




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[str]							       =   trt.Logger(trt.Logger.WARNING)
_lowerCamelCase	:		Dict							       =   absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_lowerCamelCase	:		Optional[int]							       =   logging.getLogger(__name__)

_lowerCamelCase	:		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""",
)

_lowerCamelCase	:		Tuple							       =   parser.parse_args()

if args.tokenizer_name:
  _lowerCamelCase	:		List[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	:		List[Any]							       =   args.per_device_eval_batch_size

_lowerCamelCase	:		Optional[Any]							       =   (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCamelCase	:		List[str]							       =   True

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

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

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

  with builder.create_builder_config() as config:
    _lowerCamelCase	:		Tuple							       =   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	:		Optional[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	:		Union[str, 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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       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() ,				lowercase_						)
       cuda.memcpy_htod_async(d_inputs[1] ,				attention_mask.ravel() ,				lowercase_						)
       cuda.memcpy_htod_async(d_inputs[2] ,				token_type_ids.ravel() ,				lowercase_						)
       # start time
       A__       =    time.time()
       # Run inference
       context.execute_async(
           bindings=[int(lowercase_						) for d_inp in d_inputs] + [int(lowercase_						), int(lowercase_						)] ,				stream_handle=stream.handle						)
       # Transfer predictions back from GPU
       cuda.memcpy_dtoh_async(lowercase_ ,				lowercase_ ,				lowercase_						)
       cuda.memcpy_dtoh_async(lowercase_ ,				lowercase_ ,				lowercase_						)
       # 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	:		List[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.
  _lowerCamelCase	:		Optional[int]							       =   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	:		int							       =   raw_datasets["""validation"""].column_names

_lowerCamelCase	:		str							       =   """question""" if """question""" in column_names else column_names[0]
_lowerCamelCase	:		Optional[int]							       =   """context""" if """context""" in column_names else column_names[1]
_lowerCamelCase	:		Tuple							       =   """answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase	:		Optional[int]							       =   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	:		Union[str, Any]							       =   min(args.max_seq_length, tokenizer.model_max_length)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       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=lowercase_ ,				stride=args.doc_stride ,				return_overflowing_tokens=lowercase_ ,				return_offsets_mapping=lowercase_ ,				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(lowercase_						)
              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	:		int							       =   raw_datasets["""validation"""]
# Validation Feature Creation
_lowerCamelCase	:		Optional[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	:		Tuple							       =   default_data_collator

_lowerCamelCase	:		Union[str, Any]							       =   eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
_lowerCamelCase	:		Any							       =   DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_="eval"						)   ->       Tuple:



       """simple docstring"""

       A__       =    postprocess_qa_predictions(
           examples=lowercase_ ,				features=lowercase_ ,				predictions=lowercase_ ,				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=lowercase_ ,				)
       # 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=lowercase_ ,				label_ids=lowercase_						)


_lowerCamelCase	:		List[Any]							       =   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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[int]:



         """simple docstring"""

         return trt.volume(engine.get_binding_shape(lowercase_						)						) * engine.get_binding_dtype(lowercase_						).itemsize

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

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

  # Create a stream in which to copy inputs/outputs and run inference.
  _lowerCamelCase	:		int							       =   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	:		Dict							       =   0.0
  _lowerCamelCase	:		List[str]							       =   0
  _lowerCamelCase	:		Optional[Any]							       =   timeit.default_timer()

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

    _lowerCamelCase       ,			_lowerCamelCase	:		Optional[int]							       =   outputs
    _lowerCamelCase	:		Any							       =   torch.tensor(start_logits)
    _lowerCamelCase	:		Optional[Any]							       =   torch.tensor(end_logits)

    # necessary to pad predictions and labels for being gathered
    _lowerCamelCase	:		Union[str, Any]							       =   accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
    _lowerCamelCase	:		Optional[int]							       =   accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

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

  if all_preds is not None:
    _lowerCamelCase	:		Tuple							       =   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 * 1000 / niter))
  logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))
  logger.info("""Total Number of Inference =  %d""", niter)

_lowerCamelCase	:		Any							       =   post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase	:		List[str]							       =   metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F'''Evaluation metrics: {eval_metric}''')








import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DeformableDetrImageProcessor


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	Dict=3					, UpperCAmelCase__      :	List[Any]=30					, UpperCAmelCase__      :	Any=400					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Any=True					, UpperCAmelCase__      :	Optional[Any]=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	List[str]=True					, UpperCAmelCase__      :	Optional[int]=1 / 255					, UpperCAmelCase__      :	Optional[Any]=True					, )						->str:





           '''simple docstring'''

           A__       =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           A__       =    parent
           A__       =    batch_size
           A__       =    num_channels
           A__       =    min_resolution
           A__       =    max_resolution
           A__       =    do_resize
           A__       =    size
           A__       =    do_normalize
           A__       =    image_mean
           A__       =    image_std
           A__       =    do_rescale
           A__       =    rescale_factor
           A__       =    do_pad


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int=False)						->Optional[Any]:





           '''simple docstring'''

           if not batched:
                  A__       =    image_inputs[0]
                  if isinstance(UpperCAmelCase__					, Image.Image):
                         A__ ,							A__       =    image.size
                  else:
                         A__ ,							A__       =    image.shape[1], image.shape[2]
                  if w < h:
                         A__       =    int(self.size['''shortest_edge'''] * h / w)
                         A__       =    self.size['''shortest_edge''']
                  elif w > h:
                         A__       =    self.size['''shortest_edge''']
                         A__       =    int(self.size['''shortest_edge'''] * w / h)
                  else:
                         A__       =    self.size['''shortest_edge''']
                         A__       =    self.size['''shortest_edge''']

           else:
                  A__       =    []
                  for image in image_inputs:
                         A__ ,							A__       =    self.get_expected_values([image])
                         expected_values.append((expected_height, expected_width))
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[0])[0]
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[1])[1]

           return expected_height, expected_width


@require_torch
@require_vision
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      DeformableDetrImageProcessor if is_vision_available() else None


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Tuple:





           '''simple docstring'''

           A__       =    DeformableDetrImageProcessingTester(self)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_rescale'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_pad'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''size'''))


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 18, '''longest_edge''': 1_333})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)

           A__       =    self.image_processing_class.from_dict(
               self.image_processor_dict					, size=42					, max_size=84					, pad_and_return_pixel_mask=UpperCAmelCase__)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 42, '''longest_edge''': 84})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, Image.Image)

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

           # Test batched
           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__					, numpify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, np.ndarray)

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

           # Test batched
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__					, torchify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, torch.Tensor)

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

           # Test batched
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					, )


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[str]:





           '''simple docstring'''

           A__       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''					, '''r''') as f:
                  A__       =    json.loads(f.read())

           A__       =    {'''image_id''': 39_769, '''annotations''': target}

           # encode them
           A__       =    DeformableDetrImageProcessor()
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

           A__       =    torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					, UpperCAmelCase__					, atol=1e-4))

           # verify area
           A__       =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[int]:





           '''simple docstring'''

           A__       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''					, '''r''') as f:
                  A__       =    json.loads(f.read())

           A__       =    {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

           A__       =    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

           # encode them
           A__       =    DeformableDetrImageProcessor(format='''coco_panoptic''')
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, masks_path=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

           A__       =    torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					, UpperCAmelCase__					, atol=1e-4))

           # verify area
           A__       =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify masks
           A__       =    822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					, UpperCAmelCase__)
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))


