):
										(
										    (
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,								
										)       =       self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
															lowerCamelCase__       =       type
															self.model_tester.create_and_check_model(__UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	)





					def 					__magic_name__ ( self	):
										(
										    (
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,								
										)       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(
										    __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	)





					def 					__magic_name__ ( self	):
										(
										    (
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,								
										)       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(
										    __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	)





					def 					__magic_name__ ( self	):
										(
										    (
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,		(
										    lowerCamelCase__						
										)    ,								
										)       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(
										    __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	)






					@slow
					def 					__magic_name__ ( self	):
										for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCamelCase__       =       TFLayoutLMvaModel.from_pretrained(__UpperCamelCase	)
															self.assertIsNotNone(__UpperCamelCase	)





def    __UpperCamelCase      (    ) ->Any:
					lowerCamelCase__       =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







@require_tf
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					@cached_property
					def 					__magic_name__ ( self	):
										return LayoutLMvaImageProcessor(apply_ocr=__UpperCamelCase	) if is_vision_available() else None






					@slow
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"	)

										lowerCamelCase__       =       self.default_image_processor
										lowerCamelCase__       =       prepare_img()
										lowerCamelCase__       =       image_processor(images=__UpperCamelCase	,  return_tensors="tf"	).pixel_values

										lowerCamelCase__       =       tf.constant([[1, 2]]	)
										lowerCamelCase__       =       tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]	)	,  axis=0	)

										# forward pass
										lowerCamelCase__       =       model(input_ids=__UpperCamelCase	,  bbox=__UpperCamelCase	,  pixel_values=__UpperCamelCase	,  training=__UpperCamelCase	)

										# verify the logits
										lowerCamelCase__       =       (1, 199, 768)
										self.assertEqual(outputs.last_hidden_state.shape	,  __UpperCamelCase	)

										lowerCamelCase__       =       tf.constant(
										    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]	)

										self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	,  __UpperCamelCase	,  atol=1E-4	)	)








from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

A_												=     logging.get_logger(__name__)





def    __UpperCamelCase      (    a) ->List[List[ImageInput]]:
					if isinstance(a,     (list, tuple)) and isinstance(videos[0],     (list, tuple)) and is_valid_image(videos[0][0]):
										return videos

					elif isinstance(a,     (list, tuple)) and is_valid_image(videos[0]):
										return [videos]

					elif is_valid_image(a):
										return [[videos]]

					raise ValueError(f"Could not make batched video from {videos}")







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    ["pixel_values"]





					def __init__( self	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 1 / 255	,  _lowerCAmelCase = True	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										super().__init__(**_lowerCAmelCase	)
										lowerCamelCase__       =       size if size is not None else {"shortest_edge": 256}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

										lowerCamelCase__       =       do_resize
										lowerCamelCase__       =       size
										lowerCamelCase__       =       do_center_crop
										lowerCamelCase__       =       crop_size
										lowerCamelCase__       =       resample
										lowerCamelCase__       =       do_rescale
										lowerCamelCase__       =       rescale_factor
										lowerCamelCase__       =       offset
										lowerCamelCase__       =       do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCamelCase__       =       image_std if image_std is not None else IMAGENET_STANDARD_STD





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										if "shortest_edge" in size:
															lowerCamelCase__       =       get_resize_output_image_size(_lowerCAmelCase	,  size["shortest_edge"]	,  default_to_square=_lowerCAmelCase	)
										elif "height" in size and "width" in size:
															lowerCamelCase__       =       (size["height"], size["width"])
										else:
															raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"	)
										return resize(_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	)
										if "height" not in size or "width" not in size:
															raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"	)
										return center_crop(_lowerCAmelCase	,  size=(size["height"], size["width"])	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       image.astype(np.floataa	)
										if offset:
															lowerCamelCase__       =       image - (scale / 2)
										return rescale(_lowerCAmelCase	,  scale=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										return normalize(_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  ):
										if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."	)

										if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."	)

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

										if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."	)

										# All transformations expect numpy arrays.
										lowerCamelCase__       =       to_numpy_array(_lowerCAmelCase	)

										if do_resize:
															lowerCamelCase__       =       self.resize(image=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	)

										if do_center_crop:
															lowerCamelCase__       =       self.center_crop(_lowerCAmelCase	,  size=_lowerCAmelCase	)

										if do_rescale:
															lowerCamelCase__       =       self.rescale(image=_lowerCAmelCase	,  scale=_lowerCAmelCase	,  offset=_lowerCAmelCase	)

										if do_normalize:
															lowerCamelCase__       =       self.normalize(image=_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	)

										lowerCamelCase__       =       to_channel_dimension_format(_lowerCAmelCase	,  _lowerCAmelCase	)
										return image






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__       =       resample if resample is not None else self.resample
										lowerCamelCase__       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__       =       do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase__       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase__       =       offset if offset is not None else self.offset
										lowerCamelCase__       =       do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else self.image_mean
										lowerCamelCase__       =       image_std if image_std is not None else self.image_std

										lowerCamelCase__       =       size if size is not None else self.size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

										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."	)

										lowerCamelCase__       =       make_batched(_lowerCAmelCase	)

										lowerCamelCase__       =       [
										    [
										        self._preprocess_image(
										            image=_lowerCAmelCase	,  do_resize=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  do_center_crop=_lowerCAmelCase	,  crop_size=_lowerCAmelCase	,  do_rescale=_lowerCAmelCase	,  rescale_factor=_lowerCAmelCase	,  offset=_lowerCAmelCase	,  do_normalize=_lowerCAmelCase	,  image_mean=_lowerCAmelCase	,  image_std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  )
										        for img in video
										    ]
										    for video in videos
										]

										lowerCamelCase__       =       {"pixel_values": videos}
										return BatchFeature(data=_lowerCAmelCase	,  tensor_type=_lowerCAmelCase	)

