






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

__snake_case    :    Union[str, Any]         =     random.Random()







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					int=1.0    ,       UpperCamelCase_       :					Any=None    ,       UpperCamelCase_       :					List[str]=None		)  ->   List[Any]:

						"""simple docstring"""



						if rng is None:
												lowerCAmelCase__    						=						global_rng

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

						return values


class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=7     ,						_UpperCamelCase=4_00     ,						_UpperCamelCase=20_00     ,						_UpperCamelCase=20_48     ,						_UpperCamelCase=1_28     ,						_UpperCamelCase=1     ,						_UpperCamelCase=5_12     ,						_UpperCamelCase=30     ,						_UpperCamelCase=4_41_00     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						parent
												lowerCAmelCase__    						=						batch_size
												lowerCAmelCase__    						=						min_seq_length
												lowerCAmelCase__    						=						max_seq_length
												lowerCAmelCase__    						=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCAmelCase__    						=						spectrogram_length
												lowerCAmelCase__    						=						feature_size
												lowerCAmelCase__    						=						num_audio_channels
												lowerCAmelCase__    						=						hop_length
												lowerCAmelCase__    						=						chunk_length
												lowerCAmelCase__    						=						sampling_rate






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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       UpperCamelCase__      (	self     ,						_UpperCamelCase=False     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												def _flatten(_UpperCamelCase   ):
																		return list(itertools.chain(*_UpperCamelCase   )   )

												if equal_length:
																		lowerCAmelCase__    						=						[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
												else:
																		# make sure that inputs increase in size
																		lowerCAmelCase__    						=						[
																		    floats_list((x, self.feature_size)   )
																		    for x in range(self.min_seq_length     ,						self.max_seq_length     ,						self.seq_length_diff   )
																		]
												if numpify:
																		lowerCAmelCase__    						=						[np.asarray(_UpperCamelCase   ) for x in speech_inputs]
												return speech_inputs




@require_torch
@require_torchaudio
class     __SCREAMING_SNAKE_CASE					(   __lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					TvltFeatureExtractor






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						TvltFeatureExtractionTester(self   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_dict   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'spectrogram_length'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'feature_size'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'num_audio_channels'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'hop_length'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'chunk_length'   )   )
												self.assertTrue(hasattr(_UpperCamelCase     ,						'sampling_rate'   )   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_dict   )

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

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_dict   )

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

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Initialize feature_extractor
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_dict   )

												# create three inputs of length 800, 1000, and 1200
												lowerCAmelCase__    						=						[floats_list((1, x)   )[0] for x in range(8_00     ,						14_00     ,						2_00   )]
												lowerCAmelCase__    						=						[np.asarray(_UpperCamelCase   ) for speech_input in speech_inputs]

												# Test not batched input
												lowerCAmelCase__    						=						feature_extractor(np_speech_inputs[0]     ,						return_tensors='np'     ,						sampling_rate=4_41_00   ).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
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'     ,						sampling_rate=4_41_00   ).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
												lowerCAmelCase__    						=						feature_extractor(
												    _UpperCamelCase     ,						return_tensors='np'     ,						sampling_rate=4_41_00     ,						mask_audio=_UpperCamelCase   ).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.
												lowerCAmelCase__    						=						[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
												lowerCAmelCase__    						=						np.asarray(_UpperCamelCase   )
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'     ,						sampling_rate=4_41_00   ).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       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,						'clean'     ,						split='validation'   )
												# automatic decoding with librispeech
												lowerCAmelCase__    						=						ds.sort('id'   ).select(range(_UpperCamelCase   )   )[:num_samples]['audio']

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







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._load_datasamples(1   )
												lowerCAmelCase__    						=						TvltFeatureExtractor()
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='pt'   ).audio_values

												self.assertEquals(audio_values.shape     ,						(1, 1, 1_92, 1_28)   )

												lowerCAmelCase__    						=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]   )
												self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,						_UpperCamelCase     ,						atol=1E-4   )   )













import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(F"{file_root}.jpg"    ,       UpperCamelCase_    ,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
												print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						F"{anno[0]} {x_center} {y_center} {width} {height}"
																		annos_list.append(UpperCamelCase_		)
												with open(F"{file_root}.txt"    ,       'w'		) as outfile:
																		outfile.write('\n'.join(line for line in annos_list		)		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) + float(obj[4]		) / 2

																		boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
												if not boxes:
																		continue
												img_paths.append(UpperCamelCase_		)
												labels.append(UpperCamelCase_		)
						return img_paths, labels







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for i, index in enumerate(UpperCamelCase_		):
												lowerCAmelCase__    						=						all_img_list[index]
												path_list.append(UpperCamelCase_		)
												lowerCAmelCase__    						=						all_annos[index]
												lowerCAmelCase__    						=						cva.imread(UpperCamelCase_		)
												if i == 0:  # top-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 1:  # top-right
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (output_size[1] - divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 2:  # bottom-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												else:  # bottom-right
																		lowerCAmelCase__    						=						cva.resize(
																		    UpperCamelCase_    ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowerCAmelCase__    						=						[
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   str:

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")





