


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 YolosImageProcessor


class   a__	(						unittest.TestCase    ):

       def __init__(   self      :     str      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Tuple=7      ,							lowerCamelCase_      :     Any=3      ,							lowerCamelCase_      :     Dict=3_0      ,							lowerCamelCase_      :     Optional[Any]=4_0_0      ,							lowerCamelCase_      :     Tuple=True      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     List[Any]=True      ,							lowerCamelCase_      :     Union[str, Any]=[0.5, 0.5, 0.5]      ,							lowerCamelCase_      :     List[str]=[0.5, 0.5, 0.5]      ,							lowerCamelCase_      :     Optional[int]=True      ,							lowerCamelCase_      :     Any=1 / 2_5_5      ,							lowerCamelCase_      :     List[Any]=True      ,							):
            a_    :     Union[str, Any]										=    size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
            a_    :     Any										=    parent
            a_    :     str										=    batch_size
            a_    :     int										=    num_channels
            a_    :     Union[str, Any]										=    min_resolution
            a_    :     Optional[int]										=    max_resolution
            a_    :     List[Any]										=    do_resize
            a_    :     Dict										=    size
            a_    :     Tuple										=    do_normalize
            a_    :     Union[str, Any]										=    image_mean
            a_    :     Dict										=    image_std
            a_    :     int										=    do_rescale
            a_    :     Any										=    rescale_factor
            a_    :     List[str]										=    do_pad

       def 						UpperCAmelCase(   self      :     List[str]    ):
            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 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     List[Any]=False    ):


            if not batched:
                 a_    :     Tuple										=    image_inputs[0]
                 if isinstance(__UpperCamelCase      ,							Image.Image    ):
                      a_			,			a_    :     Optional[int]										=    image.size
                 else:
                      a_			,			a_    :     int										=    image.shape[1], image.shape[2]
                 if w < h:
                      a_    :     Union[str, Any]										=    int(self.size["""shortest_edge"""] * h / w    )
                      a_    :     List[str]										=    self.size["""shortest_edge"""]
                 elif w > h:
                      a_    :     str										=    self.size["""shortest_edge"""]
                      a_    :     Union[str, Any]										=    int(self.size["""shortest_edge"""] * w / h    )
                 else:
                      a_    :     Union[str, Any]										=    self.size["""shortest_edge"""]
                      a_    :     Dict										=    self.size["""shortest_edge"""]

            else:
                 a_    :     List[Any]										=    []
                 for image in image_inputs:
                      a_			,			a_    :     str										=    self.get_expected_values([image]    )
                      expected_values.append((expected_height, expected_width)    )
                 a_    :     str										=    max(__UpperCamelCase      ,							key=lambda lowerCamelCase_    : item[0]    )[0]
                 a_    :     Union[str, Any]										=    max(__UpperCamelCase      ,							key=lambda lowerCamelCase_    : item[1]    )[1]

            return expected_height, expected_width


@require_torch
@require_vision
class   a__	(						__snake_case						,       unittest.TestCase    ):
       lowerCamelCase__:     Any							    =     YolosImageProcessor if is_vision_available() else None

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Optional[Any]										=    YolosImageProcessingTester(self    )

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            return self.image_processor_tester.prepare_image_processor_dict()

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Optional[Any]										=    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      ,							"""size"""    )    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     List[str]										=    self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size      ,							{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}    )
            self.assertEqual(image_processor.do_pad      ,							__UpperCamelCase    )

            a_    :     Optional[Any]										=    self.image_processing_class.from_dict(
                self.image_processor_dict      ,							size=4_2      ,							max_size=8_4      ,							pad_and_return_pixel_mask=__UpperCamelCase    )
            self.assertEqual(image_processor.size      ,							{"""shortest_edge""": 4_2, """longest_edge""": 8_4}    )
            self.assertEqual(image_processor.do_pad      ,							__UpperCamelCase    )

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

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     int										=    self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            a_    :     Any										=    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_    :     Optional[int]										=    image_processing(image_inputs[0]      ,							return_tensors="""pt"""    ).pixel_values

            a_			,			a_    :     Optional[int]										=    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_    :     Union[str, Any]										=    self.image_processor_tester.get_expected_values(__UpperCamelCase      ,							batched=__UpperCamelCase    )

            a_    :     List[Any]										=    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 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Optional[Any]										=    self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            a_    :     Dict										=    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_    :     List[Any]										=    image_processing(image_inputs[0]      ,							return_tensors="""pt"""    ).pixel_values

            a_			,			a_    :     Dict										=    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_    :     List[str]										=    image_processing(__UpperCamelCase      ,							return_tensors="""pt"""    ).pixel_values

            a_			,			a_    :     Optional[int]										=    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 						UpperCAmelCase(   self      :     int    ):
            a_    :     Optional[int]										=    self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            a_    :     Any										=    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_    :     List[str]										=    image_processing(image_inputs[0]      ,							return_tensors="""pt"""    ).pixel_values

            a_			,			a_    :     Optional[int]										=    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_    :     List[str]										=    image_processing(__UpperCamelCase      ,							return_tensors="""pt"""    ).pixel_values

            a_			,			a_    :     List[str]										=    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 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[str]										=    self.image_processing_class(**self.image_processor_dict    )
            a_    :     Any										=    self.image_processing_class(do_resize=__UpperCamelCase      ,							do_normalize=__UpperCamelCase      ,							do_rescale=__UpperCamelCase    )
            # create random PyTorch tensors
            a_    :     Optional[int]										=    prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__UpperCamelCase      ,							torchify=__UpperCamelCase    )
            for image in image_inputs:
                 self.assertIsInstance(__UpperCamelCase      ,							torch.Tensor    )

            # Test whether the method "pad" and calling the image processor return the same tensors
            a_    :     List[Any]										=    image_processing_a.pad(__UpperCamelCase      ,							return_tensors="""pt"""    )
            a_    :     List[Any]										=    image_processing_a(__UpperCamelCase      ,							return_tensors="""pt"""    )

            self.assertTrue(
                torch.allclose(encoded_images_with_method["""pixel_values"""]      ,							encoded_images["""pixel_values"""]      ,							atol=1E-4    )    )

       @slow
       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     str										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
            with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,							"""r"""    ) as f:
                 a_    :     int										=    json.loads(f.read()    )

            a_    :     Union[str, Any]										=    {"""image_id""": 3_9_7_6_9, """annotations""": target}

            # encode them
            a_    :     str										=    YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""    )
            a_    :     Any										=    image_processing(images=__UpperCamelCase      ,							annotations=__UpperCamelCase      ,							return_tensors="""pt"""    )

            # verify pixel values
            a_    :     Optional[Any]										=    torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
            self.assertEqual(encoding["""pixel_values"""].shape      ,							__UpperCamelCase    )

            a_    :     int										=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
            self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,							__UpperCamelCase      ,							atol=1E-4    )    )

            # verify area
            a_    :     Tuple										=    torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,							__UpperCamelCase    )    )
            # verify boxes
            a_    :     Optional[Any]										=    torch.Size([6, 4]    )
            self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,							__UpperCamelCase    )
            a_    :     Optional[int]										=    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,							__UpperCamelCase      ,							atol=1E-3    )    )
            # verify image_id
            a_    :     Optional[Any]										=    torch.tensor([3_9_7_6_9]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,							__UpperCamelCase    )    )
            # verify is_crowd
            a_    :     Any										=    torch.tensor([0, 0, 0, 0, 0, 0]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,							__UpperCamelCase    )    )
            # verify class_labels
            a_    :     int										=    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,							__UpperCamelCase    )    )
            # verify orig_size
            a_    :     str										=    torch.tensor([4_8_0, 6_4_0]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,							__UpperCamelCase    )    )
            # verify size
            a_    :     Any										=    torch.tensor([8_0_0, 1_0_6_6]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,							__UpperCamelCase    )    )

       @slow
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[int]										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
            with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,							"""r"""    ) as f:
                 a_    :     Optional[int]										=    json.loads(f.read()    )

            a_    :     List[str]										=    {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

            a_    :     Optional[Any]										=    pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

            # encode them
            a_    :     int										=    YolosImageProcessor(format="""coco_panoptic"""    )
            a_    :     List[str]										=    image_processing(images=__UpperCamelCase      ,							annotations=__UpperCamelCase      ,							masks_path=__UpperCamelCase      ,							return_tensors="""pt"""    )

            # verify pixel values
            a_    :     Dict										=    torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
            self.assertEqual(encoding["""pixel_values"""].shape      ,							__UpperCamelCase    )

            a_    :     Dict										=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
            self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,							__UpperCamelCase      ,							atol=1E-4    )    )

            # verify area
            a_    :     Any										=    torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,							__UpperCamelCase    )    )
            # verify boxes
            a_    :     Any										=    torch.Size([6, 4]    )
            self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,							__UpperCamelCase    )
            a_    :     Optional[Any]										=    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,							__UpperCamelCase      ,							atol=1E-3    )    )
            # verify image_id
            a_    :     str										=    torch.tensor([3_9_7_6_9]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,							__UpperCamelCase    )    )
            # verify is_crowd
            a_    :     Optional[Any]										=    torch.tensor([0, 0, 0, 0, 0, 0]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,							__UpperCamelCase    )    )
            # verify class_labels
            a_    :     List[Any]										=    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,							__UpperCamelCase    )    )
            # verify masks
            a_    :     Optional[int]										=    8_2_2_8_7_3
            self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,							__UpperCamelCase    )
            # verify orig_size
            a_    :     Optional[int]										=    torch.tensor([4_8_0, 6_4_0]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,							__UpperCamelCase    )    )
            # verify size
            a_    :     str										=    torch.tensor([8_0_0, 1_0_6_6]    )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,							__UpperCamelCase    )    )







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCamelCase        =     logging.get_logger(__name__)



def  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


@dataclass
class   a__	:
       lowerCamelCase__:     List[str]							    =     list_field(
           default=[]						,       metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           }						,       )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8]						,       metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}    )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8, 32, 128, 512]						,       metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}						,       )

       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Use FP16 to accelerate inference."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Benchmark training of model"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Verbose memory tracing"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           }						,       )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Trace memory line by line"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save result to a CSV file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save all print statements in a log file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Whether to print environment information"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           }						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""env_info_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving environment information."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""log_{round(time()    )}.csv"""						,       metadata={"""help""": """Log filename used if print statements are saved in log."""}						,       )
       lowerCamelCase__:     int							    =     field(default=3						,       metadata={"""help""": """Times an experiment will be run."""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     Any    ):
            warnings.warn(
                F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
                """ are deprecated in general and it is advised to use external Benchmarking libraries """
                """ to benchmark Transformer models."""      ,							lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return json.dumps(dataclasses.asdict(self    )      ,							indent=2    )

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            if len(self.models    ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""    )
            return self.models

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            if not self.multi_process:
                 return False
            elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""    )
                 return False
            else:
                 return True



