




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE__			: Tuple    =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__			: Union[str, Any]    =  {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}




class 						UpperCamelCase__      (SCREAMING_SNAKE_CASE_	):

    '''simple docstring'''



    lowerCamelCase_ :      Any      	= 't5'
    lowerCamelCase_ :      str      	= ['past_key_values']
    lowerCamelCase_ :      Tuple      	= {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







    def __init__(       self	,    UpperCamelCase__=3_2128	,    UpperCamelCase__=512	,    UpperCamelCase__=64	,    UpperCamelCase__=2048	,    UpperCamelCase__=6	,    UpperCamelCase__=None	,    UpperCamelCase__=8	,    UpperCamelCase__=32	,    UpperCamelCase__=128	,    UpperCamelCase__=0.1	,    UpperCamelCase__=1e-6	,    UpperCamelCase__=1.0	,    UpperCamelCase__="relu"	,    UpperCamelCase__=True	,    UpperCamelCase__=True	,    UpperCamelCase__=0	,    UpperCamelCase__=1	,    **UpperCamelCase__	,    )	-> int:
           lowerCamelCase  : Dict     					=   vocab_size
           lowerCamelCase  : Optional[Any]     					=   d_model
           lowerCamelCase  : Optional[Any]     					=   d_kv
           lowerCamelCase  : Union[str, Any]     					=   d_ff
           lowerCamelCase  : Tuple     					=   num_layers
           lowerCamelCase  : Tuple     					=   (
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           lowerCamelCase  : Tuple     					=   num_heads
           lowerCamelCase  : List[Any]     					=   relative_attention_num_buckets
           lowerCamelCase  : Optional[Any]     					=   relative_attention_max_distance
           lowerCamelCase  : Optional[int]     					=   dropout_rate
           lowerCamelCase  : str     					=   layer_norm_epsilon
           lowerCamelCase  : Union[str, Any]     					=   initializer_factor
           lowerCamelCase  : str     					=   feed_forward_proj
           lowerCamelCase  : List[Any]     					=   use_cache

           lowerCamelCase  : List[Any]     					=   self.feed_forward_proj.split("-"	)
           lowerCamelCase  : Union[str, Any]     					=   act_info[-1]
           lowerCamelCase  : Dict     					=   act_info[0] == 'gated'

           if len(UpperCamelCase__	) > 1 and act_info[0] != "gated" or len(UpperCamelCase__	) > 2:
                  raise ValueError(
                      F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                      "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                      "\'gated-gelu\' or \'relu\'"	)

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                  lowerCamelCase  : Optional[Any]     					=   'gelu_new'

           super().__init__(
               pad_token_id=UpperCamelCase__	,    eos_token_id=UpperCamelCase__	,    is_encoder_decoder=UpperCamelCase__	,    **UpperCamelCase__	,    )




class 						UpperCamelCase__      (SCREAMING_SNAKE_CASE_	):

    '''simple docstring'''



    @property
    def        _lowercase	(       self	)	-> int:
           lowerCamelCase  : Union[str, Any]     					=   {
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                  lowerCamelCase  : int     					=   'past_encoder_sequence + sequence'
                  lowerCamelCase  : Union[str, Any]     					=   {0: 'batch'}
                  lowerCamelCase  : Dict     					=   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                  lowerCamelCase  : List[Any]     					=   {0: 'batch', 1: 'decoder_sequence'}
                  lowerCamelCase  : int     					=   {0: 'batch', 1: 'decoder_sequence'}

           if self.use_past:
                  self.fill_with_past_key_values_(UpperCamelCase__	,    direction="inputs"	)

           return common_inputs







    @property
    def        _lowercase	(       self	)	-> List[Any]:
           return 13











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 						lowercase    :
									'''simple docstring'''


									@staticmethod
									def       lowerCamelCase_				(  *snake_case    :				Dict       ,	**snake_case    :				List[Any]	):



														'''simple docstring'''


														pass


@is_pipeline_test
@require_torch
@require_vision
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Any		=		MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







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



											'''simple docstring'''


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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vqa_pipeline(snake_case       ,	top_k=1	)
											self.assertEqual(
											    snake_case       ,	[
											        [{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}],
											        [{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}],
											    ]       ,	)







						@require_torch
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


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

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

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







						@slow
						@require_torch
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				pipeline('visual-question-answering'       ,	model='dandelin/vilt-b32-finetuned-vqa'	)
											SCREAMING_SNAKE_CASE							:     int						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
											SCREAMING_SNAKE_CASE							:     Dict						=				'How many cats are there?'

											SCREAMING_SNAKE_CASE							:     int						=				vqa_pipeline(image=snake_case       ,	question=snake_case       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				vqa_pipeline({'image': image, 'question': question}       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]	)

											SCREAMING_SNAKE_CASE							:     Any						=				vqa_pipeline(
											    [{'image': image, 'question': question}, {'image': image, 'question': question}]       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2       ,	)







						@require_tf
						@unittest.skip('Visual question answering not implemented in TF'	)
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											pass