





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor

_SCREAMING_SNAKE_CASE      	=		random.Random()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=1.0   , __a=None   , __a=None   ):
	if rng is None:
		snake_case_		:			Dict											=	global_rng

	snake_case_		:			str											=	[]
	for batch_idx in range(shape[0]   ):
		values.append([]   )
		for _ in range(shape[1]   ):
			values[-1].append(rng.random() * scale   )

	return values
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def __init__(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]=7		,   _A      :      int=400		,   _A      :      Optional[Any]=2000		,   _A      :      Any=24		,   _A      :      int=24		,   _A      :      List[Any]=0.0		,   _A      :      str=16000		,   _A      :      Dict=True		,   _A      :      Optional[int]=True		,   )							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	parent
							snake_case_		:			int											=	batch_size
							snake_case_		:			str											=	min_seq_length
							snake_case_		:			int											=	max_seq_length
							snake_case_		:			Optional[Any]											=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							snake_case_		:			Optional[Any]											=	feature_size
							snake_case_		:			int											=	num_mel_bins
							snake_case_		:			Union[str, Any]											=	padding_value
							snake_case_		:			List[Any]											=	sampling_rate
							snake_case_		:			Any											=	return_attention_mask
							snake_case_		:			Tuple											=	do_normalize






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







							"""simple docstring"""


							return {
							    "feature_size": self.feature_size,
							    "num_mel_bins": self.num_mel_bins,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}






						def UpperCAmelCase_				(      self      :      int		,   _A      :      int=False		,   _A      :      Union[str, Any]=False	)							->    List[str]:







							"""simple docstring"""


							def _flatten(_A      :      str	):
								return list(itertools.chain(*_A	)	)

							if equal_length:
								snake_case_		:			Union[str, Any]											=	[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
							else:
								# make sure that inputs increase in size
								snake_case_		:			Any											=	[
								    floats_list((x, self.feature_size)	)
								    for x in range(self.min_seq_length		,   self.max_seq_length		,   self.seq_length_diff	)
								]
							if numpify:
								snake_case_		:			int											=	[np.asarray(_A	) for x in speech_inputs]
							return speech_inputs
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			int       =       SpeechaTextFeatureExtractor if is_speech_available() else None






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







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	SpeechaTextFeatureExtractionTester(self	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Optional[Any]	)							->    List[str]:







							"""simple docstring"""


							self.assertTrue(np.all(np.mean(_A		,   axis=0	) < 1E-3	)	)
							self.assertTrue(np.all(np.abs(np.var(_A		,   axis=0	) - 1	) < 1E-3	)	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							# create three inputs of length 800, 1000, and 1200
							snake_case_		:			Union[str, Any]											=	[floats_list((1, x)	)[0] for x in range(800		,   1400		,   200	)]
							snake_case_		:			Optional[int]											=	[np.asarray(_A	) for speech_input in speech_inputs]

							# Test feature size
							snake_case_		:			int											=	feature_extractor(_A		,   padding=_A		,   return_tensors='np'	).input_features
							self.assertTrue(input_features.ndim == 3	)
							self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

							# Test not batched input
							snake_case_		:			Union[str, Any]											=	feature_extractor(speech_inputs[0]		,   return_tensors='np'	).input_features
							snake_case_		:			int											=	feature_extractor(np_speech_inputs[0]		,   return_tensors='np'	).input_features
							self.assertTrue(np.allclose(_A		,   _A		,   atol=1E-3	)	)

							# Test batched
							snake_case_		:			Dict											=	feature_extractor(_A		,   return_tensors='np'	).input_features
							snake_case_		:			Optional[int]											=	feature_extractor(_A		,   return_tensors='np'	).input_features
							for enc_seq_a, enc_seq_a in zip(_A		,   _A	):
								self.assertTrue(np.allclose(_A		,   _A		,   atol=1E-3	)	)

							# Test 2-D numpy arrays are batched.
							snake_case_		:			Optional[int]											=	[floats_list((1, x)	)[0] for x in (800, 800, 800)]
							snake_case_		:			Union[str, Any]											=	np.asarray(_A	)
							snake_case_		:			Tuple											=	feature_extractor(_A		,   return_tensors='np'	).input_features
							snake_case_		:			List[str]											=	feature_extractor(_A		,   return_tensors='np'	).input_features
							for enc_seq_a, enc_seq_a in zip(_A		,   _A	):
								self.assertTrue(np.allclose(_A		,   _A		,   atol=1E-3	)	)






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







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							snake_case_		:			Any											=	[floats_list((1, x)	)[0] for x in range(800		,   1400		,   200	)]

							snake_case_		:			Optional[Any]											=	['longest', 'max_length', 'do_not_pad']
							snake_case_		:			Union[str, Any]											=	[None, 16, None]
							for max_length, padding in zip(_A		,   _A	):
								snake_case_		:			Tuple											=	feature_extractor(
								    _A		,   padding=_A		,   max_length=_A		,   return_attention_mask=_A	)
								snake_case_		:			List[str]											=	inputs.input_features
								snake_case_		:			str											=	inputs.attention_mask
								snake_case_		:			Dict											=	[np.sum(_A	) for x in attention_mask]

								self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
								self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
								self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							snake_case_		:			str											=	[floats_list((1, x)	)[0] for x in range(800		,   1400		,   200	)]

							snake_case_		:			int											=	['longest', 'max_length', 'do_not_pad']
							snake_case_		:			Tuple											=	[None, 16, None]
							for max_length, padding in zip(_A		,   _A	):
								snake_case_		:			List[str]											=	feature_extractor(
								    _A		,   max_length=_A		,   padding=_A		,   return_tensors='np'		,   return_attention_mask=_A	)
								snake_case_		:			int											=	inputs.input_features
								snake_case_		:			Tuple											=	inputs.attention_mask
								snake_case_		:			Optional[int]											=	[np.sum(_A	) for x in attention_mask]

								self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
								self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6	)
								self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
								self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6	)
								self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    int:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							snake_case_		:			Optional[Any]											=	[floats_list((1, x)	)[0] for x in range(800		,   1400		,   200	)]
							snake_case_		:			List[str]											=	feature_extractor(
							    _A		,   padding='max_length'		,   max_length=4		,   truncation=_A		,   return_tensors='np'		,   return_attention_mask=_A		,   )
							snake_case_		:			Optional[Any]											=	inputs.input_features
							snake_case_		:			List[Any]											=	inputs.attention_mask
							snake_case_		:			List[str]											=	np.sum(attention_mask == 1		,   axis=1	)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
							self._check_zero_mean_unit_variance(input_features[1]	)
							self._check_zero_mean_unit_variance(input_features[2]	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							snake_case_		:			Tuple											=	[floats_list((1, x)	)[0] for x in range(800		,   1400		,   200	)]
							snake_case_		:			Dict											=	feature_extractor(
							    _A		,   padding='longest'		,   max_length=4		,   truncation=_A		,   return_tensors='np'		,   return_attention_mask=_A		,   )
							snake_case_		:			str											=	inputs.input_features
							snake_case_		:			Optional[Any]											=	inputs.attention_mask
							snake_case_		:			Tuple											=	np.sum(attention_mask == 1		,   axis=1	)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
							self._check_zero_mean_unit_variance(input_features[2]	)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape		,   (3, 4, 24)	)

							snake_case_		:			List[str]											=	[floats_list((1, x)	)[0] for x in range(800		,   1400		,   200	)]
							snake_case_		:			Optional[Any]											=	feature_extractor(
							    _A		,   padding='longest'		,   max_length=16		,   truncation=_A		,   return_tensors='np'		,   return_attention_mask=_A		,   )
							snake_case_		:			int											=	inputs.input_features
							snake_case_		:			List[Any]											=	inputs.attention_mask
							snake_case_		:			str											=	np.sum(attention_mask == 1		,   axis=1	)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
							self._check_zero_mean_unit_variance(input_features[2]	)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape		,   (3, 6, 24)	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							import torch

							snake_case_		:			List[Any]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							snake_case_		:			Union[str, Any]											=	np.random.rand(100		,   32	).astype(np.floataa	)
							snake_case_		:			List[Any]											=	np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
								snake_case_		:			Union[str, Any]											=	feature_extractor.pad([{'input_features': inputs}]		,   return_tensors='np'	)
								self.assertTrue(np_processed.input_features.dtype == np.floataa	)
								snake_case_		:			Optional[int]											=	feature_extractor.pad([{'input_features': inputs}]		,   return_tensors='pt'	)
								self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Tuple	)							->    int:







							"""simple docstring"""


							from datasets import load_dataset

							snake_case_		:			str											=	load_dataset('hf-internal-testing/librispeech_asr_dummy'		,   'clean'		,   split='validation'	)
							# automatic decoding with librispeech
							snake_case_		:			str											=	ds.sort('id'	).select(range(_A	)	)[:num_samples]['audio']

							return [x["array"] for x in speech_samples]






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			List[str]											=	np.array([
							    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
							    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
							    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
							]	)
							# fmt: on

							snake_case_		:			Dict											=	self._load_datasamples(1	)
							snake_case_		:			Any											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
							snake_case_		:			int											=	feature_extractor(_A		,   return_tensors='pt'	).input_features
							self.assertEquals(input_features.shape		,   (1, 584, 24)	)
							self.assertTrue(np.allclose(input_features[0, 0, :30]		,   _A		,   atol=1E-4	)	)








from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	return [
	    int(10_00 * (box[0] / width)   ),
	    int(10_00 * (box[1] / height)   ),
	    int(10_00 * (box[2] / width)   ),
	    int(10_00 * (box[3] / height)   ),
	]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a = None   ):
	snake_case_		:			int											=	tesseract_config if tesseract_config is not None else ''

	# apply OCR
	snake_case_		:			str											=	to_pil_image(__a   )
	snake_case_						,snake_case_		:			str											=	pil_image.size
	snake_case_		:			str											=	pytesseract.image_to_data(__a   , lang=__a   , output_type='dict'   , config=__a   )
	snake_case_						,snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Dict											=	data['text'], data['left'], data['top'], data['width'], data['height']

	# filter empty words and corresponding coordinates
	snake_case_		:			str											=	[idx for idx, word in enumerate(__a   ) if not word.strip()]
	snake_case_		:			Optional[Any]											=	[word for idx, word in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			Any											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			Any											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			List[Any]											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			int											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]

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

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

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

	return words, normalized_boxes


class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       ["pixel_values"]






						def __init__(      self      :      List[str]		,   _A      :      bool = True		,   _A      :      Dict[str, int] = None		,   _A      :      PILImageResampling = PILImageResampling.BILINEAR		,   _A      :      bool = True		,   _A      :      Optional[str] = None		,   _A      :      Optional[str] = ""		,   **_A      :      int		,   )							->    None:







							"""simple docstring"""


							super().__init__(**_A	)
							snake_case_		:			Optional[int]											=	size if size is not None else {'height': 224, 'width': 224}
							snake_case_		:			List[str]											=	get_size_dict(_A	)

							snake_case_		:			int											=	do_resize
							snake_case_		:			int											=	size
							snake_case_		:			Any											=	resample
							snake_case_		:			List[str]											=	apply_ocr
							snake_case_		:			str											=	ocr_lang
							snake_case_		:			Dict											=	tesseract_config






						def UpperCAmelCase_				(      self      :      int		,   _A      :      np.ndarray		,   _A      :      Dict[str, int]		,   _A      :      PILImageResampling = PILImageResampling.BILINEAR		,   _A      :      Optional[Union[str, ChannelDimension]] = None		,   **_A      :      str		,   )							->    np.ndarray:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	get_size_dict(_A	)
							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()}"""	)
							snake_case_		:			Optional[Any]											=	(size['height'], size['width'])
							return resize(_A		,   size=_A		,   resample=_A		,   data_format=_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      ImageInput		,   _A      :      bool = None		,   _A      :      Dict[str, int] = None		,   _A      :      PILImageResampling = None		,   _A      :      bool = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[Union[str, TensorType]] = None		,   _A      :      ChannelDimension = ChannelDimension.FIRST		,   **_A      :      List[str]		,   )							->    PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:			Dict											=	do_resize if do_resize is not None else self.do_resize
							snake_case_		:			Optional[Any]											=	size if size is not None else self.size
							snake_case_		:			Union[str, Any]											=	get_size_dict(_A	)
							snake_case_		:			Dict											=	resample if resample is not None else self.resample
							snake_case_		:			Tuple											=	apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case_		:			int											=	ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case_		:			int											=	tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case_		:			str											=	make_list_of_images(_A	)

							if not valid_images(_A	):
								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.'	)

							# All transformations expect numpy arrays.
							snake_case_		:			List[str]											=	[to_numpy_array(_A	) for image in images]

							if apply_ocr:
								requires_backends(self		,   'pytesseract'	)
								snake_case_		:			str											=	[]
								snake_case_		:			List[Any]											=	[]
								for image in images:
									snake_case_						,snake_case_		:			Tuple											=	apply_tesseract(_A		,   _A		,   _A	)
									words_batch.append(_A	)
									boxes_batch.append(_A	)

							if do_resize:
								snake_case_		:			Tuple											=	[self.resize(image=_A		,   size=_A		,   resample=_A	) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							snake_case_		:			int											=	[flip_channel_order(_A	) for image in images]
							snake_case_		:			Any											=	[to_channel_dimension_format(_A		,   _A	) for image in images]

							snake_case_		:			List[str]											=	BatchFeature(data={'pixel_values': images}		,   tensor_type=_A	)

							if apply_ocr:
								snake_case_		:			Dict											=	words_batch
								snake_case_		:			List[str]											=	boxes_batch
							return data

