
"""simple docstring"""



import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

UpperCAmelCase__          = random.Random()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       str=1.0 ,					__lowerCamelCase   :       List[str]=None ,					__lowerCamelCase   :       Tuple=None		)     ->					Union[str, Any]:
  if rng is None:
    _snake_case					     =       global_rng

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

  return values

class       lowerCAmelCase__				(						unittest.TestCase	):







     def __init__(							self			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple=7    ,      _lowerCamelCase			:			List[Any]=400    ,      _lowerCamelCase			:			Tuple=2000    ,      _lowerCamelCase			:			Optional[int]=2048    ,      _lowerCamelCase			:			List[str]=128    ,      _lowerCamelCase			:			Optional[Any]=1    ,      _lowerCamelCase			:			int=512    ,      _lowerCamelCase			:			Any=30    ,      _lowerCamelCase			:			int=44100    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       min_seq_length
       _snake_case					     =       max_seq_length
       _snake_case					     =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _snake_case					     =       spectrogram_length
       _snake_case					     =       feature_size
       _snake_case					     =       num_audio_channels
       _snake_case					     =       hop_length
       _snake_case					     =       chunk_length
       _snake_case					     =       sampling_rate







     def      lowercase	(							self			:			Optional[Any]			):
       return {
           "spectrogram_length": self.spectrogram_length,
           "feature_size": self.feature_size,
           "num_audio_channels": self.num_audio_channels,
           "hop_length": self.hop_length,
           "chunk_length": self.chunk_length,
           "sampling_rate": self.sampling_rate,
       }







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Any=False    ,      _lowerCamelCase			:			List[str]=False			):
       def _flatten(_lowerCamelCase			:			Optional[int]			):
         return list(itertools.chain(*_lowerCamelCase			)			)

       if equal_length:
         _snake_case					     =       [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					     =       [
             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					     =       [np.asarray(_lowerCamelCase			) for x in speech_inputs]
       return speech_inputs






@require_torch
@require_torchaudio
class       lowerCAmelCase__				(						__lowerCamelCase ,	unittest.TestCase	):
     __a						=					TvltFeatureExtractor







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       TvltFeatureExtractionTester(self			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_dict			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''spectrogram_length'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''feature_size'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''num_audio_channels'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''hop_length'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''chunk_length'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''sampling_rate'''			)			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_dict			)

       with tempfile.TemporaryDirectory() as tmpdirname:
         _snake_case					     =       feat_extract_first.save_pretrained(_lowerCamelCase			)[0]
         check_json_file_has_correct_format(_lowerCamelCase			)
         _snake_case					     =       self.feature_extraction_class.from_pretrained(_lowerCamelCase			)

       _snake_case					     =       feat_extract_first.to_dict()
       _snake_case					     =       feat_extract_second.to_dict()
       _snake_case					     =       dict_first.pop('''mel_filters'''			)
       _snake_case					     =       dict_second.pop('''mel_filters'''			)
       self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase			)			)
       self.assertEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_dict			)

       with tempfile.TemporaryDirectory() as tmpdirname:
         _snake_case					     =       os.path.join(_lowerCamelCase    ,      '''feat_extract.json'''			)
         feat_extract_first.to_json_file(_lowerCamelCase			)
         _snake_case					     =       self.feature_extraction_class.from_json_file(_lowerCamelCase			)

       _snake_case					     =       feat_extract_first.to_dict()
       _snake_case					     =       feat_extract_second.to_dict()
       _snake_case					     =       dict_first.pop('''mel_filters'''			)
       _snake_case					     =       dict_second.pop('''mel_filters'''			)
       self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase			)			)
       self.assertEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]			):
       # Initialize feature_extractor
       _snake_case					     =       self.feature_extraction_class(**self.feat_extract_dict			)

       # create three inputs of length 800, 1000, and 1200
       _snake_case					     =       [floats_list((1, x)			)[0] for x in range(800    ,      1400    ,      200			)]
       _snake_case					     =       [np.asarray(_lowerCamelCase			) for speech_input in speech_inputs]

       # Test not batched input
       _snake_case					     =       feature_extractor(np_speech_inputs[0]    ,      return_tensors='''np'''    ,      sampling_rate=44100			).audio_values

       self.assertTrue(encoded_audios.ndim == 4			)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

       # Test batched
       _snake_case					     =       feature_extractor(_lowerCamelCase    ,      return_tensors='''np'''    ,      sampling_rate=44100			).audio_values

       self.assertTrue(encoded_audios.ndim == 4			)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

       # Test audio masking
       _snake_case					     =       feature_extractor(
           _lowerCamelCase    ,      return_tensors='''np'''    ,      sampling_rate=44100    ,      mask_audio=_lowerCamelCase			).audio_values

       self.assertTrue(encoded_audios.ndim == 4			)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

       # Test 2-D numpy arrays are batched.
       _snake_case					     =       [floats_list((1, x)			)[0] for x in (800, 800, 800)]
       _snake_case					     =       np.asarray(_lowerCamelCase			)
       _snake_case					     =       feature_extractor(_lowerCamelCase    ,      return_tensors='''np'''    ,      sampling_rate=44100			).audio_values
       self.assertTrue(encoded_audios.ndim == 4			)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       load_dataset('''hf-internal-testing/librispeech_asr_dummy'''    ,      '''clean'''    ,      split='''validation'''			)
       # automatic decoding with librispeech
       _snake_case					     =       ds.sort('''id'''			).select(range(_lowerCamelCase			)			)[:num_samples]['''audio''']

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







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self._load_datasamples(1			)
       _snake_case					     =       TvltFeatureExtractor()
       _snake_case					     =       feature_extractor(_lowerCamelCase    ,      return_tensors='''pt'''			).audio_values

       self.assertEquals(audio_values.shape    ,      (1, 1, 192, 128)			)

       _snake_case					     =       torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]			)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]    ,      _lowerCamelCase    ,      atol=1e-4			)			)





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

UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	return [
	    int(1000 * (box[0] / width)							),
	    int(1000 * (box[1] / height)							),
	    int(1000 * (box[2] / width)							),
	    int(1000 * (box[3] / height)							),
	]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

	return words, normalized_boxes







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    snake_case = None		,    snake_case = ""		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_rescale
		lowercase       =    rescale_value
		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
		lowercase       =    apply_ocr
		lowercase       =    ocr_lang
		lowercase       =    tesseract_config





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		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()}'''       )
		lowercase       =    (size['height'], size['width'])
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case=None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		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       =    apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase       =    ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase       =    tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(snake_case       ):
			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.
		lowercase       =    [to_numpy_array(snake_case       ) for image in images]

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

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

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

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

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

		lowercase       =    BatchFeature(data={'pixel_values': images}		,    tensor_type=snake_case       )

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

