

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


A											=     logging.get_logger(__name__)

A											=     OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

A											=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)





def a(lowercase__	):





	'''simple docstring'''






	for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
		if class_name in extractors:
			snake_case_      		= model_type_to_module_name(__SCREAMING_SNAKE_CASE	)

			snake_case_      		= importlib.import_module(f""".{module_name}"""			, 'transformers.models'	)
			try:
				return getattr(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE	)
			except AttributeError:
				continue

	for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
		if getattr(__SCREAMING_SNAKE_CASE			, '__name__'			, __SCREAMING_SNAKE_CASE	) == class_name:
			return extractor

    # 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.
	snake_case_      		= importlib.import_module('transformers'	)
	if hasattr(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE	):
		return getattr(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE	)

	return None





def a(lowercase__			, lowercase__ = None			, lowercase__ = False			, lowercase__ = False			, lowercase__ = None			, lowercase__ = None			, lowercase__ = None			, lowercase__ = False			, **lowercase__			, ):





	'''simple docstring'''
	snake_case_      		= get_file_from_repo(
	    __SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, cache_dir=__SCREAMING_SNAKE_CASE			, force_download=__SCREAMING_SNAKE_CASE			, resume_download=__SCREAMING_SNAKE_CASE			, proxies=__SCREAMING_SNAKE_CASE			, use_auth_token=__SCREAMING_SNAKE_CASE			, revision=__SCREAMING_SNAKE_CASE			, local_files_only=__SCREAMING_SNAKE_CASE			, )
	if resolved_config_file is None:
		logger.info(
		    'Could not locate the image processor configuration file, will try to use the model config instead.'	)
		return {}

	with open(__SCREAMING_SNAKE_CASE			, encoding='utf-8'	) as reader:
		return json.load(__SCREAMING_SNAKE_CASE	)






class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self							):

				"""simple docstring"""


				raise EnvironmentError(
				    'AutoImageProcessor is designed to be instantiated '
				    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'							)


			@classmethod
			@replace_list_option_in_docstrings(lowercase_							)
			def 			__lowerCAmelCase (    cls ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= kwargs.pop('config' ,				lowercase_							)
				snake_case_      		= kwargs.pop('trust_remote_code' ,				lowercase_							)
				snake_case_      		= True

				snake_case_      		= ImageProcessingMixin.get_image_processor_dict(lowercase_ ,				**lowercase_							)
				snake_case_      		= config_dict.get('image_processor_type' ,				lowercase_							)
				snake_case_      		= None
				if "AutoImageProcessor" in config_dict.get('auto_map' ,				{}							):
					snake_case_      		= config_dict["auto_map"]["AutoImageProcessor"]

				# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
				# and if so, infer the image processor class from there.
				if image_processor_class is None and image_processor_auto_map is None:
					snake_case_      		= config_dict.pop('feature_extractor_type' ,				lowercase_							)
					if feature_extractor_class is not None:
						logger.warning(
						    'Could not find image processor class in the image processor config or the model config. Loading'
						    ' based on pattern matching with the model\'s feature extractor configuration.'							)
						snake_case_      		= feature_extractor_class.replace('FeatureExtractor' ,				'ImageProcessor'							)
					if "AutoFeatureExtractor" in config_dict.get('auto_map' ,				{}							):
						snake_case_      		= config_dict["auto_map"]["AutoFeatureExtractor"]
						snake_case_      		= feature_extractor_auto_map.replace('FeatureExtractor' ,				'ImageProcessor'							)
						logger.warning(
						    'Could not find image processor auto map in the image processor config or the model config.'
						    ' Loading based on pattern matching with the model\'s feature extractor configuration.'							)

        # If we don't find the image processor class in the image processor config, let's try the model config.
				if image_processor_class is None and image_processor_auto_map is None:
					if not isinstance(lowercase_ ,				lowercase_							):
						snake_case_      		= AutoConfig.from_pretrained(lowercase_ ,				**lowercase_							)
					# It could be in `config.image_processor_type``
					snake_case_      		= getattr(lowercase_ ,				'image_processor_type' ,				lowercase_							)
					if hasattr(lowercase_ ,				'auto_map'							) and "AutoImageProcessor" in config.auto_map:
						snake_case_      		= config.auto_map["AutoImageProcessor"]

				if image_processor_class is not None:
					snake_case_      		= image_processor_class_from_name(lowercase_							)

				snake_case_      		= image_processor_auto_map is not None
				snake_case_      		= image_processor_class is not None or type(lowercase_							) in IMAGE_PROCESSOR_MAPPING
				snake_case_      		= resolve_trust_remote_code(
				    lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_							)

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

				raise ValueError(
				    f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
				    f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
				    f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()							)}"""							)







			@staticmethod
			def 			__lowerCAmelCase (    __UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				IMAGE_PROCESSOR_MAPPING.register(lowercase_ ,				lowercase_							)



import functools
import operator

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


A											=     logging.get_logger(__name__)

A											=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """unispeech-sat"""


			def __init__(    self ,				__UpperCamelCase=32 ,				__UpperCamelCase=7_68 ,				__UpperCamelCase=12 ,				__UpperCamelCase=12 ,				__UpperCamelCase=30_72 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase="group" ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,				__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,				__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,				__UpperCamelCase=False ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=16 ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.05 ,				__UpperCamelCase=10 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0 ,				__UpperCamelCase=3_20 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=1_00 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="mean" ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) ,				__UpperCamelCase=(5, 3, 3, 1, 1) ,				__UpperCamelCase=(1, 2, 3, 1, 1) ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=0 ,				__UpperCamelCase=1 ,				__UpperCamelCase=2 ,				__UpperCamelCase=5_04 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase ,				pad_token_id=__UpperCamelCase ,				bos_token_id=__UpperCamelCase ,				eos_token_id=__UpperCamelCase							)
				snake_case_      		= hidden_size
				snake_case_      		= feat_extract_norm
				snake_case_      		= feat_extract_activation
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= conv_bias
				snake_case_      		= num_conv_pos_embeddings
				snake_case_      		= num_conv_pos_embedding_groups
				snake_case_      		= len(self.conv_dim							)
				snake_case_      		= num_hidden_layers
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_dropout
				snake_case_      		= attention_dropout
				snake_case_      		= activation_dropout
				snake_case_      		= feat_proj_dropout
				snake_case_      		= final_dropout
				snake_case_      		= layerdrop
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= vocab_size
				snake_case_      		= num_clusters
				snake_case_      		= do_stable_layer_norm
				snake_case_      		= use_weighted_layer_sum

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
					    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
					    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				snake_case_      		= apply_spec_augment
				snake_case_      		= mask_time_prob
				snake_case_      		= mask_time_length
				snake_case_      		= mask_time_min_masks
				snake_case_      		= mask_feature_prob
				snake_case_      		= mask_feature_length
				snake_case_      		= mask_feature_min_masks

				# parameters for pretraining with codevector quantized representations
				snake_case_      		= num_codevectors_per_group
				snake_case_      		= num_codevector_groups
				snake_case_      		= contrastive_logits_temperature
				snake_case_      		= feat_quantizer_dropout
				snake_case_      		= num_negatives
				snake_case_      		= codevector_dim
				snake_case_      		= proj_codevector_dim
				snake_case_      		= diversity_loss_weight

				# ctc loss
				snake_case_      		= ctc_loss_reduction
				snake_case_      		= ctc_zero_infinity

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				snake_case_      		= classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= xvector_output_dim







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return functools.reduce(operator.mul ,				self.conv_stride ,				1							)
