






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase__							=     random.Random()






def   UpperCamelCase  (       snake_case__     :   List[Any]	,snake_case__     :   int=1.0	,snake_case__     :   Any=None	,snake_case__     :   Optional[Any]=None  ):




     '''simple docstring'''






     if rng is None:
          __snake_case			:Optional[int]          =						global_rng

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

     return values




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=4_00			,						a__=20_00			,						a__=1			,						a__=0.0			,						a__=1_60_00			,						a__=True			,						a__=True			,						) ->      Dict:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__=False			,						a__=False    ) ->      List[Any]:






            '''simple docstring'''





            def _flatten(a__    ):
                 return list(itertools.chain(*a__    )    )

            if equal_length:
                 __snake_case			:Dict          =						floats_list((self.batch_size, self.max_seq_length)    )
            else:
                 # make sure that inputs increase in size
                 __snake_case			:Optional[int]          =						[
                     _flatten(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			:Union[str, Any]          =						[np.asarray(a__    ) for x in speech_inputs]

            return speech_inputs




class 			snake_case__   (							__lowercase	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= WavaVecaFeatureExtractor



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:str          =						WavaVecaFeatureExtractionTester(self    )



       def 					__lowercase						(							self			,						a__    ) ->      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 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            # create three inputs of length 800, 1000, and 1200
            __snake_case			:int          =						[floats_list((1, x)    )[0] for x in range(8_00			,						14_00			,						2_00    )]
            __snake_case			:Tuple          =						[np.asarray(a__    ) for speech_input in speech_inputs]

            # Test not batched input
            __snake_case			:int          =						feat_extract(speech_inputs[0]			,						return_tensors="""np"""    ).input_values
            __snake_case			:Dict          =						feat_extract(np_speech_inputs[0]			,						return_tensors="""np"""    ).input_values
            self.assertTrue(np.allclose(a__			,						a__			,						atol=1e-3    )    )

            # Test batched
            __snake_case			:Union[str, Any]          =						feat_extract(a__			,						return_tensors="""np"""    ).input_values
            __snake_case			:Optional[Any]          =						feat_extract(a__			,						return_tensors="""np"""    ).input_values
            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			:List[str]          =						[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
            __snake_case			:Tuple          =						np.asarray(a__    )
            __snake_case			:List[Any]          =						feat_extract(a__			,						return_tensors="""np"""    ).input_values
            __snake_case			:Union[str, Any]          =						feat_extract(a__			,						return_tensors="""np"""    ).input_values
            for enc_seq_a, enc_seq_a in zip(a__			,						a__    ):
                 self.assertTrue(np.allclose(a__			,						a__			,						atol=1e-3    )    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            __snake_case			:Tuple          =						[floats_list((1, x)    )[0] for x in range(8_00			,						14_00			,						2_00    )]

            __snake_case			:Dict          =						["longest", "max_length", "do_not_pad"]
            __snake_case			:List[str]          =						[None, 16_00, None]
            for max_length, padding in zip(a__			,						a__    ):
                 __snake_case			:str          =						feat_extract(a__			,						padding=a__			,						max_length=a__			,						return_tensors="""np"""    )
                 __snake_case			:Any          =						processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:8_00]    )
                 self.assertTrue(input_values[0][8_00:].sum() < 1e-6    )
                 self._check_zero_mean_unit_variance(input_values[1][:10_00]    )
                 self.assertTrue(input_values[0][10_00:].sum() < 1e-6    )
                 self._check_zero_mean_unit_variance(input_values[2][:12_00]    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            __snake_case			:Any          =						range(8_00			,						14_00			,						2_00    )
            __snake_case			:int          =						[floats_list((1, x)    )[0] for x in lengths]

            __snake_case			:Any          =						["longest", "max_length", "do_not_pad"]
            __snake_case			:Union[str, Any]          =						[None, 16_00, None]

            for max_length, padding in zip(a__			,						a__    ):
                 __snake_case			:Union[str, Any]          =						feat_extract(a__			,						max_length=a__			,						padding=a__    )
                 __snake_case			:Union[str, Any]          =						processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:8_00]    )
                 self._check_zero_mean_unit_variance(input_values[1][:10_00]    )
                 self._check_zero_mean_unit_variance(input_values[2][:12_00]    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:int          =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            __snake_case			:List[Any]          =						[floats_list((1, x)    )[0] for x in range(8_00			,						14_00			,						2_00    )]
            __snake_case			:Dict          =						feat_extract(
                a__			,						truncation=a__			,						max_length=10_00			,						padding="""max_length"""			,						return_tensors="""np"""    )
            __snake_case			:Union[str, Any]          =						processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_00]    )
            self._check_zero_mean_unit_variance(input_values[1]    )
            self._check_zero_mean_unit_variance(input_values[2]    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:str          =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            __snake_case			:Tuple          =						[floats_list((1, x)    )[0] for x in range(8_00			,						14_00			,						2_00    )]
            __snake_case			:List[str]          =						feat_extract(
                a__			,						truncation=a__			,						max_length=10_00			,						padding="""longest"""			,						return_tensors="""np"""    )
            __snake_case			:List[Any]          =						processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_00]    )
            self._check_zero_mean_unit_variance(input_values[1, :10_00]    )
            self._check_zero_mean_unit_variance(input_values[2]    )

            # make sure that if max_length < longest -> then pad to max_length
            self.assertTrue(input_values.shape == (3, 10_00)    )

            __snake_case			:Optional[Any]          =						[floats_list((1, x)    )[0] for x in range(8_00			,						14_00			,						2_00    )]
            __snake_case			:Any          =						feat_extract(
                a__			,						truncation=a__			,						max_length=20_00			,						padding="""longest"""			,						return_tensors="""np"""    )
            __snake_case			:Any          =						processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_00]    )
            self._check_zero_mean_unit_variance(input_values[1, :10_00]    )
            self._check_zero_mean_unit_variance(input_values[2]    )

            # make sure that if max_length > longest -> then pad to longest
            self.assertTrue(input_values.shape == (3, 12_00)    )



       @require_torch
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            import torch

            __snake_case			:Union[str, Any]          =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            __snake_case			:Optional[Any]          =						np.random.rand(1_00    ).astype(np.floataa    )
            __snake_case			:Optional[Any]          =						np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                 __snake_case			:Optional[Any]          =						feature_extractor.pad([{"""input_values""": inputs}]			,						return_tensors="""np"""    )
                 self.assertTrue(np_processed.input_values.dtype == np.floataa    )
                 __snake_case			:Dict          =						feature_extractor.pad([{"""input_values""": inputs}]			,						return_tensors="""pt"""    )
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )



       @slow
       @require_torch
       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                 __snake_case			:List[Any]          =						WavaVecaConfig.from_pretrained(a__    )
                 __snake_case			:Tuple          =						WavaVecaFeatureExtractor.from_pretrained(a__    )

                 # only "layer" feature extraction norm should make use of
                 # attention_mask
                 self.assertEqual(feat_extract.return_attention_mask			,						config.feat_extract_norm == """layer"""    )










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   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   Dict	,snake_case__     :   List[str]  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						1.5
     __snake_case			:Any          =						int(factor * num_class_images  )
     __snake_case			:List[str]          =						ClipClient(
         url="""https://knn.laion.ai/knn-service"""	,indice_name="""laion_400m"""	,num_images=snake_case__	,aesthetic_weight=0.1  )

     os.makedirs(f'''{class_data_dir}/images'''	,exist_ok=snake_case__  )
     if len(list(Path(f'''{class_data_dir}/images'''  ).iterdir()  )  ) >= num_class_images:
          return

     while True:
          __snake_case			:Optional[Any]          =						client.query(text=snake_case__  )
          if len(snake_case__  ) >= factor * num_class_images or num_images > 1e4:
               break
          else:
               __snake_case			:Tuple          =						int(factor * num_images  )
               __snake_case			:Any          =						ClipClient(
                   url="""https://knn.laion.ai/knn-service"""	,indice_name="""laion_400m"""	,num_images=snake_case__	,aesthetic_weight=0.1	,)

     __snake_case			:Dict          =						0
     __snake_case			:Tuple          =						0
     __snake_case			:Dict          =						tqdm(desc="""downloading real regularization images"""	,total=snake_case__  )

     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:
               __snake_case			:List[Any]          =						class_images[count]
               count += 1
               try:
                    __snake_case			:str          =						requests.get(images["""url"""]  )
                    if img.status_code == 200:
                         __snake_case			:Any          =						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   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						argparse.ArgumentParser(""""""	,add_help=snake_case__  )
     parser.add_argument("""--class_prompt"""	,help="""text prompt to retrieve images"""	,required=snake_case__	,type=snake_case__  )
     parser.add_argument("""--class_data_dir"""	,help="""path to save images"""	,required=snake_case__	,type=snake_case__  )
     parser.add_argument("""--num_class_images"""	,help="""number of images to download"""	,default=200	,type=snake_case__  )
     return parser.parse_args()


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


