




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

_lowerCAmelCase						:   Dict		=   random.Random()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[Any]=1.0						,							_lowerCAmelCase							:   Dict=None						,							_lowerCAmelCase							:   Any=None	):




 """simple docstring"""







 if rng is None:
  UpperCAmelCase__							 =						global_rng

 UpperCAmelCase__							 =						[]
 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 			_UpperCamelCase      (				unittest.TestCase		):
      def __init__(			self   :List[Any] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :int=7 ,      lowerCamelCase   :Any=400 ,      lowerCamelCase   :Optional[int]=2000 ,      lowerCamelCase   :Tuple=24 ,      lowerCamelCase   :Optional[int]=24 ,      lowerCamelCase   :int=0.0 ,      lowerCamelCase   :Tuple=1_6000 ,      lowerCamelCase   :int=True ,      lowerCamelCase   :Any=True ,      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						min_seq_length
       UpperCAmelCase__							 =						max_seq_length
       UpperCAmelCase__							 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCAmelCase__							 =						feature_size
       UpperCAmelCase__							 =						num_mel_bins
       UpperCAmelCase__							 =						padding_value
       UpperCAmelCase__							 =						sampling_rate
       UpperCAmelCase__							 =						return_attention_mask
       UpperCAmelCase__							 =						do_normalize
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Optional[Any]:
       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   :Optional[Any] ,      lowerCamelCase   :Dict=False ,      lowerCamelCase   :List[str]=False      )     ->			Optional[Any]:
       def _flatten(lowerCamelCase   :Optional[Any]      ):
        return list(itertools.chain(*lowerCamelCase      )      )

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



@require_torch
@require_torchaudio
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		SpeechaTextFeatureExtractor if is_speech_available() else None
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			int:
       UpperCAmelCase__							 =						SpeechaTextFeatureExtractionTester(self      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Optional[Any]      )     ->			Optional[int]:
       self.assertTrue(np.all(np.mean(lowerCamelCase ,      axis=0      ) < 1e-3      )      )
       self.assertTrue(np.all(np.abs(np.var(lowerCamelCase ,      axis=0      ) - 1      ) < 1e-3      )      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Union[str, Any]:
       # Tests that all call wrap to encode_plus and batch_encode_plus
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       # create three inputs of length 800, 1000, and 1200
       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in range(800 ,      1400 ,      200      )]
       UpperCAmelCase__							 =						[np.asarray(lowerCamelCase      ) for speech_input in speech_inputs]

       # Test feature size
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      padding=lowerCamelCase ,      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
       UpperCAmelCase__							 =						feature_extractor(speech_inputs[0] ,      return_tensors="np"      ).input_features
       UpperCAmelCase__							 =						feature_extractor(np_speech_inputs[0] ,      return_tensors="np"      ).input_features
       self.assertTrue(np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3      )      )

       # Test batched
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      return_tensors="np"      ).input_features
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      return_tensors="np"      ).input_features
       for enc_seq_a, enc_seq_a in zip(lowerCamelCase ,      lowerCamelCase      ):
        self.assertTrue(np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3      )      )

       # Test 2-D numpy arrays are batched.
       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in (800, 800, 800)]
       UpperCAmelCase__							 =						np.asarray(lowerCamelCase      )
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      return_tensors="np"      ).input_features
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      return_tensors="np"      ).input_features
       for enc_seq_a, enc_seq_a in zip(lowerCamelCase ,      lowerCamelCase      ):
        self.assertTrue(np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3      )      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[str]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in range(800 ,      1400 ,      200      )]

       UpperCAmelCase__							 =						["longest", "max_length", "do_not_pad"]
       UpperCAmelCase__							 =						[None, 16, None]
       for max_length, padding in zip(lowerCamelCase ,      lowerCamelCase      ):
        UpperCAmelCase__							 =						feature_extractor(
            lowerCamelCase ,      padding=lowerCamelCase ,      max_length=lowerCamelCase ,      return_attention_mask=lowerCamelCase      )
        UpperCAmelCase__							 =						inputs.input_features
        UpperCAmelCase__							 =						inputs.attention_mask
        UpperCAmelCase__							 =						[np.sum(lowerCamelCase      ) 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   :str      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in range(800 ,      1400 ,      200      )]

       UpperCAmelCase__							 =						["longest", "max_length", "do_not_pad"]
       UpperCAmelCase__							 =						[None, 16, None]
       for max_length, padding in zip(lowerCamelCase ,      lowerCamelCase      ):
        UpperCAmelCase__							 =						feature_extractor(
            lowerCamelCase ,      max_length=lowerCamelCase ,      padding=lowerCamelCase ,      return_tensors="np" ,      return_attention_mask=lowerCamelCase      )
        UpperCAmelCase__							 =						inputs.input_features
        UpperCAmelCase__							 =						inputs.attention_mask
        UpperCAmelCase__							 =						[np.sum(lowerCamelCase      ) 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   :Dict      )     ->			List[str]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in range(800 ,      1400 ,      200      )]
       UpperCAmelCase__							 =						feature_extractor(
           lowerCamelCase ,      padding="max_length" ,      max_length=4 ,      truncation=lowerCamelCase ,      return_tensors="np" ,      return_attention_mask=lowerCamelCase ,      )
       UpperCAmelCase__							 =						inputs.input_features
       UpperCAmelCase__							 =						inputs.attention_mask
       UpperCAmelCase__							 =						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   :Union[str, Any]      )     ->			str:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in range(800 ,      1400 ,      200      )]
       UpperCAmelCase__							 =						feature_extractor(
           lowerCamelCase ,      padding="longest" ,      max_length=4 ,      truncation=lowerCamelCase ,      return_tensors="np" ,      return_attention_mask=lowerCamelCase ,      )
       UpperCAmelCase__							 =						inputs.input_features
       UpperCAmelCase__							 =						inputs.attention_mask
       UpperCAmelCase__							 =						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)      )

       UpperCAmelCase__							 =						[floats_list((1, x)      )[0] for x in range(800 ,      1400 ,      200      )]
       UpperCAmelCase__							 =						feature_extractor(
           lowerCamelCase ,      padding="longest" ,      max_length=16 ,      truncation=lowerCamelCase ,      return_tensors="np" ,      return_attention_mask=lowerCamelCase ,      )
       UpperCAmelCase__							 =						inputs.input_features
       UpperCAmelCase__							 =						inputs.attention_mask
       UpperCAmelCase__							 =						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   :Any      )     ->			Union[str, Any]:
       import torch

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       UpperCAmelCase__							 =						np.random.rand(100 ,      32      ).astype(np.floataa      )
       UpperCAmelCase__							 =						np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
        UpperCAmelCase__							 =						feature_extractor.pad([{"input_features": inputs}] ,      return_tensors="np"      )
        self.assertTrue(np_processed.input_features.dtype == np.floataa      )
        UpperCAmelCase__							 =						feature_extractor.pad([{"input_features": inputs}] ,      return_tensors="pt"      )
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :int      )     ->			int:
       from datasets import load_dataset

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

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







      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       # fmt: off
       UpperCAmelCase__							 =						np.array([
           -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
           -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
           -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
       ]      )
       # fmt: on

       UpperCAmelCase__							 =						self._load_datasamples(1      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      return_tensors="pt"      ).input_features
       self.assertEquals(input_features.shape ,      (1, 584, 24)      )
       self.assertTrue(np.allclose(input_features[0, 0, :30] ,      lowerCamelCase ,      atol=1e-4      )      )











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

import cva
import numpy as np

# Parrameters
_lowerCAmelCase						:   Union[str, Any]		=   (7_2_0, 1_2_8_0)  # Height, Width
_lowerCAmelCase						:   Any		=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCAmelCase						:   List[str]		=   1 / 1_0_0
_lowerCAmelCase						:   Union[str, Any]		=   ""
_lowerCAmelCase						:   Any		=   ""
_lowerCAmelCase						:   List[str]		=   ""
_lowerCAmelCase						:   Union[str, Any]		=   2_5_0



def 						lowerCAmelCase		(		):




 """simple docstring"""







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

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







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

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

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list[int]						,							_lowerCAmelCase							:   tuple[int, int]						,							_lowerCAmelCase							:   tuple[float, float]						,							_lowerCAmelCase							:   float = 0.0						,							):




 """simple docstring"""







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

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




 """simple docstring"""







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


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





