

'''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 	lowerCAmelCase  :







              @staticmethod
              def       snake_case (				*__lowercase      :			int    ,   **__lowercase      :			str   ):







                   """simple docstring"""







                   pass


@is_pipeline_test
@require_torch
@require_vision
class 	lowerCAmelCase  (   unittest.TestCase ):
       lowerCAmelCase_												=			MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Any   ):







            """simple docstring"""







            __lowercase      		=pipeline('visual-question-answering'    ,   model='hf-internal-testing/tiny-vilt-random-vqa'   )
            __lowercase      		=[
                {
                    '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       snake_case (				self      :			Tuple    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=vqa_pipeline(__lowercase    ,   top_k=1   )
            self.assertEqual(
                __lowercase    ,   [
                    [{'score': ANY(__lowercase   ), 'answer': ANY(__lowercase   )}],
                    [{'score': ANY(__lowercase   ), 'answer': ANY(__lowercase   )}],
                ]    ,   )







       @require_torch
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=pipeline('visual-question-answering'    ,   model='hf-internal-testing/tiny-vilt-random-vqa'   )
            __lowercase      		='./tests/fixtures/tests_samples/COCO/000000039769.png'
            __lowercase      		='How many cats are there?'

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

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







       @slow
       @require_torch
       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            __lowercase      		=pipeline('visual-question-answering'    ,   model='dandelin/vilt-b32-finetuned-vqa'   )
            __lowercase      		='./tests/fixtures/tests_samples/COCO/000000039769.png'
            __lowercase      		='How many cats are there?'

            __lowercase      		=vqa_pipeline(image=__lowercase    ,   question=__lowercase    ,   top_k=2   )
            self.assertEqual(
                nested_simplify(__lowercase    ,   decimals=4   )    ,   [{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]   )

            __lowercase      		=vqa_pipeline({'image': image, 'question': question}    ,   top_k=2   )
            self.assertEqual(
                nested_simplify(__lowercase    ,   decimals=4   )    ,   [{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]   )

            __lowercase      		=vqa_pipeline(
                [{'image': image, 'question': question}, {'image': image, 'question': question}]    ,   top_k=2   )
            self.assertEqual(
                nested_simplify(__lowercase    ,   decimals=4   )    ,   [[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]] * 2    ,   )







       @require_tf
       @unittest.skip('Visual question answering not implemented in TF'   )
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            pass





'''simple docstring'''

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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

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

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   __lowercase    ,   tokenizer_file=__lowercase    ,   unk_token=__lowercase    ,   bos_token=__lowercase    ,   eos_token=__lowercase    ,   add_prefix_space=__lowercase    ,   **__lowercase    ,   )

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

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







            __lowercase      		=self._tokenizer.model.save(__lowercase    ,   name=__lowercase   )
            return tuple(__lowercase   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







            __lowercase      		=[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowercase    ,   add_special_tokens=__lowercase   ) + [self.eos_token_id]   )

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


