

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
					from PIL import Image





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  str		     =							"dandelin/vilt-b32-finetuned-vqa"
			SCREAMING_SNAKE_CASE	:  Any		     =							(
			    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
			    "image containing the information, as well as a `question` which should be the question in English. It "
			    "returns a text that is the answer to the question."
			)
			SCREAMING_SNAKE_CASE	:  Tuple		     =							"image_qa"
			SCREAMING_SNAKE_CASE	:  str		     =							AutoProcessor
			SCREAMING_SNAKE_CASE	:  int		     =							AutoModelForVisualQuestionAnswering

			SCREAMING_SNAKE_CASE	:  str		     =							["image", "text"]
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							["text"]







			def __init__(	self					:							List[Any]				,  *_UpperCamelCase					:							Dict				,  **_UpperCamelCase					:							List[Any]				) ->int:
									requires_backends(self				,  ['''vision''']				)
									super().__init__(*_UpperCamelCase				,  **_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							"Image"				,  _UpperCamelCase					:							str				) ->Union[str, Any]:
									return self.pre_processor(_UpperCamelCase				,  _UpperCamelCase				,  return_tensors='''pt'''				)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							Union[str, Any]				) ->str:
									with torch.no_grad():
															return self.model(**_UpperCamelCase				).logits







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							Dict				) ->Any:
									snake_case_  =      outputs.argmax(-1				).item()
									return self.model.config.idalabel[idx]







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





