






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor





def 						A_					(						lowercase_	,  lowercase_=1_0	,  lowercase_=1_0	,  lowercase_=False	,  lowercase_=False			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		[]
 for i in range(image_processor_tester.num_frames			):
  video.append(np.random.randint(2_5_5	,  size=(image_processor_tester.num_channels, width, height)	,  dtype=np.uinta			)			)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  SCREAMING_SNAKE_CASE				    =		[Image.fromarray(np.moveaxis(lowercase_	,  0	,  -1			)			) for frame in video]

 if torchify:
  SCREAMING_SNAKE_CASE				    =		[torch.from_numpy(lowercase_			) for frame in video]

 return video





def 						A_					(						lowercase_	,  lowercase_=False	,  lowercase_=False	,  lowercase_=False			)				->Any:







 """simple docstring"""

 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 SCREAMING_SNAKE_CASE				    =		[]
 for i in range(image_processor_tester.batch_size			):
  if equal_resolution:
   SCREAMING_SNAKE_CASE				    =		SCREAMING_SNAKE_CASE				    =		image_processor_tester.max_resolution
  else:
   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		np.random.choice(
       np.arange(image_processor_tester.min_resolution	,  image_processor_tester.max_resolution			)	,  2			)
   SCREAMING_SNAKE_CASE				    =		prepare_video(
       image_processor_tester=lowercase_	,  width=lowercase_	,  height=lowercase_	,  numpify=lowercase_	,  torchify=lowercase_	,  )
  video_inputs.append(lowercase_			)

 return video_inputs
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def __init__(		self					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	int=7  ,  lowerCAmelCase__					:	Dict=3  ,  lowerCAmelCase__					:	Optional[int]=4  ,  lowerCAmelCase__					:	Any=1_8  ,  lowerCAmelCase__					:	Dict=3_0  ,  lowerCAmelCase__					:	Union[str, Any]=4_0_0  ,  lowerCAmelCase__					:	List[Any]=True  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	Optional[Any]=True  ,  lowerCAmelCase__					:	int=[0.5, 0.5, 0.5]  ,  lowerCAmelCase__					:	Tuple=[0.5, 0.5, 0.5]  ,  lowerCAmelCase__					:	Dict=True  ,  lowerCAmelCase__					:	Union[str, Any]=None  ,  )							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		size if size is not None else {'shortest_edge': 1_8}
      SCREAMING_SNAKE_CASE				    =		crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		num_frames
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		min_resolution
      SCREAMING_SNAKE_CASE				    =		max_resolution
      SCREAMING_SNAKE_CASE				    =		do_resize
      SCREAMING_SNAKE_CASE				    =		size
      SCREAMING_SNAKE_CASE				    =		do_normalize
      SCREAMING_SNAKE_CASE				    =		image_mean
      SCREAMING_SNAKE_CASE				    =		image_std
      SCREAMING_SNAKE_CASE				    =		do_center_crop
      SCREAMING_SNAKE_CASE				    =		crop_size





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Optional[Any]:

      """simple docstring"""
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }

@require_torch
@require_vision
class 	a_(		lowercase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				int  					=TvltImageProcessor if is_vision_available() else None





     def     __UpperCamelCase						(		self					:	str)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		TvltImageProcessorTester(self)





     @property
     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()





     def     __UpperCamelCase						(		self					:	List[Any])							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_mean'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_std'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_normalize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_resize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_center_crop'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'size'))





     def     __UpperCamelCase						(		self					:	Tuple)							->	int:

      """simple docstring"""
      # Initialize image_processor
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      SCREAMING_SNAKE_CASE				    =		prepare_video_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__)
      for video in video_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  lowerCAmelCase__)
       self.assertIsInstance(video[0]  ,  Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processor(video_inputs[0]  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processor(lowerCAmelCase__  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )





     def     __UpperCamelCase						(		self					:	str)							->	Any:

      """simple docstring"""
      # Initialize image_processor
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE				    =		prepare_video_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  numpify=lowerCAmelCase__)
      for video in video_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  lowerCAmelCase__)
       self.assertIsInstance(video[0]  ,  np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processor(video_inputs[0]  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processor(lowerCAmelCase__  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )





     def     __UpperCamelCase						(		self					:	Tuple)							->	Optional[Any]:

      """simple docstring"""
      # Initialize image_processor
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE				    =		prepare_video_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  torchify=lowerCAmelCase__)
      for video in video_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  lowerCAmelCase__)
       self.assertIsInstance(video[0]  ,  torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processor(video_inputs[0]  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processor(lowerCAmelCase__  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )





'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Any       ="""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """     )
        _UpperCamelCase  :Optional[Any]       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )

        _UpperCamelCase  :int       ={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _UpperCamelCase  :Union[str, Any]       ={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        _UpperCamelCase  :List[str]       =tempfile.mkdtemp()
        _UpperCamelCase  :Optional[Any]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]     )
        _UpperCamelCase  :Tuple       =os.path.join(self.tmpdirname					,      lowerCAmelCase__     )
        with open(self.vocab_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )

        with open(self.feature_extraction_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase__     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.get_tokenizer()
        _UpperCamelCase  :int       =self.get_feature_extractor()

        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase  :Dict       =WavaVecaProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer.get_vocab()     )
        self.assertIsInstance(processor.tokenizer					,      lowerCAmelCase__     )

        self.assertEqual(processor.feature_extractor.to_json_string()					,      feature_extractor.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[int]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Optional[int]       =self.get_feature_extractor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :Optional[int]       =WavaVecaProcessor.from_pretrained(
            self.tmpdirname					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer					,      lowerCAmelCase__     )

        self.assertEqual(processor.feature_extractor.to_json_string()					,      feature_extractor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :List[str]       =self.get_tokenizer()

        _UpperCamelCase  :Any       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =floats_list((3, 1_000)     )

        _UpperCamelCase  :Tuple       =feature_extractor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Union[str, Any]       =processor(lowerCAmelCase__					,      return_tensors="""np"""     )

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()					,      input_processor[key].sum()					,      delta=1e-2     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.get_feature_extractor()
        _UpperCamelCase  :List[Any]       =self.get_tokenizer()

        _UpperCamelCase  :Tuple       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""This is a test string"""

        _UpperCamelCase  :Union[str, Any]       =processor(text=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =tokenizer(lowerCAmelCase__     )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]					,      encoded_processor[key]     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :str       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :List[Any]       =processor.batch_decode(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.batch_decode(lowerCAmelCase__     )

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :List[str]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        self.assertListEqual(
            processor.model_input_names					,      feature_extractor.model_input_names					,      msg="""`processor` and `feature_extractor` model input names do not match"""					,      )