




import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
         TimesformerForVideoClassification,
         TimesformerModel,
     )
     from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from transformers import VideoMAEImageProcessor

class     __a:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=13  ,_SCREAMING_SNAKE_CASE=10  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=5  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=37  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=10  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE="divided_space_time"  ,_SCREAMING_SNAKE_CASE=None  ,)					->						Union[str, Any]:
         UpperCAmelCase_     :				List[Any]  =     parent
         UpperCAmelCase_     :				List[Any]  =     batch_size
         UpperCAmelCase_     :				Optional[int]  =     image_size
         UpperCAmelCase_     :				Optional[Any]  =     num_channels
         UpperCAmelCase_     :				Optional[Any]  =     patch_size
         UpperCAmelCase_     :				Dict  =     num_frames
         UpperCAmelCase_     :				int  =     is_training
         UpperCAmelCase_     :				Any  =     use_labels
         UpperCAmelCase_     :				Union[str, Any]  =     hidden_size
         UpperCAmelCase_     :				Tuple  =     num_hidden_layers
         UpperCAmelCase_     :				Optional[int]  =     num_attention_heads
         UpperCAmelCase_     :				Tuple  =     intermediate_size
         UpperCAmelCase_     :				List[Any]  =     hidden_act
         UpperCAmelCase_     :				Dict  =     hidden_dropout_prob
         UpperCAmelCase_     :				int  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Dict  =     attention_type
         UpperCAmelCase_     :				List[Any]  =     initializer_range
         UpperCAmelCase_     :				int  =     scope
         UpperCAmelCase_     :				List[str]  =     num_labels

         # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
         UpperCAmelCase_     :				str  =     (image_size // patch_size) ** 2
         UpperCAmelCase_     :				Dict  =     (num_frames) * self.num_patches_per_frame + 1





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Any  =     floats_tensor(
             [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

         UpperCAmelCase_     :				Union[str, Any]  =     None
         if self.use_labels:
              UpperCAmelCase_     :				Optional[Any]  =     ids_tensor([self.batch_size]  ,self.num_labels    )

         UpperCAmelCase_     :				Any  =     self.get_config()

         return config, pixel_values, labels





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				List[Any]  =     TimesformerConfig(
             image_size=self.image_size  ,patch_size=self.patch_size  ,num_channels=self.num_channels  ,num_frames=self.num_frames  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,intermediate_size=self.intermediate_size  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,initializer_range=self.initializer_range  ,attention_type=self.attention_type  ,)
         UpperCAmelCase_     :				Any  =     self.num_labels
         return config





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[str]  =     TimesformerModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Optional[Any]  =     model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         UpperCAmelCase_     :				int  =     TimesformerForVideoClassification(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()

         UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE    )

         # verify the logits shape
         UpperCAmelCase_     :				Optional[Any]  =     torch.Size((self.batch_size, self.num_labels)    )
         self.parent.assertEqual(result.logits.shape  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				int  =     self.prepare_config_and_inputs()
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     config_and_inputs
         UpperCAmelCase_     :				Tuple  =     {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
    lowerCAmelCase         = (
        {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
        if is_torch_available()
        else {}
    )

    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Dict  =     TimesformerModelTester(self    )
         UpperCAmelCase_     :				str  =     ConfigTester(
             self  ,config_class=_SCREAMING_SNAKE_CASE  ,has_text_modality=_SCREAMING_SNAKE_CASE  ,hidden_size=37    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=False    )					->						List[Any]:
         UpperCAmelCase_     :				List[str]  =     copy.deepcopy(_SCREAMING_SNAKE_CASE    )

         if return_labels:
              if model_class in get_values(_SCREAMING_SNAKE_CASE    ):
                   UpperCAmelCase_     :				List[str]  =     torch.zeros(
                       self.model_tester.batch_size  ,dtype=torch.long  ,device=_SCREAMING_SNAKE_CASE    )

         return inputs_dict





    def 				a__ ( self    )					->						Union[str, Any]:
         self.config_tester.run_common_tests()





    @unittest.skip(reason='''TimeSformer does not use inputs_embeds'''    )
    def 				a__ ( self    )					->						Union[str, Any]:
         pass





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_,       UpperCAmelCase_     :				int  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_     :				int  =     model_class(_SCREAMING_SNAKE_CASE    )
              self.assertIsInstance(model.get_input_embeddings()  ,(nn.Module)    )
              UpperCAmelCase_     :				int  =     model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE  ,nn.Linear    )    )





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_     :				str  =     model_class(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Any  =     inspect.signature(model.forward    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_     :				Any  =     [*signature.parameters.keys()]

              UpperCAmelCase_     :				Optional[int]  =     ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_video_classification(*_SCREAMING_SNAKE_CASE    )





    @slow
    def 				a__ ( self    )					->						List[Any]:
         for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_     :				Optional[Any]  =     TimesformerModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[str]:
         if not self.has_attentions:
              pass

         else:
              UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.model_tester.prepare_config_and_inputs_for_common()
              UpperCAmelCase_     :				Optional[int]  =     True

              for model_class in self.all_model_classes:
                   UpperCAmelCase_     :				List[Any]  =     self.model_tester.seq_length
                   UpperCAmelCase_     :				Dict  =     self.model_tester.num_frames

                   UpperCAmelCase_     :				Any  =     True
                   UpperCAmelCase_     :				Any  =     False
                   UpperCAmelCase_     :				str  =     True
                   UpperCAmelCase_     :				List[Any]  =     model_class(_SCREAMING_SNAKE_CASE    )
                   model.to(_SCREAMING_SNAKE_CASE    )
                   model.eval()
                   with torch.no_grad():
                        UpperCAmelCase_     :				List[str]  =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )
                   UpperCAmelCase_     :				Optional[Any]  =     outputs.attentions
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,self.model_tester.num_hidden_layers    )

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   UpperCAmelCase_     :				List[str]  =     True
                   UpperCAmelCase_     :				Dict  =     model_class(_SCREAMING_SNAKE_CASE    )
                   model.to(_SCREAMING_SNAKE_CASE    )
                   model.eval()
                   with torch.no_grad():
                        UpperCAmelCase_     :				Optional[Any]  =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )
                   UpperCAmelCase_     :				Union[str, Any]  =     outputs.attentions
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,self.model_tester.num_hidden_layers    )

                   # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]    )  ,[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]  ,)
                   UpperCAmelCase_     :				Optional[Any]  =     len(_SCREAMING_SNAKE_CASE    )

                   # Check attention is always last and order is fine
                   UpperCAmelCase_     :				Optional[Any]  =     True
                   UpperCAmelCase_     :				Optional[int]  =     True
                   UpperCAmelCase_     :				List[str]  =     model_class(_SCREAMING_SNAKE_CASE    )
                   model.to(_SCREAMING_SNAKE_CASE    )
                   model.eval()
                   with torch.no_grad():
                        UpperCAmelCase_     :				Any  =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )

                   self.assertEqual(out_len + 1  ,len(_SCREAMING_SNAKE_CASE    )    )

                   UpperCAmelCase_     :				str  =     outputs.attentions

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,self.model_tester.num_hidden_layers    )

                   # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]    )  ,[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]  ,)





    def 				a__ ( self    )					->						Optional[int]:
         def check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				List[Any]  =     model_class(_SCREAMING_SNAKE_CASE    )
              model.to(_SCREAMING_SNAKE_CASE    )
              model.eval()

              with torch.no_grad():
                   UpperCAmelCase_     :				List[Any]  =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )

              UpperCAmelCase_     :				List[Any]  =     outputs.hidden_states
              UpperCAmelCase_     :				Union[str, Any]  =     self.model_tester.num_hidden_layers + 1
              self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				List[Any]  =     self.model_tester.seq_length

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]    )  ,[seq_length, self.model_tester.hidden_size]  ,)

         UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_     :				Any  =     True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_     :				Optional[int]  =     True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''					,		filename='''eating_spaghetti.npy'''					,		repo_type='''dataset''' )
     UpperCAmelCase_     :				List[Any]  =     np.load(_lowercase )
     return list(_lowercase )



@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @cached_property
    def 				a__ ( self    )					->						List[Any]:
         # logits were tested with a different mean and std, so we use the same here
         return (
             VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,image_std=[0.5, 0.5, 0.5]    )
             if is_vision_available()
             else None
         )





    @slow
    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Any  =     TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''    ).to(
             _SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Union[str, Any]  =     self.default_image_processor
         UpperCAmelCase_     :				Optional[Any]  =     prepare_video()
         UpperCAmelCase_     :				List[str]  =     image_processor(video[:8]  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     model(**_SCREAMING_SNAKE_CASE    )

         # verify the logits
         UpperCAmelCase_     :				List[str]  =     torch.Size((1, 400)    )
         self.assertEqual(outputs.logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([-0.30_16, -0.77_13, -0.42_05]    ).to(_SCREAMING_SNAKE_CASE    )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )





import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase      :						Dict							=  get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_sentencepiece_available():
  import sentencepiece as sp


_lowerCamelCase      :						str							=  5
_lowerCamelCase      :						int							=  10


@require_sentencepiece
@require_tokenizers
class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = SpeechaTextTokenizer
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = True


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[str]:





           '''simple docstring'''

           super().setUp()

           A__ 					=       sp.SentencePieceProcessor()
           spm_model.Load(UpperCAmelCase__)
           A__ 					=       ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']

           vocab += [spm_model.IdToPiece(id_) for id_ in range(len(UpperCAmelCase__))]
           A__ 					=       dict(zip(UpperCAmelCase__   ,					range(len(UpperCAmelCase__))))

           A__ 					=       Path(self.tmpdirname)
           save_json(UpperCAmelCase__   ,					save_dir / VOCAB_FILES_NAMES['''vocab_file'''])
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                  copyfile(UpperCAmelCase__   ,					save_dir / VOCAB_FILES_NAMES['''spm_file'''])

           A__ 					=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
           tokenizer.save_pretrained(self.tmpdirname)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       '''<pad>'''
           A__ 					=       1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__)   ,					UpperCAmelCase__)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__)   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

           A__ 					=       list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]   ,					'''<s>''')
           self.assertEqual(vocab_keys[1]   ,					'''<pad>''')
           self.assertEqual(vocab_keys[-1]   ,					'''j''')
           self.assertEqual(len(UpperCAmelCase__)   ,					1_001)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[str]:





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size   ,					1_001)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[str]:





           '''simple docstring'''

           A__ 					=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname)

           A__ 					=       tokenizer.tokenize('''This is a test''')
           self.assertListEqual(UpperCAmelCase__   ,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCAmelCase__)   ,					[289, 50, 14, 174, 386]   ,					)

           A__ 					=       tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               UpperCAmelCase__   ,					[SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']   ,					)
           A__ 					=       tokenizer.convert_tokens_to_ids(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__   ,					[12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8])

           A__ 					=       tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__   ,					[SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']   ,					)





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[Any]:





           '''simple docstring'''

           A__ 					=       {'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase__   ,					model_name='''facebook/s2t-small-mustc-en-de-st'''   ,					revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''   ,					)


@require_sentencepiece
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''valhalla/s2t_mustc_multilinguial_medium'''

    UpperCAmelCase__				    = '''C\'est trop cool'''
    UpperCAmelCase__				    = '''Esto es genial'''


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Dict)		->Dict:





           '''simple docstring'''

           A__ 					=       SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name)
           return cls


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Dict:





           '''simple docstring'''

           self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']   ,					4)
           self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']   ,					6)
           self.assertEqual(self.tokenizer.lang_code_to_id['''it''']   ,					9)
           self.assertEqual(self.tokenizer.lang_code_to_id['''de''']   ,					11)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Dict:





           '''simple docstring'''

           self.assertEqual(self.tokenizer.vocab_size   ,					10_000)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Union[str, Any]:





           '''simple docstring'''

           self.assertIn(UpperCAmelCase__   ,					self.tokenizer.all_special_ids)
           A__ 					=       [ES_CODE, 4, 1_601, 47, 7_647, 2]
           A__ 					=       self.tokenizer.decode(UpperCAmelCase__   ,					skip_special_tokens=UpperCAmelCase__)
           A__ 					=       self.tokenizer.decode(generated_ids[1:]   ,					skip_special_tokens=UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__   ,					UpperCAmelCase__)
           self.assertNotIn(self.tokenizer.eos_token   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->str:





           '''simple docstring'''

           A__ 					=       '''fr'''
           A__ 					=       self.tokenizer(self.french_text).input_ids
           self.assertEqual(encoded[0]   ,					UpperCAmelCase__)
           self.assertEqual(encoded[-1]   ,					self.tokenizer.eos_token_id)





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





           '''simple docstring'''

           A__ 					=       '''fr'''
           self.assertListEqual(self.tokenizer.prefix_tokens   ,					[FR_CODE])

           A__ 					=       '''es'''
           self.assertListEqual(self.tokenizer.prefix_tokens   ,					[ES_CODE])


