





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


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

_snake_case    :       int		       =    logging.getLogger(__name__)

_snake_case    :       List[str]		       =    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',
)

_snake_case    :       Union[str, Any]		       =    parser.parse_args()

if args.tokenizer_name:
     _snake_case    :       str		       =    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)

_snake_case    :       List[Any]		       =    args.per_device_eval_batch_size

_snake_case    :       Tuple		       =    (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_snake_case    :       Optional[Any]		       =    True

_snake_case    :       Optional[Any]		       =    '''temp_engine/bert-fp32.engine'''
if args.fpaa:
     _snake_case    :       List[str]		       =    '''temp_engine/bert-fp16.engine'''
if args.inta:
     _snake_case    :       List[str]		       =    '''temp_engine/bert-int8.engine'''

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

_snake_case    :       Union[str, Any]		       =    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
     _snake_case    :       Dict		       =    [network.get_input(i) for i in range(network.num_inputs)]
     _snake_case    :       Any		       =    [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

     with builder.create_builder_config() as config:
          _snake_case    :       Optional[int]		       =    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)
          _snake_case    :       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)
          _snake_case    :       List[Any]		       =    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     a_       (					lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     List[Any],      lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     List[Any]      ):
   __lowerCAmelCase				      =      np.asarray(inputs['input_ids'],      dtype=np.intaa      )
   __lowerCAmelCase				      =      np.asarray(inputs['attention_mask'],      dtype=np.intaa      )
   __lowerCAmelCase				      =      np.asarray(inputs['token_type_ids'],      dtype=np.intaa      )

   # Copy inputs
   cuda.memcpy_htod_async(d_inputs[0],      input_ids.ravel(),      lowerCAmelCase_      )
   cuda.memcpy_htod_async(d_inputs[1],      attention_mask.ravel(),      lowerCAmelCase_      )
   cuda.memcpy_htod_async(d_inputs[2],      token_type_ids.ravel(),      lowerCAmelCase_      )
   # start time
   __lowerCAmelCase				      =      time.time()
   # Run inference
   context.execute_async(
       bindings=[int(lowerCAmelCase_      ) for d_inp in d_inputs] + [int(lowerCAmelCase_      ), int(lowerCAmelCase_      )],      stream_handle=stream.handle      )
   # Transfer predictions back from GPU
   cuda.memcpy_dtoh_async(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )
   cuda.memcpy_dtoh_async(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )
   # Synchronize the stream and take time
   stream.synchronize()
   # end time
   __lowerCAmelCase				      =      time.time()
   __lowerCAmelCase				      =      end_time - start_time
   __lowerCAmelCase				      =      (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.
_snake_case    :       Union[str, Any]		       =    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.
     _snake_case    :       List[Any]		       =    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.

_snake_case    :       Optional[int]		       =    raw_datasets['''validation'''].column_names

_snake_case    :       str		       =    '''question''' if '''question''' in column_names else column_names[0]
_snake_case    :       Dict		       =    '''context''' if '''context''' in column_names else column_names[1]
_snake_case    :       Any		       =    '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_snake_case    :       Tuple		       =    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}."""
     )

_snake_case    :       Optional[Any]		       =    min(args.max_seq_length, tokenizer.model_max_length)

def     a_       (					lowerCAmelCase_ :     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
   __lowerCAmelCase				      =      [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.
   __lowerCAmelCase				      =      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=lowerCAmelCase_,      stride=args.doc_stride,      return_overflowing_tokens=lowerCAmelCase_,      return_offsets_mapping=lowerCAmelCase_,      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.
   __lowerCAmelCase				      =      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.
   __lowerCAmelCase				      =      []

   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).
      __lowerCAmelCase				      =      tokenized_examples.sequence_ids(lowerCAmelCase_      )
      __lowerCAmelCase				      =      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.
      __lowerCAmelCase				      =      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.
      __lowerCAmelCase				      =      [
          (o if sequence_ids[k] == context_index else None)
          for k, o in enumerate(tokenized_examples['offset_mapping'][i]      )
      ]

   return tokenized_examples


_snake_case    :       Any		       =    raw_datasets['''validation''']
# Validation Feature Creation
_snake_case    :       str		       =    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',
)

_snake_case    :       Tuple		       =    default_data_collator

_snake_case    :       List[str]		       =    eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_snake_case    :       List[Any]		       =    DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def     a_       (					lowerCAmelCase_ :     str,      lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     Any="eval"      ):
   # Post-processing: we match the start logits and end logits to answers in the original context.
   __lowerCAmelCase				      =      postprocess_qa_predictions(
       examples=lowerCAmelCase_,      features=lowerCAmelCase_,      predictions=lowerCAmelCase_,      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=lowerCAmelCase_,      )
   # Format the result to the format the metric expects.
   if args.version_2_with_negative:
      __lowerCAmelCase				      =      [
          {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
      ]
   else:
      __lowerCAmelCase				      =      [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


_snake_case    :       Optional[int]		       =    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     a_       (					lowerCAmelCase_ :     Optional[int]      ):
        return trt.volume(engine.get_binding_shape(lowerCAmelCase_      )      ) * engine.get_binding_dtype(lowerCAmelCase_      ).itemsize

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

     # Allocate output buffer
     _snake_case    :       List[Any]		       =    cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
     _snake_case    :       Any		       =    cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
     _snake_case    :       Union[str, Any]		       =    cuda.mem_alloc(h_outputa.nbytes)
     _snake_case    :       Dict		       =    cuda.mem_alloc(h_outputa.nbytes)

     # Create a stream in which to copy inputs/outputs and run inference.
     _snake_case    :       Dict		       =    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}""")

     _snake_case    :       Dict		       =    0.0
     _snake_case    :       Dict		       =    0
     _snake_case    :       Dict		       =    timeit.default_timer()

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

          _snake_case    :       Any		       =    outputs
          _snake_case    :       Optional[Any]		       =    torch.tensor(start_logits)
          _snake_case    :       Any		       =    torch.tensor(end_logits)

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

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

     if all_preds is not None:
          _snake_case    :       List[str]		       =    nested_truncate(all_preds, len(eval_dataset))

     _snake_case    :       List[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 * 1000 / niter))
     logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))




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

_snake_case    :       int		       =    post_processing_function(eval_examples, eval_dataset, all_preds)
_snake_case    :       Optional[Any]		       =    metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




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


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor



class        lowerCamelCase__				(   unittest.TestCase				):





			def __init__(       self	:	List[str]  ,  lowercase__	:	Any  ,  lowercase__	:	List[Any]=7  ,  lowercase__	:	List[str]=3  ,  lowercase__	:	str=18  ,  lowercase__	:	List[Any]=30  ,  lowercase__	:	Optional[int]=4_00  ,  lowercase__	:	Dict=True  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	int=True  ,  lowercase__	:	Tuple=None  ,  lowercase__	:	int=True  ,  lowercase__	:	Tuple=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]  ,  lowercase__	:	Optional[int]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]  ,  lowercase__	:	Any=True  ,  ):
										_lowerCAmelCase							=							size if size is not None else {'height': 2_24, 'width': 2_24}
										_lowerCAmelCase							=							crop_size if crop_size is not None else {'height': 18, 'width': 18}
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							num_channels
										_lowerCAmelCase							=							image_size
										_lowerCAmelCase							=							min_resolution
										_lowerCAmelCase							=							max_resolution
										_lowerCAmelCase							=							do_resize
										_lowerCAmelCase							=							size
										_lowerCAmelCase							=							do_center_crop
										_lowerCAmelCase							=							crop_size
										_lowerCAmelCase							=							do_normalize
										_lowerCAmelCase							=							image_mean
										_lowerCAmelCase							=							image_std
										_lowerCAmelCase							=							do_convert_rgb





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Tuple=False  ,  lowercase__	:	List[Any]=False  ,  lowercase__	:	str=False						):
										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																	_lowerCAmelCase							=							[]
																	for i in range(self.batch_size						):
																								image_inputs.append(
																								    np.random.randint(
																								        2_55  ,  size=(self.num_channels, self.max_resolution, self.max_resolution)  ,  dtype=np.uinta						)						)
										else:
																	_lowerCAmelCase							=							[]
																	for i in range(self.batch_size						):
																								_lowerCAmelCase			,		_lowerCAmelCase							=							np.random.choice(np.arange(self.min_resolution  ,  self.max_resolution						)  ,  2						)
																								image_inputs.append(np.random.randint(2_55  ,  size=(self.num_channels, width, height)  ,  dtype=np.uinta						)						)

										if not numpify and not torchify:
																	# PIL expects the channel dimension as last dimension
																	_lowerCAmelCase							=							[Image.fromarray(np.moveaxis(lowercase__  ,  0  ,  -1						)						) for x in image_inputs]

										if torchify:
																	_lowerCAmelCase							=							[torch.from_numpy(lowercase__						) for x in image_inputs]

										return image_inputs







@require_torch
@require_vision
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=ChineseCLIPImageProcessor if is_vision_available() else None





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							ChineseCLIPImageProcessingTester(self  ,  do_center_crop=lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										return self.image_processor_tester.prepare_image_processor_dict()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.image_processing_class(**self.image_processor_dict						)
										self.assertTrue(hasattr(lowercase__  ,  'do_resize'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'size'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'do_center_crop'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'center_crop'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'do_normalize'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'image_mean'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'image_std'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'do_convert_rgb'						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase							=							self.image_processing_class.from_dict(self.image_processor_dict						)
										self.assertEqual(image_processor.size  ,  {'height': 2_24, 'width': 2_24}						)
										self.assertEqual(image_processor.crop_size  ,  {'height': 18, 'width': 18}						)

										_lowerCAmelCase							=							self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  ,  crop_size=84						)
										self.assertEqual(image_processor.size  ,  {'shortest_edge': 42}						)
										self.assertEqual(image_processor.crop_size  ,  {'height': 84, 'width': 84}						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										# Initialize image_processing
										_lowerCAmelCase							=							self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										_lowerCAmelCase							=							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__						)
										for image in image_inputs:
																	self.assertIsInstance(lowercase__  ,  Image.Image						)

										# Test not batched input
										_lowerCAmelCase							=							image_processing(image_inputs[0]  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )

										# Test batched
										_lowerCAmelCase							=							image_processing(lowercase__  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										# Initialize image_processing
										_lowerCAmelCase							=							self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										_lowerCAmelCase							=							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  ,  numpify=lowercase__						)
										for image in image_inputs:
																	self.assertIsInstance(lowercase__  ,  np.ndarray						)

										# Test not batched input
										_lowerCAmelCase							=							image_processing(image_inputs[0]  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )

										# Test batched
										_lowerCAmelCase							=							image_processing(lowercase__  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										# Initialize image_processing
										_lowerCAmelCase							=							self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										_lowerCAmelCase							=							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  ,  torchify=lowercase__						)
										for image in image_inputs:
																	self.assertIsInstance(lowercase__  ,  torch.Tensor						)

										# Test not batched input
										_lowerCAmelCase							=							image_processing(image_inputs[0]  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )

										# Test batched
										_lowerCAmelCase							=							image_processing(lowercase__  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )







@require_torch
@require_vision
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=ChineseCLIPImageProcessor if is_vision_available() else None





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							ChineseCLIPImageProcessingTester(self  ,  num_channels=4  ,  do_center_crop=lowercase__						)
										_lowerCAmelCase							=							3





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return self.image_processor_tester.prepare_image_processor_dict()





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							self.image_processing_class(**self.image_processor_dict						)
										self.assertTrue(hasattr(lowercase__  ,  'do_resize'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'size'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'do_center_crop'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'center_crop'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'do_normalize'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'image_mean'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'image_std'						)						)
										self.assertTrue(hasattr(lowercase__  ,  'do_convert_rgb'						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										# Initialize image_processing
										_lowerCAmelCase							=							self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										_lowerCAmelCase							=							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__						)
										for image in image_inputs:
																	self.assertIsInstance(lowercase__  ,  Image.Image						)

										# Test not batched input
										_lowerCAmelCase							=							image_processing(image_inputs[0]  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )

										# Test batched
										_lowerCAmelCase							=							image_processing(lowercase__  ,  return_tensors='pt'						).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )  ,  )






