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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCamelCase_ 						=						logging.get_logger(__name__)

UpperCamelCase_ 						=						{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class    a		( __UpperCAmelCase       ):
							lowercase_		:    int 				=	"perceiver"







							def __init__(    self	:	Dict   ,					snake_case__	:	Union[str, Any]=256   ,					snake_case__	:	List[Any]=1_280   ,					snake_case__	:	Any=768   ,					snake_case__	:	int=1   ,					snake_case__	:	str=26   ,					snake_case__	:	Dict=8   ,					snake_case__	:	List[str]=8   ,					snake_case__	:	List[str]=None   ,					snake_case__	:	List[str]=None   ,					snake_case__	:	Dict="kv"   ,					snake_case__	:	Any=1   ,					snake_case__	:	Optional[Any]=1   ,					snake_case__	:	Union[str, Any]="gelu"   ,					snake_case__	:	List[Any]=0.1   ,					snake_case__	:	List[str]=0.0_2   ,					snake_case__	:	Dict=1E-12   ,					snake_case__	:	str=True   ,					snake_case__	:	int=262   ,					snake_case__	:	Optional[Any]=2_048   ,					snake_case__	:	int=56   ,					snake_case__	:	Union[str, Any]=[368, 496]   ,					snake_case__	:	Union[str, Any]=16   ,					snake_case__	:	str=1_920   ,					snake_case__	:	Any=16   ,					snake_case__	:	Dict=[1, 16, 224, 224]   ,					**snake_case__	:	Dict   ,					):
								"""simple docstring"""







								super().__init__(**snake_case__			)

								__lowerCAmelCase    =				num_latents
								__lowerCAmelCase    =				d_latents
								__lowerCAmelCase    =				d_model
								__lowerCAmelCase    =				num_blocks
								__lowerCAmelCase    =				num_self_attends_per_block
								__lowerCAmelCase    =				num_self_attention_heads
								__lowerCAmelCase    =				num_cross_attention_heads
								__lowerCAmelCase    =				qk_channels
								__lowerCAmelCase    =				v_channels
								__lowerCAmelCase    =				cross_attention_shape_for_attention
								__lowerCAmelCase    =				self_attention_widening_factor
								__lowerCAmelCase    =				cross_attention_widening_factor
								__lowerCAmelCase    =				hidden_act
								__lowerCAmelCase    =				attention_probs_dropout_prob
								__lowerCAmelCase    =				initializer_range
								__lowerCAmelCase    =				layer_norm_eps
								__lowerCAmelCase    =				use_query_residual
								# masked language modeling attributes
								__lowerCAmelCase    =				vocab_size
								__lowerCAmelCase    =				max_position_embeddings
								# image classification attributes
								__lowerCAmelCase    =				image_size
								# flow attributes
								__lowerCAmelCase    =				train_size
								# multimodal autoencoding attributes
								__lowerCAmelCase    =				num_frames
								__lowerCAmelCase    =				audio_samples_per_frame
								__lowerCAmelCase    =				samples_per_patch
								__lowerCAmelCase    =				output_shape

class    a		( __UpperCAmelCase       ):







							@property
							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								if self.task == "multiple-choice":
									__lowerCAmelCase    =				{0: "batch", 1: "choice", 2: "sequence"}
								else:
									__lowerCAmelCase    =				{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("inputs", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]			)







							@property
							def    UpperCAmelCase__       (    self	:	Dict			):
								"""simple docstring"""







								return 1E-4







							def    UpperCAmelCase__       (    self	:	Any   ,					snake_case__	:	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,					snake_case__	:	int = -1   ,					snake_case__	:	int = -1   ,					snake_case__	:	int = -1   ,					snake_case__	:	bool = False   ,					snake_case__	:	Optional[TensorType] = None   ,					snake_case__	:	int = 3   ,					snake_case__	:	int = 40   ,					snake_case__	:	int = 40   ,					):
								"""simple docstring"""







								if isinstance(snake_case__   ,					snake_case__			):
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									__lowerCAmelCase    =				compute_effective_axis_dimension(
									    snake_case__   ,					fixed_dimension=OnnxConfig.default_fixed_batch   ,					num_token_to_add=0			)
									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									__lowerCAmelCase    =				preprocessor.num_special_tokens_to_add(snake_case__			)
									__lowerCAmelCase    =				compute_effective_axis_dimension(
									    snake_case__   ,					fixed_dimension=OnnxConfig.default_fixed_sequence   ,					num_token_to_add=snake_case__			)
									# Generate dummy inputs according to compute batch and sequence
									__lowerCAmelCase    =				[" ".join(["a"]			) * seq_length] * batch_size
									__lowerCAmelCase    =				dict(preprocessor(snake_case__   ,					return_tensors=snake_case__			)			)
									__lowerCAmelCase    =				inputs.pop("input_ids"			)
									return inputs
								elif isinstance(snake_case__   ,					snake_case__			) and preprocessor.model_input_names[0] == "pixel_values":
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									__lowerCAmelCase    =				compute_effective_axis_dimension(snake_case__   ,					fixed_dimension=OnnxConfig.default_fixed_batch			)
									__lowerCAmelCase    =				self._generate_dummy_images(snake_case__   ,					snake_case__   ,					snake_case__   ,					snake_case__			)
									__lowerCAmelCase    =				dict(preprocessor(images=snake_case__   ,					return_tensors=snake_case__			)			)
									__lowerCAmelCase    =				inputs.pop("pixel_values"			)
									return inputs
								else:
									raise ValueError(
									    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."			)










'''simple docstring'''
from typing import Dict, Iterable, Optional, Union

import numpy as np

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


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							return [
							    int(1_0_0_0 * (box[0] / width) ),
							    int(1_0_0_0 * (box[1] / height) ),
							    int(1_0_0_0 * (box[2] / width) ),
							    int(1_0_0_0 * (box[3] / height) ),
							]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		to_pil_image(UpperCamelCase_ )
							A_    ,					A_			    =		pil_image.size
							A_			    =		pytesseract.image_to_data(UpperCamelCase_						,  lang=UpperCamelCase_						,  output_type="dict"						,  config=UpperCamelCase_ )
							A_    ,					A_    ,					A_    ,					A_    ,					A_			    =		data["text"], data["left"], data["top"], data["width"], data["height"]

							# filter empty words and corresponding coordinates
							A_			    =		[idx for idx, word in enumerate(UpperCamelCase_ ) if not word.strip()]
							A_			    =		[word for idx, word in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							A_			    =		[]
							for x, y, w, h in zip(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
														A_			    =		[x, y, x + w, y + h]
														actual_boxes.append(UpperCamelCase_ )

							# finally, normalize the bounding boxes
							A_			    =		[]
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ) )

							assert len(UpperCamelCase_ ) == len(UpperCamelCase_ ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes







class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=["pixel_values"]






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      float = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = "" ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 2_24, "width": 2_24}
													A_			    =		get_size_dict(lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_value
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A_			    =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													A_			    =		apply_ocr
													A_			    =		ocr_lang
													A_			    =		tesseract_config






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		get_size_dict(lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      int ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      float = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      int ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase	)
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		apply_ocr if apply_ocr is not None else self.apply_ocr
													A_			    =		ocr_lang if ocr_lang is not None else self.ocr_lang
													A_			    =		tesseract_config if tesseract_config is not None else self.tesseract_config

													A_			    =		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_normalize and (image_mean is None or image_std is None):
																				raise ValueError("If do_normalize is True, image_mean and image_std must be specified."	)

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

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																				requires_backends(self ,  "pytesseract"	)
																				A_			    =		[]
																				A_			    =		[]
																				for image in images:
																											A_    ,					A_			    =		apply_tesseract(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
																											words_batch.append(lowerCAmelCase	)
																											boxes_batch.append(lowerCAmelCase	)

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

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

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

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													if apply_ocr:
																				A_			    =		words_batch
																				A_			    =		boxes_batch
													return data





