
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: Any      =       {"tokenizer_file": "tokenizer.json"}

_UpperCAmelCase					: Optional[int]      =       {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       Dict	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       Optional[Any]	     =				["input_ids", "attention_mask"]
		__lowercase       :       Union[str, Any]	     =				None



		def __init__( self				,      A_=None				,      A_=None				,      A_=None				,      A_="<unk>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_=False				,      A_=False				,      **A_				,      )  ->					Dict:
				"""simple docstring"""
				super().__init__(
				    A_				,      A_				,      tokenizer_file=A_				,      unk_token=A_				,      bos_token=A_				,      eos_token=A_				,      pad_token=A_				,      add_prefix_space=A_				,      clean_up_tokenization_spaces=A_				,      **A_				,      )
				UpperCamelCase		=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
				if pre_tok_state.get('add_prefix_space'				,      A_       ) != add_prefix_space:
						UpperCamelCase		=     getattr(A_				,      pre_tok_state.pop('type'       )       )
						UpperCamelCase		=     add_prefix_space
						UpperCamelCase		=     pre_tok_class(**A_       )

				UpperCamelCase		=     add_prefix_space



		def __UpperCamelCase ( self				,      *A_				,      **A_       )  ->					BatchEncoding:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.get('is_split_into_words'				,      A_       )
				if not (self.add_prefix_space or not is_split_into_words):
						raise Exception(
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
						    ' pretokenized inputs.'       )

				return super()._batch_encode_plus(*A_				,      **A_       )



		def __UpperCamelCase ( self				,      *A_				,      **A_       )  ->					BatchEncoding:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.get('is_split_into_words'				,      A_       )

				if not (self.add_prefix_space or not is_split_into_words):
						raise Exception(
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
						    ' pretokenized inputs.'       )

				return super()._encode_plus(*A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				UpperCamelCase		=     self._tokenizer.model.save(A_				,      name=A_       )
				return tuple(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					List[int]:
				"""simple docstring"""
				UpperCamelCase		=     []
				for is_user, text in conversation.iter_texts():
						input_ids.extend(self.encode(A_				,      add_special_tokens=A_       ) + [self.eos_token_id]       )

				if len(A_       ) > self.model_max_length:
						UpperCamelCase		=     input_ids[-self.model_max_length :]
				return input_ids


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				lowercase					(		unittest.TestCase   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

						self.assertEqual(
						    A_				,      [
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						    ]				,      )



		@require_torch
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				UpperCamelCase		=     VideoMAEFeatureExtractor(
				    size={'shortest_edge': 10}				,      crop_size={'height': 10, 'width': 10}       )
				UpperCamelCase		=     pipeline(
				    'video-classification'				,      model=A_				,      feature_extractor=A_				,      frame_sampling_rate=4       )

				UpperCamelCase		=     hf_hub_download(repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     video_classifier(A_				,      top_k=2       )
				self.assertEqual(
				    nested_simplify(A_				,      decimals=4       )				,      [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}]				,      )

				UpperCamelCase		=     video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]				,      top_k=2				,      )
				self.assertEqual(
				    nested_simplify(A_				,      decimals=4       )				,      [
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				    ]				,      )



		@require_tf
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
