


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


lowerCAmelCase				:Optional[int]        =      4
lowerCAmelCase				:Any        =      3
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		pass







def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	for shard in shards:
		for i in range(lowercase__		):
			yield {"i": i, "shard": shard}





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   str					     =					int(os.environ['RANK']		)
	__magic_name__	:   Tuple					     =					int(os.environ['WORLD_SIZE']		)

	__magic_name__	:   List[str]					     =					ArgumentParser()
	parser.add_argument('--streaming'		,		type=lowercase__		)
	parser.add_argument('--local_rank'		,		type=lowercase__		)
	parser.add_argument('--num_workers'		,		type=lowercase__		,		default=0		)
	__magic_name__	:   str					     =					parser.parse_args()
	__magic_name__	:   Any					     =					args.streaming
	__magic_name__	:   Tuple					     =					args.num_workers

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

	__magic_name__	:   str					     =					split_dataset_by_node(lowercase__		,		rank=lowercase__		,		world_size=lowercase__		)
	__magic_name__	:   Optional[Any]					     =					torch.utils.data.DataLoader(lowercase__		,		num_workers=lowercase__		)

	__magic_name__	:   Union[str, Any]					     =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
	__magic_name__	:   List[str]					     =					full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)		)

	__magic_name__	:   Optional[Any]					     =					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()



'''simple docstring'''

# 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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
		    """image to caption, and returns a text that contains the description in English."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







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







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()