
"""simple docstring"""

import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm




def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       Optional[int]			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       1.5
    UpperCAmelCase          =       int(factor * num_class_images			)
    UpperCAmelCase          =       ClipClient(
        url='https://knn.laion.ai/knn-service'			,					indice_name='laion_400m'			,					num_images=lowerCAmelCase			,					aesthetic_weight=0.1			)

    os.makedirs(F"{class_data_dir}/images"			,					exist_ok=lowerCAmelCase			)
    if len(list(Path(F"{class_data_dir}/images"			).iterdir()			)			) >= num_class_images:
        return

    while True:
        UpperCAmelCase          =       client.query(text=lowerCAmelCase			)
        if len(lowerCAmelCase			) >= factor * num_class_images or num_images > 1E4:
            break
        else:
            UpperCAmelCase          =       int(factor * num_images			)
            UpperCAmelCase          =       ClipClient(
                url='https://knn.laion.ai/knn-service'			,					indice_name='laion_400m'			,					num_images=lowerCAmelCase			,					aesthetic_weight=0.1			,					)

    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    UpperCAmelCase          =       tqdm(desc='downloading real regularization images'			,					total=lowerCAmelCase			)

    with open(F"{class_data_dir}/caption.txt"			,					'w'			) as fa, open(F"{class_data_dir}/urls.txt"			,					'w'			) as fa, open(
        F"{class_data_dir}/images.txt"			,					'w'			) as fa:
        while total < num_class_images:
            UpperCAmelCase          =       class_images[count]
            count += 1
            try:
                UpperCAmelCase          =       requests.get(images['url']			)
                if img.status_code == 200:
                    UpperCAmelCase          =       Image.open(BytesIO(img.content			)			)
                    with open(F"{class_data_dir}/images/{total}.jpg"			,					'wb'			) as f:
                        f.write(img.content			)
                    fa.write(images['caption'] + '\n'			)
                    fa.write(images['url'] + '\n'			)
                    fa.write(F"{class_data_dir}/images/{total}.jpg" + '\n'			)
                    total += 1
                    pbar.update(1			)
                else:
                    continue
            except Exception:
                continue
    return




def      lowercase__    (       )			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase          =       argparse.ArgumentParser(''			,					add_help=lowerCAmelCase			)
    parser.add_argument('--class_prompt'			,					help='text prompt to retrieve images'			,					required=lowerCAmelCase			,					type=lowerCAmelCase			)
    parser.add_argument('--class_data_dir'			,					help='path to save images'			,					required=lowerCAmelCase			,					type=lowerCAmelCase			)
    parser.add_argument('--num_class_images'			,					help='number of images to download'			,					default=200			,					type=lowerCAmelCase			)
    return parser.parse_args()


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    parse_args()
   retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)





"""simple docstring"""

import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

SCREAMING_SNAKE_CASE_            =    random.Random()




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       Union[str, Any]=1.0			,					lowerCAmelCase					:       Any=None			,					lowerCAmelCase					:       Optional[Any]=None			)			->   Dict:






    """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 ,							lowercase_ ,							lowercase_=7 ,							lowercase_=4_0_0 ,							lowercase_=2_0_0_0 ,							lowercase_=1_0 ,							lowercase_=1_6_0 ,							lowercase_=8 ,							lowercase_=0.0 ,							lowercase_=4_0_0_0 ,							lowercase_=False ,							lowercase_=True ,							)    ->       Optional[int]:
          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          =       padding_value
          UpperCAmelCase          =       sampling_rate
          UpperCAmelCase          =       return_attention_mask
          UpperCAmelCase          =       do_normalize
          UpperCAmelCase          =       feature_size
          UpperCAmelCase          =       chunk_length
          UpperCAmelCase          =       hop_length




      def      a_   (				self				)    ->       Dict:
          return {
              "feature_size": self.feature_size,
              "hop_length": self.hop_length,
              "chunk_length": self.chunk_length,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "return_attention_mask": self.return_attention_mask,
              "do_normalize": self.do_normalize,
          }




      def      a_   (				self ,							lowercase_=False ,							lowercase_=False				)    ->       Optional[Any]:
          def _flatten(lowercase_				):
              return list(itertools.chain(*lowercase_				)				)

          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(lowercase_				) for x in speech_inputs]
          return speech_inputs

@require_torch
@require_torchaudio
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						WhisperFeatureExtractor if is_speech_available() else None




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       WhisperFeatureExtractionTester(self				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)

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

          UpperCAmelCase          =       feat_extract_first.to_dict()
          UpperCAmelCase          =       feat_extract_second.to_dict()
          UpperCAmelCase          =       feat_extract_first.mel_filters
          UpperCAmelCase          =       feat_extract_second.mel_filters
          self.assertTrue(np.allclose(lowercase_ ,							lowercase_				)				)
          self.assertEqual(lowercase_ ,							lowercase_				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)

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

          UpperCAmelCase          =       feat_extract_first.to_dict()
          UpperCAmelCase          =       feat_extract_second.to_dict()
          UpperCAmelCase          =       feat_extract_first.mel_filters
          UpperCAmelCase          =       feat_extract_second.mel_filters
          self.assertTrue(np.allclose(lowercase_ ,							lowercase_				)				)
          self.assertEqual(lowercase_ ,							lowercase_				)




      def      a_   (				self				)    ->       Tuple:
          # 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(8_0_0 ,							1_4_0_0 ,							2_0_0				)]
          UpperCAmelCase          =       [np.asarray(lowercase_				) for speech_input in speech_inputs]

          # Test feature size
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							padding='max_length' ,							return_tensors='np'				).input_features
          self.assertTrue(input_features.ndim == 3				)
          self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames				)
          self.assertTrue(input_features.shape[-2] == 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(lowercase_ ,							lowercase_ ,							atol=1E-3				)				)

          # Test batched
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='np'				).input_features
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='np'				).input_features
          for enc_seq_a, enc_seq_a in zip(lowercase_ ,							lowercase_				):
              self.assertTrue(np.allclose(lowercase_ ,							lowercase_ ,							atol=1E-3				)				)

          # Test 2-D numpy arrays are batched.
          UpperCAmelCase          =       [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
          UpperCAmelCase          =       np.asarray(lowercase_				)
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='np'				).input_features
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='np'				).input_features
          for enc_seq_a, enc_seq_a in zip(lowercase_ ,							lowercase_				):
              self.assertTrue(np.allclose(lowercase_ ,							lowercase_ ,							atol=1E-3				)				)

          # Test truncation required
          UpperCAmelCase          =       [floats_list((1, x)				)[0] for x in range(2_0_0 ,							(feature_extractor.n_samples + 5_0_0) ,							2_0_0				)]
          UpperCAmelCase          =       [np.asarray(lowercase_				) for speech_input in speech_inputs]

          UpperCAmelCase          =       [x[: feature_extractor.n_samples] for x in speech_inputs]
          UpperCAmelCase          =       [np.asarray(lowercase_				) for speech_input in speech_inputs_truncated]

          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='np'				).input_features
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='np'				).input_features
          for enc_seq_a, enc_seq_a in zip(lowercase_ ,							lowercase_				):
              self.assertTrue(np.allclose(lowercase_ ,							lowercase_ ,							atol=1E-3				)				)




      def      a_   (				self				)    ->       List[Any]:
          import torch

          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
          UpperCAmelCase          =       np.random.rand(1_0_0 ,							3_2				).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      a_   (				self ,							lowercase_				)    ->       Optional[Any]:
          UpperCAmelCase          =       load_dataset('hf-internal-testing/librispeech_asr_dummy' ,							'clean' ,							split='validation'				)
          # automatic decoding with librispeech
          UpperCAmelCase          =       ds.sort('id'				).select(range(lowercase_				)				)[:num_samples]['audio']

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




      def      a_   (				self				)    ->       Dict:
          # fmt: off
          UpperCAmelCase          =       torch.tensor(
              [
                  0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
                  0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
                  0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
                  -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
              ]				)
          # fmt: on

          UpperCAmelCase          =       self._load_datasamples(1				)
          UpperCAmelCase          =       WhisperFeatureExtractor()
          UpperCAmelCase          =       feature_extractor(lowercase_ ,							return_tensors='pt'				).input_features
          self.assertEqual(input_features.shape ,							(1, 8_0, 3_0_0_0)				)
          self.assertTrue(torch.allclose(input_features[0, 0, :3_0] ,							lowercase_ ,							atol=1E-4				)				)




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
          UpperCAmelCase          =       self._load_datasamples(1				)[0]
          UpperCAmelCase          =       ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
          UpperCAmelCase          =       feat_extract.zero_mean_unit_var_norm([audio] ,							attention_mask=lowercase_				)[0]

          self.assertTrue(np.all(np.mean(lowercase_				) < 1E-3				)				)
          self.assertTrue(np.all(np.abs(np.var(lowercase_				) - 1				) < 1E-3				)				)



