


"""simple docstring"""







import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 				UpperCamelCase_			(				__lowerCAmelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Any					      =    BlenderbotSmallTokenizer
	snake_case__       :						Optional[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Union[str, Any]:
						super().setUp()

						__SCREAMING_SNAKE_CASE		     =       ["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
						__SCREAMING_SNAKE_CASE		     =       dict(zip(_UpperCamelCase    ,    range(len(_UpperCamelCase     )     )     )     )

						__SCREAMING_SNAKE_CASE		     =       ["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
						__SCREAMING_SNAKE_CASE		     =       {"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

						__SCREAMING_SNAKE_CASE		     =       os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES["vocab_file"]     )
						__SCREAMING_SNAKE_CASE		     =       os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES["merges_file"]     )
						with open(self.vocab_file    ,    "w"    ,    encoding="utf-8"     ) as fp:
											fp.write(json.dumps(_UpperCamelCase     ) + "\n"     )
						with open(self.merges_file    ,    "w"    ,    encoding="utf-8"     ) as fp:
											fp.write("\n".join(_UpperCamelCase     )     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    **UpperCAmelCase__    :       int     ) ->			Optional[int]:
						kwargs.update(self.special_tokens_map     )
						return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    ,    **_UpperCamelCase     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       List[Any]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       """adapt act apte"""
						__SCREAMING_SNAKE_CASE		     =       """adapt act apte"""
						return input_text, output_text




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       BlenderbotSmallTokenizer(self.vocab_file    ,    self.merges_file    ,    **self.special_tokens_map     )
						__SCREAMING_SNAKE_CASE		     =       """adapt act apte"""
						__SCREAMING_SNAKE_CASE		     =       ["""adapt""", """act""", """ap@@""", """te"""]
						__SCREAMING_SNAKE_CASE		     =       tokenizer.tokenize(_UpperCamelCase     )
						self.assertListEqual(_UpperCamelCase    ,    _UpperCamelCase     )

						__SCREAMING_SNAKE_CASE		     =       [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

						__SCREAMING_SNAKE_CASE		     =       [0, 1, 2, 3, 4, 5]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )    ,    _UpperCamelCase     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
						assert tok("sam"     ).input_ids == [1_3_8_4]
						__SCREAMING_SNAKE_CASE		     =       """I am a small frog."""
						__SCREAMING_SNAKE_CASE		     =       tok([src_text]    ,    padding=_UpperCamelCase    ,    truncation=_UpperCamelCase     )["""input_ids"""]
						__SCREAMING_SNAKE_CASE		     =       tok.batch_decode(_UpperCamelCase    ,    skip_special_tokens=_UpperCamelCase    ,    clean_up_tokenization_spaces=_UpperCamelCase     )[0]
						assert src_text != decoded  # I wish it did!
						assert decoded == "i am a small frog ."




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
						__SCREAMING_SNAKE_CASE		     =       """I am a small frog ."""
						__SCREAMING_SNAKE_CASE		     =       """."""
						__SCREAMING_SNAKE_CASE		     =       tok(_UpperCamelCase     )["""input_ids"""]
						__SCREAMING_SNAKE_CASE		     =       tok(_UpperCamelCase     )["""input_ids"""]

						assert encoded[-1] == encoded_dot[0]









"""simple docstring"""







import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:




		class 				UpperCamelCase_			:

			"""simple docstring"""





			@staticmethod
			def 						UpperCAmelCase_   (				*UpperCAmelCase__    :       List[Any]    ,    **UpperCAmelCase__    :       Optional[Any]     ) ->			Optional[int]:
								pass




@is_pipeline_test
@require_torch
@require_vision
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       pipeline("visual-question-answering"    ,    model="hf-internal-testing/tiny-vilt-random-vqa"     )
						__SCREAMING_SNAKE_CASE		     =       [
						    {
						        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
						        "question": "How many cats are there?",
						    },
						    {
						        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
						        "question": "How many cats are there?",
						    },
						]
						return vqa_pipeline, examples




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Any     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       vqa_pipeline(UpperCAmelCase__    ,    top_k=1     )
						self.assertEqual(
						    UpperCAmelCase__    ,    [
						        [{"score": ANY(UpperCAmelCase__     ), "answer": ANY(UpperCAmelCase__     )}],
						        [{"score": ANY(UpperCAmelCase__     ), "answer": ANY(UpperCAmelCase__     )}],
						    ]    ,    )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       pipeline("visual-question-answering"    ,    model="hf-internal-testing/tiny-vilt-random-vqa"     )
						__SCREAMING_SNAKE_CASE		     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
						__SCREAMING_SNAKE_CASE		     =       "How many cats are there?"

						__SCREAMING_SNAKE_CASE		     =       vqa_pipeline(image=UpperCAmelCase__    ,    question="How many cats are there?"    ,    top_k=2     )
						self.assertEqual(
						    UpperCAmelCase__    ,    [{"score": ANY(UpperCAmelCase__     ), "answer": ANY(UpperCAmelCase__     )}, {"score": ANY(UpperCAmelCase__     ), "answer": ANY(UpperCAmelCase__     )}]     )

						__SCREAMING_SNAKE_CASE		     =       vqa_pipeline({"image": image, "question": question}    ,    top_k=2     )
						self.assertEqual(
						    UpperCAmelCase__    ,    [{"score": ANY(UpperCAmelCase__     ), "answer": ANY(UpperCAmelCase__     )}, {"score": ANY(UpperCAmelCase__     ), "answer": ANY(UpperCAmelCase__     )}]     )




	@slow
	@require_torch
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       pipeline("visual-question-answering"    ,    model="dandelin/vilt-b32-finetuned-vqa"     )
						__SCREAMING_SNAKE_CASE		     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
						__SCREAMING_SNAKE_CASE		     =       "How many cats are there?"

						__SCREAMING_SNAKE_CASE		     =       vqa_pipeline(image=UpperCAmelCase__    ,    question=UpperCAmelCase__    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__    ,    decimals=4     )    ,    [{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]     )

						__SCREAMING_SNAKE_CASE		     =       vqa_pipeline({"image": image, "question": question}    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__    ,    decimals=4     )    ,    [{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]     )

						__SCREAMING_SNAKE_CASE		     =       vqa_pipeline(
						    [{"image": image, "question": question}, {"image": image, "question": question}]    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__    ,    decimals=4     )    ,    [[{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2    ,    )




	@require_tf
	@unittest.skip("Visual question answering not implemented in TF"     )
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Optional[Any]:
						pass
