

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xglm import XGLMTokenizer
else:
       lowerCamelCase     =      None


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase     =      {
    '''facebook/xglm-564M''': 2048,
}







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
   lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
   lowerCamelCase__				:			Any												=     XGLMTokenizer



   def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:






     """simple docstring"""






     a__						=7
     a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

     a__						=kwargs.get('''additional_special_tokens''',					[] )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)

     a__						=vocab_file
     a__						=False if not self.vocab_file else True



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     a__						=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     a__						=[self.sep_token_id]

     if token_ids_a is None:
       return len(sep + token_ids_a ) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:






     """simple docstring"""






     if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.''' )

     if not os.path.isdir(lowercase_ ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
       return
     a__						=os.path.join(
         lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
       copyfile(self.vocab_file,					lowercase_ )

     return (out_vocab_file,)



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     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				List[Any]=10			,   _A   :				Optional[int]=10			,   _A   :				Dict=False			,   _A   :				Any=False							):





  '''simple docstring'''







  a__						=[]
  for i in range(image_processor_tester.num_frames							):
    video.append(np.random.randint(2_55			,   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
    a__						=[Image.fromarray(np.moveaxis(_A			,   0			,   -1							)							) for frame in video]

  if torchify:
    a__						=[torch.from_numpy(_A							) for frame in video]

  return video




def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[int]=False			,   _A   :				Dict=False			,   _A   :				Optional[Any]=False							):





  '''simple docstring'''







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

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

  return video_inputs







class 		__magic_name__    (							unittest.TestCase							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=4,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					lowercase_=True,					lowercase_=None,					)							->					Optional[int]:






     """simple docstring"""






     a__						=size if size is not None else {'''shortest_edge''': 18}
     a__						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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



   def 				_UpperCAmelCase				(       self )							->					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 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			str												=     TvltImageProcessor if is_vision_available() else None



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






     """simple docstring"""






     a__						=TvltImageProcessorTester(self )



   @property
   def 				_UpperCAmelCase				(       self )							->					Optional[Any]:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



   def 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_center_crop''' ) )
     self.assertTrue(hasattr(lowercase_,					'''size''' ) )



   def 				_UpperCAmelCase				(       self )							->					Tuple:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PIL videos
     a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_ )
     for video in video_inputs:
       self.assertIsInstance(lowercase_,					lowercase_ )
       self.assertIsInstance(video[0],					Image.Image )

     # Test not batched input
     a__						=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
     a__						=image_processor(lowercase_,					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 )							->					int:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					numpify=lowercase_ )
     for video in video_inputs:
       self.assertIsInstance(lowercase_,					lowercase_ )
       self.assertIsInstance(video[0],					np.ndarray )

     # Test not batched input
     a__						=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
     a__						=image_processor(lowercase_,					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 )							->					int:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					torchify=lowercase_ )
     for video in video_inputs:
       self.assertIsInstance(lowercase_,					lowercase_ )
       self.assertIsInstance(video[0],					torch.Tensor )

     # Test not batched input
     a__						=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
     a__						=image_processor(lowercase_,					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'''],
         ),					)
