



"""simple docstring"""


import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_ = 4096		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					key.shape[-3:]
						UpperCAmelCase			 =					value.shape[-1]
						UpperCAmelCase			 =					min(lowercase_						,      lowercase_		)
						UpperCAmelCase			 =					query / jnp.sqrt(lowercase_		)

						@functools.partial(jax.checkpoint						,      prevent_cse=lowercase_		)
						def summarize_chunk(lowercase_						,      lowercase_						,      lowercase_		):
												UpperCAmelCase			 =					jnp.einsum('...qhd,...khd->...qhk'						,      lowercase_						,      lowercase_						,      precision=lowercase_		)

												UpperCAmelCase			 =					jnp.max(lowercase_						,      axis=-1						,      keepdims=lowercase_		)
												UpperCAmelCase			 =					jax.lax.stop_gradient(lowercase_		)
												UpperCAmelCase			 =					jnp.exp(attn_weights - max_score		)

												UpperCAmelCase			 =					jnp.einsum('...vhf,...qhv->...qhf'						,      lowercase_						,      lowercase_						,      precision=lowercase_		)
												UpperCAmelCase			 =					jnp.einsum('...qhk->...qh'						,      lowercase_		)

												return (exp_values, exp_weights.sum(axis=-1		), max_score)

						def chunk_scanner(lowercase_		):
												# julienne key array
												UpperCAmelCase			 =					jax.lax.dynamic_slice(
												    operand=lowercase_						,      start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]						,      slice_sizes=list(key.shape[:-3]		) + [key_chunk_size, num_heads, k_features]						,      )

												# julienne value array
												UpperCAmelCase			 =					jax.lax.dynamic_slice(
												    operand=lowercase_						,      start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]						,      slice_sizes=list(value.shape[:-3]		) + [key_chunk_size, num_heads, v_features]						,      )

												return summarize_chunk(lowercase_						,      lowercase_						,      lowercase_		)

						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					jax.lax.map(f=lowercase_						,      xs=jnp.arange(0						,      lowercase_						,      lowercase_		)		)

						UpperCAmelCase			 =					jnp.max(lowercase_						,      axis=0						,      keepdims=lowercase_		)
						UpperCAmelCase			 =					jnp.exp(chunk_max - global_max		)

						chunk_values *= jnp.expand_dims(lowercase_						,      axis=-1		)
						chunk_weights *= max_diffs

						UpperCAmelCase			 =					chunk_values.sum(axis=0		)
						UpperCAmelCase			 =					jnp.expand_dims(lowercase_						,      -1		).sum(axis=0		)

						return all_values / all_weights

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_=jax.lax.Precision.HIGHEST						,      lowercase_ = 1024						,      lowercase_ = 4096		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					query.shape[-3:]

						def chunk_scanner(lowercase_						,      lowercase_		):
												# julienne query array
												UpperCAmelCase			 =					jax.lax.dynamic_slice(
												    operand=lowercase_						,      start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]						,      slice_sizes=list(query.shape[:-3]		) + [min(lowercase_						,      lowercase_		), num_heads, q_features]						,      )

												return (
												    chunk_idx + query_chunk_size,  # unused ignore it
												    _query_chunk_attention(
												        query=lowercase_						,      key=lowercase_						,      value=lowercase_						,      precision=lowercase_						,      key_chunk_size=lowercase_		),
												)

						UpperCAmelCase ,       UpperCAmelCase			 =					jax.lax.scan(
						    f=lowercase_						,      init=0						,      xs=lowercase_						,      length=math.ceil(num_q / query_chunk_size		)		)

						return jnp.concatenate(lowercase_						,      axis=-3		)  # fuse the chunked result back







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				8
						__UpperCamelCase													=				64
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Any   )							->     Dict:
												UpperCAmelCase			 =					self.dim_head * self.heads
												UpperCAmelCase			 =					self.dim_head**-0.5

												# Weights were exported with old names {to_q, to_k, to_v, to_out}
												UpperCAmelCase			 =					nn.Dense(lowercase_  ,    use_bias=lowercase_  ,    dtype=self.dtype  ,    name='to_q'   )
												UpperCAmelCase			 =					nn.Dense(lowercase_  ,    use_bias=lowercase_  ,    dtype=self.dtype  ,    name='to_k'   )
												UpperCAmelCase			 =					nn.Dense(lowercase_  ,    use_bias=lowercase_  ,    dtype=self.dtype  ,    name='to_v'   )

												UpperCAmelCase			 =					nn.Dense(self.query_dim  ,    dtype=self.dtype  ,    name='to_out_0'   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:str   )							->     str:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					tensor.shape
												UpperCAmelCase			 =					self.heads
												UpperCAmelCase			 =					tensor.reshape(lowercase_  ,    lowercase_  ,    lowercase_  ,    dim // head_size   )
												UpperCAmelCase			 =					jnp.transpose(lowercase_  ,    (0, 2, 1, 3)   )
												UpperCAmelCase			 =					tensor.reshape(batch_size * head_size  ,    lowercase_  ,    dim // head_size   )
												return tensor






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Any   )							->     Optional[Any]:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					tensor.shape
												UpperCAmelCase			 =					self.heads
												UpperCAmelCase			 =					tensor.reshape(batch_size // head_size  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					jnp.transpose(lowercase_  ,    (0, 2, 1, 3)   )
												UpperCAmelCase			 =					tensor.reshape(batch_size // head_size  ,    lowercase_  ,    dim * head_size   )
												return tensor






						def __call__(   self					:Optional[int]  ,    lowercase_					:int  ,    lowercase_					:Tuple=None  ,    lowercase_					:List[Any]=True   )							->     Optional[int]:
												UpperCAmelCase			 =					hidden_states if context is None else context

												UpperCAmelCase			 =					self.query(lowercase_   )
												UpperCAmelCase			 =					self.key(lowercase_   )
												UpperCAmelCase			 =					self.value(lowercase_   )

												UpperCAmelCase			 =					self.reshape_heads_to_batch_dim(lowercase_   )
												UpperCAmelCase			 =					self.reshape_heads_to_batch_dim(lowercase_   )
												UpperCAmelCase			 =					self.reshape_heads_to_batch_dim(lowercase_   )

												if self.use_memory_efficient_attention:
																		UpperCAmelCase			 =					query_states.transpose(1  ,    0  ,    2   )
																		UpperCAmelCase			 =					key_states.transpose(1  ,    0  ,    2   )
																		UpperCAmelCase			 =					value_states.transpose(1  ,    0  ,    2   )

																		# this if statement create a chunk size for each layer of the unet
																		# the chunk size is equal to the query_length dimension of the deepest layer of the unet

																		UpperCAmelCase			 =					query_states.shape[-3]
																		if flatten_latent_dim % 64 == 0:
																								UpperCAmelCase			 =					int(flatten_latent_dim / 64   )
																		elif flatten_latent_dim % 16 == 0:
																								UpperCAmelCase			 =					int(flatten_latent_dim / 16   )
																		elif flatten_latent_dim % 4 == 0:
																								UpperCAmelCase			 =					int(flatten_latent_dim / 4   )
																		else:
																								UpperCAmelCase			 =					int(lowercase_   )

																		UpperCAmelCase			 =					jax_memory_efficient_attention(
																		    lowercase_  ,    lowercase_  ,    lowercase_  ,    query_chunk_size=lowercase_  ,    key_chunk_size=40_96 * 4   )

																		UpperCAmelCase			 =					hidden_states.transpose(1  ,    0  ,    2   )
												else:
																		# compute attentions
																		UpperCAmelCase			 =					jnp.einsum('b i d, b j d->b i j'  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					attention_scores * self.scale
																		UpperCAmelCase			 =					nn.softmax(lowercase_  ,    axis=2   )

																		# attend to values
																		UpperCAmelCase			 =					jnp.einsum('b i j, b j d -> b i d'  ,    lowercase_  ,    lowercase_   )

												UpperCAmelCase			 =					self.reshape_batch_dim_to_heads(lowercase_   )
												UpperCAmelCase			 =					self.proj_attn(lowercase_   )
												return self.dropout_layer(lowercase_  ,    deterministic=lowercase_   )







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Tuple:
												# self attention (or cross_attention if only_cross_attention is True)
												UpperCAmelCase			 =					FlaxAttention(
												    self.dim  ,    self.n_heads  ,    self.d_head  ,    self.dropout  ,    self.use_memory_efficient_attention  ,    dtype=self.dtype   )
												# cross attention
												UpperCAmelCase			 =					FlaxAttention(
												    self.dim  ,    self.n_heads  ,    self.d_head  ,    self.dropout  ,    self.use_memory_efficient_attention  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					FlaxFeedForward(dim=self.dim  ,    dropout=self.dropout  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.LayerNorm(epsilon=1E-5  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.LayerNorm(epsilon=1E-5  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.LayerNorm(epsilon=1E-5  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Tuple=True   )							->     Tuple:
												# self attention
												UpperCAmelCase			 =					hidden_states
												if self.only_cross_attention:
																		UpperCAmelCase			 =					self.attna(self.norma(lowercase_   )  ,    lowercase_  ,    deterministic=lowercase_   )
												else:
																		UpperCAmelCase			 =					self.attna(self.norma(lowercase_   )  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					hidden_states + residual

												# cross attention
												UpperCAmelCase			 =					hidden_states
												UpperCAmelCase			 =					self.attna(self.norma(lowercase_   )  ,    lowercase_  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					hidden_states + residual

												# feed forward
												UpperCAmelCase			 =					hidden_states
												UpperCAmelCase			 =					self.ff(self.norma(lowercase_   )  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					hidden_states + residual

												return self.dropout_layer(lowercase_  ,    deterministic=lowercase_   )







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				42
						__UpperCamelCase													=				1
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				jnp.floataa
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:int   )							->     Dict:
												UpperCAmelCase			 =					nn.GroupNorm(num_groups=32  ,    epsilon=1E-5   )

												UpperCAmelCase			 =					self.n_heads * self.d_head
												if self.use_linear_projection:
																		UpperCAmelCase			 =					nn.Dense(lowercase_  ,    dtype=self.dtype   )
												else:
																		UpperCAmelCase			 =					nn.Conv(
																		    lowercase_  ,    kernel_size=(1, 1)  ,    strides=(1, 1)  ,    padding='VALID'  ,    dtype=self.dtype  ,    )

												UpperCAmelCase			 =					[
												    FlaxBasicTransformerBlock(
												        lowercase_  ,    self.n_heads  ,    self.d_head  ,    dropout=self.dropout  ,    only_cross_attention=self.only_cross_attention  ,    dtype=self.dtype  ,    use_memory_efficient_attention=self.use_memory_efficient_attention  ,    )
												    for _ in range(self.depth   )
												]

												if self.use_linear_projection:
																		UpperCAmelCase			 =					nn.Dense(lowercase_  ,    dtype=self.dtype   )
												else:
																		UpperCAmelCase			 =					nn.Conv(
																		    lowercase_  ,    kernel_size=(1, 1)  ,    strides=(1, 1)  ,    padding='VALID'  ,    dtype=self.dtype  ,    )

												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:List[Any]  ,    lowercase_					:int  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[int]=True   )							->     int:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					hidden_states.shape
												UpperCAmelCase			 =					hidden_states
												UpperCAmelCase			 =					self.norm(lowercase_   )
												if self.use_linear_projection:
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    height * width  ,    lowercase_   )
																		UpperCAmelCase			 =					self.proj_in(lowercase_   )
												else:
																		UpperCAmelCase			 =					self.proj_in(lowercase_   )
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    height * width  ,    lowercase_   )

												for transformer_block in self.transformer_blocks:
																		UpperCAmelCase			 =					transformer_block(lowercase_  ,    lowercase_  ,    deterministic=lowercase_   )

												if self.use_linear_projection:
																		UpperCAmelCase			 =					self.proj_out(lowercase_   )
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
												else:
																		UpperCAmelCase			 =					hidden_states.reshape(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					self.proj_out(lowercase_   )

												UpperCAmelCase			 =					hidden_states + residual
												return self.dropout_layer(lowercase_  ,    deterministic=lowercase_   )







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     int:
												# The second linear layer needs to be called
												# net_2 for now to match the index of the Sequential layer
												UpperCAmelCase			 =					FlaxGEGLU(self.dim  ,    self.dropout  ,    self.dtype   )
												UpperCAmelCase			 =					nn.Dense(self.dim  ,    dtype=self.dtype   )






						def __call__(   self					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[Any]=True   )							->     Any:
												UpperCAmelCase			 =					self.net_a(lowercase_  ,    deterministic=lowercase_   )
												UpperCAmelCase			 =					self.net_a(lowercase_   )
												return hidden_states







class  A_     (	nn.Module							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				0.0
						__UpperCamelCase													=				jnp.floataa






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					self.dim * 4
												UpperCAmelCase			 =					nn.Dense(inner_dim * 2  ,    dtype=self.dtype   )
												UpperCAmelCase			 =					nn.Dropout(rate=self.dropout   )






						def __call__(   self					:str  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Any=True   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.proj(lowercase_   )
												UpperCAmelCase ,       UpperCAmelCase			 =					jnp.split(lowercase_  ,    2  ,    axis=2   )
												return self.dropout_layer(hidden_linear * nn.gelu(lowercase_   )  ,    deterministic=lowercase_   )










"""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_   )





