



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    _lowerCamelCase	(						*A       :  Any			,		**A       :  Any)       ->   List[str]:



													"""simple docstring"""






													pass







@is_pipeline_test
@require_torch
@require_vision
class       __lowerCAmelCase   (      unittest.TestCase						):
			UpperCamelCase        =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  int			,		A       :  str			,		A       :  Optional[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       pipeline('visual-question-answering'			,		model='hf-internal-testing/tiny-vilt-random-vqa')
						_UpperCAmelCase			=       [
						    {
						        '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       :  Dict			,		A       :  Any			,		A       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






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



			@require_torch
			def    _lowerCamelCase	(						self       :  Tuple)       ->   int:



						"""simple docstring"""






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

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

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



			@slow
			@require_torch
			def    _lowerCamelCase	(						self       :  Any)       ->   Union[str, Any]:



						"""simple docstring"""






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

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

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

						_UpperCAmelCase			=       vqa_pipeline(
						    [{'image': image, 'question': question}, {'image': image, 'question': question}]			,		top_k=2)
						self.assertEqual(
						    nested_simplify(A			,		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    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



						"""simple docstring"""






						pass





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

						super().__init__(bos_token_id=A			,		eos_token_id=A			,		**A)
