




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  : Optional[int]						   =							logging.get_logger(__name__)

lowercase  : str						   =							{
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						str=    "vivit"
    def __init__(  self		,snake_case=224		,snake_case=32		,snake_case=[2, 16, 16]		,snake_case=3		,snake_case=768		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu_fast"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.02		,snake_case=1e-06		,snake_case=True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					int	     =				hidden_size
           lowercase							:					List[Any]	     =				num_hidden_layers
           lowercase							:					Any	     =				num_attention_heads
           lowercase							:					int	     =				intermediate_size
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Any	     =				hidden_dropout_prob
           lowercase							:					Dict	     =				attention_probs_dropout_prob
           lowercase							:					Any	     =				initializer_range
           lowercase							:					Dict	     =				layer_norm_eps

           lowercase							:					Optional[Any]	     =				image_size
           lowercase							:					Optional[Any]	     =				num_frames
           lowercase							:					List[str]	     =				tubelet_size
           lowercase							:					Tuple	     =				num_channels
           lowercase							:					Optional[int]	     =				qkv_bias

           super().__init__(**snake_case				)


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Optional[int]=    None
    _a		:						Optional[Any]=    BloomTokenizerFast
    _a		:						Tuple=    BloomTokenizerFast
    _a		:						str=    True
    _a		:						Optional[int]=    False
    _a		:						List[Any]=    "tokenizer_file"
    _a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map				)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_rust_tokenizer()

           lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

           lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					Optional[Any]	     =				[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

                         lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
           lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

           lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
           lowercase							:					str	     =				list(sample_data.values()				)

           lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
           lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)
