




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types






def    snake_case   (				A		,  A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	if not inplace:
		lowerCamelCase_				:						int				  =				ma + ma
	else:
		ma += ma

	return ma






def    snake_case   (				A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						Union[str, Any]				  =				-1 * len(A     )
	lowerCamelCase_				:						List[Any]				  =				list(range(len(tensor.shape[:zero_index]     )     )     )
	return tensor.permute(first_inds + [zero_index + i for i in inds]     )






def    snake_case   (				A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	return t.reshape(t.shape[:-no_dims] + (-1,)     )






def    snake_case   (				A		,  A		,  A		,  A = 1e-4     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[Any]				  =				mask.expand(*value.shape     )
	return torch.sum(mask * value		,  dim=A     ) / (eps + torch.sum(A		,  dim=A     ))






def    snake_case   (				A		,  A = 2.3125		,  A = 21.6875		,  A = 64     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				torch.linspace(A		,  A		,  no_bins - 1		,  device=pts.device     )
	lowerCamelCase_				:						str				  =				torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2		,  dim=-1     )     )
	return torch.bucketize(A		,  A     )






def    snake_case   (				A		,  A     )				->				dict:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				dicts[0]
	lowerCamelCase_				:						Union[str, Any]				  =				{}
	for k, v in first.items():
		lowerCamelCase_				:						List[str]				  =				[d[k] for d in dicts]
		if isinstance(A		,  A     ):
			lowerCamelCase_				:						Union[str, Any]				  =				dict_multimap(A		,  A     )
		else:
			lowerCamelCase_				:						Any				  =				fn(A     )

	return new_dict






def    snake_case   (				A		,  A     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						str				  =				v_bins.view(((1,) * len(x.shape     )) + (len(A     ),)     )
	lowerCamelCase_				:						int				  =				x[..., None] - reshaped_bins
	lowerCamelCase_				:						List[Any]				  =				torch.argmin(torch.abs(A     )		,  dim=-1     )
	return nn.functional.one_hot(A		,  num_classes=len(A     )     ).float()






def    snake_case   (				A		,  A		,  A = 0		,  A = 0     )				->				torch.Tensor:




	'''simple docstring'''


	lowerCamelCase_				:						List[Union[slice, torch.Tensor]]				  =				[]
	for i, s in enumerate(data.shape[:no_batch_dims]     ):
		lowerCamelCase_				:						Dict				  =				torch.arange(A     )
		lowerCamelCase_				:						int				  =				r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
		ranges.append(A     )

	lowerCamelCase_				:						List[Union[slice, torch.Tensor]]				  =				[slice(A     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
	lowerCamelCase_				:						Any				  =				inds
	ranges.extend(A     )
	# Matt note: Editing this to get around the behaviour of using a list as an array index changing
	# in recent Numpy versions
	return data[tuple(A     )]


UpperCamelCase__						=		TypeVar('T')






def    snake_case   (				A		,  A		,  A     )				->				Dict[Any, Union[dict, list, tuple, Any]]:




	'''simple docstring'''


	lowerCamelCase_				:						Dict[Any, Union[dict, list, tuple, Any]]				  =				{}
	for k, v in dic.items():
		if isinstance(A		,  A     ):
			lowerCamelCase_				:						str				  =				dict_map(A		,  A		,  A     )
		else:
			lowerCamelCase_				:						int				  =				tree_map(A		,  A		,  A     )

	return new_dict






@overload
def    snake_case   (				A		,  A		,  A     )				->				Any:




	'''simple docstring'''


	...






@overload
def    snake_case   (				A		,  A		,  A     )				->				dict:




	'''simple docstring'''


	...






@overload
def    snake_case   (				A		,  A		,  A     )				->				list:




	'''simple docstring'''


	...






@overload
def    snake_case   (				A		,  A		,  A     )				->				tuple:




	'''simple docstring'''


	...






def    snake_case   (				A		,  A		,  A     )				->				Optional[int]:




	'''simple docstring'''


	if isinstance(A		,  A     ):
		return dict_map(A		,  A		,  A     )
	elif isinstance(A		,  A     ):
		return [tree_map(A		,  A		,  A     ) for x in tree]
	elif isinstance(A		,  A     ):
		return tuple(tree_map(A		,  A		,  A     ) for x in tree     )
	elif isinstance(A		,  A     ):
		return fn(A     )
	else:
		print(type(A     )     )
		raise ValueError('Not supported'     )



UpperCamelCase__						=		partial(tree_map, leaf_type=torch.Tensor)


# 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 ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__				=							{
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__				=							["""DocumentQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageCaptioningTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageSegmentationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""SpeechToTextTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextClassificationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextSummarizationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextToSpeechTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							SCREAMING_SNAKE_CASE__				=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





