


import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


_lowerCAmelCase:						List[Any]   				=       trt.Logger(trt.Logger.WARNING)
_lowerCAmelCase:						Any   				=       absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_lowerCAmelCase:						int   				=       logging.getLogger(__name__)

_lowerCAmelCase:						Tuple   				=       argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=384,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=128,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

_lowerCAmelCase:						Any   				=       parser.parse_args()

if args.tokenizer_name:
	_lowerCAmelCase:						Any   				=       AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
	raise ValueError(
	    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
	    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
	)

logger.info('Training/evaluation parameters %s', args)

_lowerCAmelCase:						Dict   				=       args.per_device_eval_batch_size

_lowerCAmelCase:						str   				=       (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCAmelCase:						Optional[int]   				=       True

_lowerCAmelCase:						Optional[int]   				=       'temp_engine/bert-fp32.engine'
if args.fpaa:
	_lowerCAmelCase:						Tuple   				=       'temp_engine/bert-fp16.engine'
if args.inta:
	_lowerCAmelCase:						Optional[Any]   				=       'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
	os.makedirs('temp_engine')

_lowerCAmelCase:						int   				=       1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
	with open(args.onnx_model_path, 'rb') as model:
		if not parser.parse(model.read()):
			for error in range(parser.num_errors):
				print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
	_lowerCAmelCase:						Optional[Any]   				=       [network.get_input(i) for i in range(network.num_inputs)]
	_lowerCAmelCase:						Optional[int]   				=       [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

	with builder.create_builder_config() as config:
		_lowerCAmelCase:						Union[str, Any]   				=       1 << 50
		if STRICT_TYPES:
			config.set_flag(trt.BuilderFlag.STRICT_TYPES)
		if args.fpaa:
			config.set_flag(trt.BuilderFlag.FPaa)
		if args.inta:
			config.set_flag(trt.BuilderFlag.INTa)
		_lowerCAmelCase:						Union[str, Any]   				=       builder.create_optimization_profile()
		config.add_optimization_profile(profile)
		for i in range(len(input_names)):
			profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
		_lowerCAmelCase:						str   				=       builder.build_engine(network, config)

		# serialize_engine and store in file (can be directly loaded and deserialized):
		with open(engine_name, 'wb') as f:
			f.write(engine.serialize())






def   _lowercase(					__a				:						Tuple    ,     __a				:						Optional[int]    ,     __a				:						Dict    ,     __a				:						Optional[Any]    ,     __a				:						Tuple    ,     __a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						Any     ):
							a__      =np.asarray(inputs['input_ids']    ,     dtype=np.intaa     )
							a__      =np.asarray(inputs['attention_mask']    ,     dtype=np.intaa     )
							a__      =np.asarray(inputs['token_type_ids']    ,     dtype=np.intaa     )

							# Copy inputs
							cuda.memcpy_htod_async(d_inputs[0]    ,     input_ids.ravel()    ,     __a     )
							cuda.memcpy_htod_async(d_inputs[1]    ,     attention_mask.ravel()    ,     __a     )
							cuda.memcpy_htod_async(d_inputs[2]    ,     token_type_ids.ravel()    ,     __a     )
							# start time
							a__      =time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(__a     ) for d_inp in d_inputs] + [int(__a     ), int(__a     )]    ,     stream_handle=stream.handle     )
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(__a    ,     __a    ,     __a     )
							cuda.memcpy_dtoh_async(__a    ,     __a    ,     __a     )
							# Synchronize the stream and take time
							stream.synchronize()
							# end time
							a__      =time.time()
							a__      =end_time - start_time
							a__      =(h_outputa, h_outputa)
							# print(outputs)
							return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCAmelCase:						Tuple   				=       Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
	datasets.utils.logging.set_verbosity_warning()
	transformers.utils.logging.set_verbosity_info()
else:
	datasets.utils.logging.set_verbosity_error()
	transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
	set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
	# Downloading and loading a dataset from the hub.
	_lowerCAmelCase:						str   				=       load_dataset(args.dataset_name, args.dataset_config_name)
else:
	raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

_lowerCAmelCase:						Tuple   				=       raw_datasets['validation'].column_names

_lowerCAmelCase:						List[str]   				=       'question' if 'question' in column_names else column_names[0]
_lowerCAmelCase:						Union[str, Any]   				=       'context' if 'context' in column_names else column_names[1]
_lowerCAmelCase:						Union[str, Any]   				=       'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_lowerCAmelCase:						Optional[Any]   				=       tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
	logger.warning(
	    F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
	    F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
	)

_lowerCAmelCase:						Tuple   				=       min(args.max_seq_length, tokenizer.model_max_length)






def   _lowercase(					__a				:						List[str]     ):
							# Some of the questions have lots of whitespace on the left, which is not useful and will make the
							# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
							# left whitespace
							a__      =[q.lstrip() for q in examples[question_column_name]]

							# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
							# in one example possible giving several features when a context is long, each of those features having a
							# context that overlaps a bit the context of the previous feature.
							a__      =tokenizer(
							    examples[question_column_name if pad_on_right else context_column_name]    ,     examples[context_column_name if pad_on_right else question_column_name]    ,     truncation='only_second' if pad_on_right else 'only_first'    ,     max_length=__a    ,     stride=args.doc_stride    ,     return_overflowing_tokens=__a    ,     return_offsets_mapping=__a    ,     padding='max_length'    ,     )

							# Since one example might give us several features if it has a long context, we need a map from a feature to
							# its corresponding example. This key gives us just that.
							a__      =tokenized_examples.pop('overflow_to_sample_mapping'     )

							# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
							# corresponding example_id and we will store the offset mappings.
							a__      =[]

							for i in range(len(tokenized_examples['input_ids']     )     ):
														# Grab the sequence corresponding to that example (to know what is the context and what is the question).
														a__      =tokenized_examples.sequence_ids(__a     )
														a__      =1 if pad_on_right else 0

														# One example can give several spans, this is the index of the example containing this span of text.
														a__      =sample_mapping[i]
														tokenized_examples["example_id"].append(examples['id'][sample_index]     )

														# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
														# position is part of the context or not.
														a__      =[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples['offset_mapping'][i]     )
														]

							return tokenized_examples


_lowerCAmelCase:						List[Any]   				=       raw_datasets['validation']
# Validation Feature Creation
_lowerCAmelCase:						Any   				=       eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

_lowerCAmelCase:						List[str]   				=       default_data_collator

_lowerCAmelCase:						Dict   				=       eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_lowerCAmelCase:						Tuple   				=       DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def   _lowercase(					__a				:						int    ,     __a				:						Optional[int]    ,     __a				:						List[Any]    ,     __a				:						Optional[Any]="eval"     ):
							# Post-processing: we match the start logits and end logits to answers in the original context.
							a__      =postprocess_qa_predictions(
							    examples=__a    ,     features=__a    ,     predictions=__a    ,     version_2_with_negative=args.version_2_with_negative    ,     n_best_size=args.n_best_size    ,     max_answer_length=args.max_answer_length    ,     null_score_diff_threshold=args.null_score_diff_threshold    ,     output_dir=args.output_dir    ,     prefix=__a    ,     )
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														a__      =[
														    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
														]
							else:
														a__      =[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

							a__      =[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=__a    ,     label_ids=__a     )


_lowerCAmelCase:						List[str]   				=       load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
	# setup for TRT inferrence
	for i in range(len(input_names)):
		context.set_binding_shape(i, INPUT_SHAPE)
	assert context.all_binding_shapes_specified






	def   _lowercase(					__a				:						Tuple     ):
								return trt.volume(engine.get_binding_shape(__a     )     ) * engine.get_binding_dtype(__a     ).itemsize

	# Allocate device memory for inputs and outputs.
	_lowerCAmelCase:						Optional[Any]   				=       [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

	# Allocate output buffer
	_lowerCAmelCase:						Dict   				=       cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
	_lowerCAmelCase:						Optional[int]   				=       cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
	_lowerCAmelCase:						Tuple   				=       cuda.mem_alloc(h_outputa.nbytes)
	_lowerCAmelCase:						Any   				=       cuda.mem_alloc(h_outputa.nbytes)

	# Create a stream in which to copy inputs/outputs and run inference.
	_lowerCAmelCase:						str   				=       cuda.Stream()

	# Evaluation
	logger.info('***** Running Evaluation *****')
	logger.info(F"""  Num examples = {len(eval_dataset)}""")
	logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

	_lowerCAmelCase:						Any   				=       0.0
	_lowerCAmelCase:						str   				=       0
	_lowerCAmelCase:						Dict   				=       timeit.default_timer()

	_lowerCAmelCase:						str   				=       None
	for step, batch in enumerate(eval_dataloader):
		_lowerCAmelCase				,						_lowerCAmelCase:						Optional[int]   				=       model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
		total_time += infer_time
		niter += 1

		_lowerCAmelCase				,						_lowerCAmelCase:						str   				=       outputs
		_lowerCAmelCase:						List[Any]   				=       torch.tensor(start_logits)
		_lowerCAmelCase:						str   				=       torch.tensor(end_logits)

		# necessary to pad predictions and labels for being gathered
		_lowerCAmelCase:						Dict   				=       accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
		_lowerCAmelCase:						List[Any]   				=       accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

		_lowerCAmelCase:						Optional[int]   				=       (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
		_lowerCAmelCase:						List[Any]   				=       logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

	if all_preds is not None:
		_lowerCAmelCase:						Union[str, Any]   				=       nested_truncate(all_preds, len(eval_dataset))

	_lowerCAmelCase:						Union[str, Any]   				=       timeit.default_timer() - start_time
	logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
	# Inference time from TRT
	logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
	logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))







	logger.info('Total Number of Inference =  %d', niter)

_lowerCAmelCase:						Tuple   				=       post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCAmelCase:						Union[str, Any]   				=       metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







logger.info(F"""Evaluation metrics: {eval_metric}""")






"""simple docstring"""






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,
)


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				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'''),
    ]
)

snake_case   					=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 			snake_case		(		lowerCAmelCase_	)						->				str:
  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _snake_case   	=      model_type_to_module_name(lowerCAmelCase_	)

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

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

  return None




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						**lowerCAmelCase_  ,						)						->				int:
  _snake_case   	=      get_file_from_repo(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						cache_dir=lowerCAmelCase_  ,						force_download=lowerCAmelCase_  ,						resume_download=lowerCAmelCase_  ,						proxies=lowerCAmelCase_  ,						use_auth_token=lowerCAmelCase_  ,						revision=lowerCAmelCase_  ,						local_files_only=lowerCAmelCase_  ,						)
  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(lowerCAmelCase_  ,						encoding='''utf-8'''	) as reader:
    return json.load(lowerCAmelCase_	)







class     UpperCAmelCase    :



   def __init__(       self    :	Any				):



     """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(__lowerCamelCase				)
   def    __UpperCAmelCase    (       cls    :	List[str]							,       __lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      kwargs.pop('''config'''							,       __lowerCamelCase				)
     _snake_case   	=      kwargs.pop('''trust_remote_code'''							,       __lowerCamelCase				)
     _snake_case   	=      True

     _snake_case	,     _snake_case   	=      ImageProcessingMixin.get_image_processor_dict(__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      config_dict.get('''image_processor_type'''							,       __lowerCamelCase				)
     _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'''							,       __lowerCamelCase				)
       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(__lowerCamelCase							,       __lowerCamelCase				):
         _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase							,       **__lowerCamelCase				)
       # It could be in `config.image_processor_type``
       _snake_case   	=      getattr(__lowerCamelCase							,       '''image_processor_type'''							,       __lowerCamelCase				)
       if hasattr(__lowerCamelCase							,       '''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(__lowerCamelCase				)

     _snake_case   	=      image_processor_auto_map is not None
     _snake_case   	=      image_processor_class is not None or type(__lowerCamelCase				) in IMAGE_PROCESSOR_MAPPING
     _snake_case   	=      resolve_trust_remote_code(
         __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

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

     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    __UpperCAmelCase    (       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase							,       __lowerCamelCase				)





