from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
 from ..models.auto import AutoTokenizer






class    a__    :


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        raise NotImplementedError()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        raise NotImplementedError()






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = False,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							tokenizer
        lowercase__				 =							skip_prompt
        lowercase__				 =							decode_kwargs

        # variables used in the streaming process
        lowercase__				 =							[]
        lowercase__				 =							0
        lowercase__				 =							True


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        if len(value.shape  ) > 1 and value.shape[0] > 1:
             raise ValueError("TextStreamer only supports batch size 1"  )
        elif len(value.shape  ) > 1:
             lowercase__				 =							value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
             lowercase__				 =							False
             return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()  )
        lowercase__				 =							self.tokenizer.decode(self.token_cache,				**self.decode_kwargs  )

        # After the symbol for a new line, we flush the cache.
        if text.endswith("\n"  ):
             lowercase__				 =							text[self.print_len :]
             lowercase__				 =							[]
             lowercase__				 =							0
        # If the last token is a CJK character, we print the characters.
        elif len(_UpperCAmelCase  ) > 0 and self._is_chinese_char(ord(text[-1]  )  ):
             lowercase__				 =							text[self.print_len :]
             self.print_len += len(_UpperCAmelCase  )
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
             lowercase__				 =							text[self.print_len : text.rfind(" "  ) + 1]
             self.print_len += len(_UpperCAmelCase  )

        self.on_finalized_text(_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if len(self.token_cache  ) > 0:
             lowercase__				 =							self.tokenizer.decode(self.token_cache,				**self.decode_kwargs  )
             lowercase__				 =							text[self.print_len :]
             lowercase__				 =							[]
             lowercase__				 =							0
        else:
             lowercase__				 =							""

        lowercase__				 =							True
        self.on_finalized_text(_UpperCAmelCase,				stream_end=_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = False  ):
        '''simple docstring'''



        print(_UpperCAmelCase,				flush=_UpperCAmelCase,				end="" if not stream_end else None  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        if (
            (cp >= 0X4e00 and cp <= 0X9fff)
            or (cp >= 0X3400 and cp <= 0X4dbf)  #
            or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
            or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
            or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
            or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
            or (cp >= 0Xf900 and cp <= 0Xfaff)
            or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
        ):  #
             return True

        return False






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				**_UpperCAmelCase  ):
        '''simple docstring'''



        super().__init__(_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  )
        lowercase__				 =							Queue()
        lowercase__				 =							None
        lowercase__				 =							timeout


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = False  ):
        '''simple docstring'''



        self.text_queue.put(_UpperCAmelCase,				timeout=self.timeout  )
        if stream_end:
             self.text_queue.put(self.stop_signal,				timeout=self.timeout  )


   def __iter__(     self  ):
        '''simple docstring'''



        return self


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.text_queue.get(timeout=self.timeout  )
        if value == self.stop_signal:
             raise StopIteration()
        else:
             return value





"""simple docstring"""


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    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

             self.assertEqual(
                 _UpperCAmelCase,				[
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                 ],				)


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
        lowercase__				 =							VideoMAEFeatureExtractor(
            size={"shortest_edge": 10},				crop_size={"height": 10, "width": 10}  )
        lowercase__				 =							pipeline(
            "video-classification",				model=_UpperCAmelCase,				feature_extractor=_UpperCAmelCase,				frame_sampling_rate=4  )

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							video_classifier(_UpperCAmelCase,				top_k=2  )
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],				)

        lowercase__				 =							video_classifier(
            [
                video_file_path,
                video_file_path,
            ],				top_k=2,				)
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
            ],				)


   @require_tf
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




