
import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Optional[int]=				BioGptTokenizer
    _a		:						Optional[Any]=				False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowercase							:     Union[str, Any]       	=      [
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """w</w>""",
               """r</w>""",
               """t</w>""",
               """lo""",
               """low""",
               """er</w>""",
               """low</w>""",
               """lowest</w>""",
               """newer</w>""",
               """wider</w>""",
               """<unk>""",
           ]
           lowercase							:     int       	=      dict(zip(snake_case		,range(len(snake_case      )      )      )      )
           lowercase							:     Union[str, Any]       	=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

           lowercase							:     str       	=      os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]      )
           lowercase							:     Tuple       	=      os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""merges_file"""]      )
           with open(self.vocab_file		,"""w"""      ) as fp:
                  fp.write(json.dumps(snake_case      )      )
           with open(self.merges_file		,"""w"""      ) as fp:
                  fp.write("""\n""".join(snake_case      )      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      """lower newer"""
           lowercase							:     int       	=      """lower newer"""
           return input_text, output_text
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      BioGptTokenizer(self.vocab_file		,self.merges_file      )

           lowercase							:     Any       	=      """lower"""
           lowercase							:     Optional[int]       	=      ["""low""", """er</w>"""]
           lowercase							:     Optional[int]       	=      tokenizer.tokenize(snake_case      )
           self.assertListEqual(snake_case		,snake_case      )

           lowercase							:     Optional[int]       	=      tokens + ["""<unk>"""]
           lowercase							:     Optional[Any]       	=      [14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case      )		,snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      BioGptTokenizer.from_pretrained("""microsoft/biogpt"""      )

           lowercase							:     Dict       	=      tokenizer.encode("""sequence builders"""		,add_special_tokens=snake_case      )
           lowercase							:     Any       	=      tokenizer.encode("""multi-sequence build"""		,add_special_tokens=snake_case      )

           lowercase							:     List[Any]       	=      tokenizer.build_inputs_with_special_tokens(snake_case      )
           lowercase							:     int       	=      tokenizer.build_inputs_with_special_tokens(snake_case		,snake_case      )

           self.assertTrue(encoded_sentence == [2] + text      )
           self.assertTrue(encoded_pair == [2] + text + [2] + text_a      )


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 __snake_case		(      unittest.TestCase     ):
    _a		:						Optional[int]=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      hf_hub_download(
               repo_id="""nateraw/video-demo"""		,filename="""archery.mp4"""		,repo_type="""dataset"""      )
           lowercase							:     List[str]       	=      VideoClassificationPipeline(model=snake_case		,image_processor=snake_case		,top_k=2      )
           lowercase							:     Dict       	=      [
               example_video_filepath,
               """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
           ]
           return video_classifier, examples
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           for example in examples:
                  lowercase							:     int       	=      video_classifier(snake_case      )

                  self.assertEqual(
                      snake_case		,[
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                      ]		,)
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
           lowercase							:     str       	=      VideoMAEFeatureExtractor(
               size={"""shortest_edge""": 10}		,crop_size={"""height""": 10, """width""": 10}      )
           lowercase							:     List[Any]       	=      pipeline(
               """video-classification"""		,model=snake_case		,feature_extractor=snake_case		,frame_sampling_rate=4      )

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

           lowercase							:     str       	=      video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,top_k=2		,)
           self.assertEqual(
               nested_simplify(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
