



"""simple docstring"""


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

snake_case_								=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def 					_lowerCAmelCase (  lowercase_		):
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		UpperCAmelCase			 =					model_type_to_module_name(lowercase_		)

																		UpperCAmelCase			 =					importlib.import_module(F""".{module_name}"""						,      'transformers.models'		)
																		try:
																								return getattr(lowercase_						,      lowercase_		)
																		except AttributeError:
																								continue

						for processor in PROCESSOR_MAPPING._extra_content.values():
												if getattr(lowercase_						,      '__name__'						,      lowercase_		) == class_name:
																		return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						UpperCAmelCase			 =					importlib.import_module('transformers'		)
						if hasattr(lowercase_						,      lowercase_		):
												return getattr(lowercase_						,      lowercase_		)

						return None







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]   )							->     str:
												raise EnvironmentError(
												    'AutoProcessor is designed to be instantiated '
												    'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'   )






						@classmethod
						@replace_list_option_in_docstrings(lowercase_   )
						def       UpperCAmelCase__	(   cls					:Optional[int]  ,    lowercase_					:Tuple  ,    **lowercase_					:int   )							->     Optional[int]:
												UpperCAmelCase			 =					kwargs.pop('config'  ,    lowercase_   )
												UpperCAmelCase			 =					kwargs.pop('trust_remote_code'  ,    lowercase_   )
												UpperCAmelCase			 =					True

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

												# First, let's see if we have a preprocessor config.
												# Filter the kwargs for `get_file_from_repo`.
												UpperCAmelCase			 =					{
												    key: kwargs[key] for key in inspect.signature(lowercase_   ).parameters.keys() if key in kwargs
												}
												# Let's start by checking whether the processor class is saved in an image processor
												UpperCAmelCase			 =					get_file_from_repo(
												    lowercase_  ,    lowercase_  ,    **lowercase_   )
												if preprocessor_config_file is not None:
																		UpperCAmelCase ,       UpperCAmelCase			 =					ImageProcessingMixin.get_image_processor_dict(lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																		if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																								UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
												if preprocessor_config_file is not None and processor_class is None:
																		UpperCAmelCase ,       UpperCAmelCase			 =					FeatureExtractionMixin.get_feature_extractor_dict(lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																		if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																								UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

												if processor_class is None:
																		# Next, let's check whether the processor class is saved in a tokenizer
																		UpperCAmelCase			 =					get_file_from_repo(
																		    lowercase_  ,    lowercase_  ,    **lowercase_   )
																		if tokenizer_config_file is not None:
																								with open(lowercase_  ,    encoding='utf-8'   ) as reader:
																														UpperCAmelCase			 =					json.load(lowercase_   )

																								UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																								if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																														UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

												if processor_class is None:
																		# Otherwise, load config, if it can be loaded.
																		if not isinstance(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					AutoConfig.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )

																		# And check if the config contains the processor class.
																		UpperCAmelCase			 =					getattr(lowercase_  ,    'processor_class'  ,    lowercase_   )
																		if hasattr(lowercase_  ,    'auto_map'   ) and "AutoProcessor" in config.auto_map:
																								UpperCAmelCase			 =					config.auto_map['AutoProcessor']

												if processor_class is not None:
																		UpperCAmelCase			 =					processor_class_from_name(lowercase_   )

												UpperCAmelCase			 =					processor_auto_map is not None
												UpperCAmelCase			 =					processor_class is not None or type(lowercase_   ) in PROCESSOR_MAPPING
												UpperCAmelCase			 =					resolve_trust_remote_code(
												    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )

												if has_remote_code and trust_remote_code:
																		UpperCAmelCase			 =					get_class_from_dynamic_module(
																		    lowercase_  ,    lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					kwargs.pop('code_revision'  ,    lowercase_   )
																		if os.path.isdir(lowercase_   ):
																								processor_class.register_for_auto_class()
																		return processor_class.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												elif processor_class is not None:
																		return processor_class.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												# Last try: we use the PROCESSOR_MAPPING.
												elif type(lowercase_   ) in PROCESSOR_MAPPING:
																		return PROCESSOR_MAPPING[type(lowercase_   )].from_pretrained(lowercase_  ,    **lowercase_   )

												# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
												# tokenizer.
												try:
																		return AutoTokenizer.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												except Exception:
																		try:
																								return AutoImageProcessor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		except Exception:
																								pass

																		try:
																								return AutoFeatureExtractor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		except Exception:
																								pass

												raise ValueError(
												    f"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
												    'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
												    'the files of at least one of those processing classes.'   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:int  ,    lowercase_					:Union[str, Any]   )							->     Tuple:
												PROCESSOR_MAPPING.register(lowercase_  ,    lowercase_   )











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  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			if not inplace:
						UpperCamelCase_     				= ma + ma
			else:
						ma += ma

			return ma




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= -1 * len(__snake_case					)
			UpperCamelCase_     				= list(range(len(tensor.shape[:zero_index]					)					)					)
			return tensor.permute(first_inds + [zero_index + i for i in inds]					)




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


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




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case = 1E-4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= mask.expand(*value.shape					)
			return torch.sum(mask * value     ,		dim=__snake_case					) / (eps + torch.sum(__snake_case     ,		dim=__snake_case					))




def  a_		(							__snake_case     ,		__snake_case = 2.31_25     ,		__snake_case = 21.68_75     ,		__snake_case = 6_4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= torch.linspace(__snake_case     ,		__snake_case     ,		no_bins - 1     ,		device=pts.device					)
			UpperCamelCase_     				= torch.sqrt(torch.sum((pts.unsqueeze(-2					) - pts.unsqueeze(-3					)) ** 2     ,		dim=-1					)					)
			return torch.bucketize(__snake_case     ,		__snake_case					)




def  a_		(							__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			UpperCamelCase_     				= dicts[0]
			UpperCamelCase_     				= {}
			for k, v in first.items():
						UpperCamelCase_     				= [d[k] for d in dicts]
						if isinstance(__snake_case     ,		__snake_case					):
									UpperCamelCase_     				= dict_multimap(__snake_case     ,		__snake_case					)
						else:
									UpperCamelCase_     				= fn(__snake_case					)

			return new_dict




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= v_bins.view(((1,) * len(x.shape					)) + (len(__snake_case					),)					)
			UpperCamelCase_     				= x[..., None] - reshaped_bins
			UpperCamelCase_     				= torch.argmin(torch.abs(__snake_case					)     ,		dim=-1					)
			return nn.functional.one_hot(__snake_case     ,		num_classes=len(__snake_case					)					).float()




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case = 0     ,		__snake_case = 0					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= []
			for i, s in enumerate(data.shape[:no_batch_dims]					):
						UpperCamelCase_     				= torch.arange(__snake_case					)
						UpperCamelCase_     				= r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape					) - i - 1)))					)
						ranges.append(__snake_case					)

			UpperCamelCase_     				= [slice(__snake_case					) for _ in range(len(data.shape					) - no_batch_dims					)]
			UpperCamelCase_     				= inds
			ranges.extend(__snake_case					)
			# 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(__snake_case					)]


__a			:	List[Any]							     =		TypeVar("""T""")




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Dict[Any, Union[dict, list, tuple, Any]]:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			for k, v in dic.items():
						if isinstance(__snake_case     ,		__snake_case					):
									UpperCamelCase_     				= dict_map(__snake_case     ,		__snake_case     ,		__snake_case					)
						else:
									UpperCamelCase_     				= tree_map(__snake_case     ,		__snake_case     ,		__snake_case					)

			return new_dict




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			list:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			tuple:

			'''simple docstring'''


			...




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


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





__a			:	Tuple							     =		partial(tree_map, leaf_type=torch.Tensor)
