




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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    hf_hub_download(
							    repo_id='nateraw/video-demo'			,						filename='archery.mp4'			,						repo_type='dataset'							)
							__lowerCamelCase		       =    VideoClassificationPipeline(model=lowerCamelCase__			,						image_processor=lowerCamelCase__			,						top_k=2							)
							__lowerCamelCase		       =    [
							    example_video_filepath,
							    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
							]
							return video_classifier, examples


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							for example in examples:
											__lowerCamelCase		       =    video_classifier(lowerCamelCase__							)

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


			@require_torch
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
							__lowerCamelCase		       =    VideoMAEFeatureExtractor(
							    size={'shortest_edge': 10}			,						crop_size={'height': 10, 'width': 10}							)
							__lowerCamelCase		       =    pipeline(
							    'video-classification'			,						model=lowerCamelCase__			,						feature_extractor=lowerCamelCase__			,						frame_sampling_rate=4							)

							__lowerCamelCase		       =    hf_hub_download(repo_id='nateraw/video-demo'			,						filename='archery.mp4'			,						repo_type='dataset'							)
							__lowerCamelCase		       =    video_classifier(lowerCamelCase__			,						top_k=2							)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__			,						decimals=4							)			,						[{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}]			,						)

							__lowerCamelCase		       =    video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]			,						top_k=2			,						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__			,						decimals=4							)			,						[
							        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
							        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
							    ]			,						)






			@require_tf
			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							pass








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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__A        =    logging.get_logger(__name__)

__A        =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A        =    {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__A        =    {
    "gpt-neox-20b": 20_48,
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       VOCAB_FILES_NAMES
			snake_case_   		=       PRETRAINED_VOCAB_FILES_MAP
			snake_case_   		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   		=       ['''input_ids''', '''attention_mask''']


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__=False			,						**lowerCamelCase__			,						)    ->      int:



							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,						lowerCamelCase__			,						tokenizer_file=lowerCamelCase__			,						unk_token=lowerCamelCase__			,						bos_token=lowerCamelCase__			,						eos_token=lowerCamelCase__			,						add_prefix_space=lowerCamelCase__			,						**lowerCamelCase__			,						)

							__lowerCamelCase		       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('add_prefix_space'			,						lowerCamelCase__							) != add_prefix_space:
											__lowerCamelCase		       =    getattr(lowerCamelCase__			,						pre_tok_state.pop('type'							)							)
											__lowerCamelCase		       =    add_prefix_space
											__lowerCamelCase		       =    pre_tok_class(**lowerCamelCase__							)

							__lowerCamelCase		       =    add_prefix_space


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



							'''simple docstring'''
							__lowerCamelCase		       =    self._tokenizer.model.save(lowerCamelCase__			,						name=lowerCamelCase__							)
							return tuple(lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__							) + [self.eos_token_id]							)

							if len(lowerCamelCase__							) > self.model_max_length:
											__lowerCamelCase		       =    input_ids[-self.model_max_length :]
							return input_ids


