

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   UpperCamelCase			(  unittest.TestCase ):

    def __init__(      self			:   Optional[Any]				,     snake_case__			:   int				,     snake_case__			:   Tuple=7				,     snake_case__			:   Any=3				,     snake_case__			:   Union[str, Any]=3_0				,     snake_case__			:   Dict=4_0_0				,     snake_case__			:   List[Any]=True				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   List[str]=True				,     snake_case__			:   Dict=[0.5, 0.5, 0.5]				,     snake_case__			:   int=[0.5, 0.5, 0.5]				,     snake_case__			:   str=True				,     snake_case__			:   int=1 / 2_5_5				,     snake_case__			:   Dict=True				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      num_channels
     SCREAMING_SNAKE_CASE		    =      min_resolution
     SCREAMING_SNAKE_CASE		    =      max_resolution
     SCREAMING_SNAKE_CASE		    =      do_resize
     SCREAMING_SNAKE_CASE		    =      size
     SCREAMING_SNAKE_CASE		    =      do_normalize
     SCREAMING_SNAKE_CASE		    =      image_mean
     SCREAMING_SNAKE_CASE		    =      image_std
     SCREAMING_SNAKE_CASE		    =      do_rescale
     SCREAMING_SNAKE_CASE		    =      rescale_factor
     SCREAMING_SNAKE_CASE		    =      do_pad

    def UpperCamelCase      (      self			:   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 UpperCamelCase      (      self			:   Any				,     snake_case__			:   Optional[int]				,     snake_case__			:   List[Any]=False     ):

     """simple docstring"""






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

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

     return expected_height, expected_width




@require_torch
@require_vision
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =YolosImageProcessor if is_vision_available() else None

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      YolosImageProcessingTester(self     )

    @property
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(snake_case__				,     'image_mean'     )     )
     self.assertTrue(hasattr(snake_case__				,     'image_std'     )     )
     self.assertTrue(hasattr(snake_case__				,     'do_normalize'     )     )
     self.assertTrue(hasattr(snake_case__				,     'do_resize'     )     )
     self.assertTrue(hasattr(snake_case__				,     'size'     )     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      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				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.image_processing_class.from_dict(
         self.image_processor_dict				,     size=4_2				,     max_size=8_4				,     pad_and_return_pixel_mask=snake_case__     )
     self.assertEqual(image_processor.size				,     {'shortest_edge': 4_2, 'longest_edge': 8_4}     )
     self.assertEqual(image_processor.do_pad				,     snake_case__     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.image_processor_tester.get_expected_values(snake_case__     )

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

     # Test batched
     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.image_processor_tester.get_expected_values(snake_case__				,     batched=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      image_processing(snake_case__				,     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			:   List[Any]     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.image_processor_tester.get_expected_values(snake_case__     )

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

     # Test batched
     SCREAMING_SNAKE_CASE		    =      image_processing(snake_case__				,     return_tensors='pt'     ).pixel_values

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.image_processor_tester.get_expected_values(snake_case__				,     batched=snake_case__     )

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

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.image_processor_tester.get_expected_values(snake_case__     )

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

     # Test batched
     SCREAMING_SNAKE_CASE		    =      image_processing(snake_case__				,     return_tensors='pt'     ).pixel_values

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.image_processor_tester.get_expected_values(snake_case__				,     batched=snake_case__     )

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

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(**self.image_processor_dict     )
     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(do_resize=snake_case__				,     do_normalize=snake_case__				,     do_rescale=snake_case__     )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE		    =      prepare_image_inputs(self.image_processor_tester				,     equal_resolution=snake_case__				,     torchify=snake_case__     )
     for image in image_inputs:
      self.assertIsInstance(snake_case__				,     torch.Tensor     )

     # Test whether the method "pad" and calling the image processor return the same tensors
     SCREAMING_SNAKE_CASE		    =      image_processing_a.pad(snake_case__				,     return_tensors='pt'     )
     SCREAMING_SNAKE_CASE		    =      image_processing_a(snake_case__				,     return_tensors='pt'     )

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

    @slow
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      {'image_id': 3_9_7_6_9, 'annotations': target}

     # encode them
     SCREAMING_SNAKE_CASE		    =      YolosImageProcessor.from_pretrained('hustvl/yolos-small'     )
     SCREAMING_SNAKE_CASE		    =      image_processing(images=snake_case__				,     annotations=snake_case__				,     return_tensors='pt'     )

     # verify pixel values
     SCREAMING_SNAKE_CASE		    =      torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
     self.assertEqual(encoding['pixel_values'].shape				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,     snake_case__				,     atol=1E-4     )     )

     # verify area
     SCREAMING_SNAKE_CASE		    =      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,     snake_case__     )     )
     # verify boxes
     SCREAMING_SNAKE_CASE		    =      torch.Size([6, 4]     )
     self.assertEqual(encoding['labels'][0]['boxes'].shape				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,     snake_case__				,     atol=1E-3     )     )
     # verify image_id
     SCREAMING_SNAKE_CASE		    =      torch.tensor([3_9_7_6_9]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,     snake_case__     )     )
     # verify is_crowd
     SCREAMING_SNAKE_CASE		    =      torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,     snake_case__     )     )
     # verify class_labels
     SCREAMING_SNAKE_CASE		    =      torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,     snake_case__     )     )
     # verify orig_size
     SCREAMING_SNAKE_CASE		    =      torch.tensor([4_8_0, 6_4_0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,     snake_case__     )     )
     # verify size
     SCREAMING_SNAKE_CASE		    =      torch.tensor([8_0_0, 1_0_6_6]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,     snake_case__     )     )




    @slow
    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

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

     # encode them
     SCREAMING_SNAKE_CASE		    =      YolosImageProcessor(format='coco_panoptic'     )
     SCREAMING_SNAKE_CASE		    =      image_processing(images=snake_case__				,     annotations=snake_case__				,     masks_path=snake_case__				,     return_tensors='pt'     )

     # verify pixel values
     SCREAMING_SNAKE_CASE		    =      torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
     self.assertEqual(encoding['pixel_values'].shape				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,     snake_case__				,     atol=1E-4     )     )

     # verify area
     SCREAMING_SNAKE_CASE		    =      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,     snake_case__     )     )
     # verify boxes
     SCREAMING_SNAKE_CASE		    =      torch.Size([6, 4]     )
     self.assertEqual(encoding['labels'][0]['boxes'].shape				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,     snake_case__				,     atol=1E-3     )     )
     # verify image_id
     SCREAMING_SNAKE_CASE		    =      torch.tensor([3_9_7_6_9]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,     snake_case__     )     )
     # verify is_crowd
     SCREAMING_SNAKE_CASE		    =      torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,     snake_case__     )     )
     # verify class_labels
     SCREAMING_SNAKE_CASE		    =      torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,     snake_case__     )     )
     # verify masks
     SCREAMING_SNAKE_CASE		    =      8_2_2_8_7_3
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,     snake_case__     )
     # verify orig_size
     SCREAMING_SNAKE_CASE		    =      torch.tensor([4_8_0, 6_4_0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,     snake_case__     )     )
     # verify size
     SCREAMING_SNAKE_CASE		    =      torch.tensor([8_0_0, 1_0_6_6]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,     snake_case__     )     )




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a_     :    str     		=   [
    "EAGER",
    "AOT_EAGER",
    "INDUCTOR",
    "NVFUSER",
    "AOT_NVFUSER",
    "AOT_CUDAGRAPHS",
    "OFI",
    "FX2TRT",
    "ONNXRT",
    "IPEX",
]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	List[Any]=None		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	str=None						)		->				Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      True
 while ask_again:
  SCREAMING_SNAKE_CASE		    =      input(_UpperCamelCase						)
  try:
   if default is not None and len(_UpperCamelCase						) == 0:
    return default
   return convert_value(_UpperCamelCase						) if convert_value is not None else result
  except Exception:
   if error_message is not None:
    print(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]=[]		,				_UpperCamelCase		:	Optional[int]=None		,				_UpperCamelCase		:	Optional[Any]=0						)		->				List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BulletMenu(_UpperCamelCase		,				_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      menu.run(default_choice=_UpperCamelCase						)
 return convert_value(_UpperCamelCase						) if convert_value is not None else result


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(_UpperCamelCase						)
 return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(_UpperCamelCase						)
 return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]						)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(_UpperCamelCase						)
 return DynamoBackend(DYNAMO_BACKENDS[value]						).value


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(_UpperCamelCase						)
 return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      int(_UpperCamelCase						)
 return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict						)		->				str:
 '''simple docstring'''


 return {"yes": True, "no": False}[value.lower()]




class   UpperCamelCase			(  argparse.RawDescriptionHelpFormatter ):

    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Tuple				,     snake_case__			:   Optional[Any]				,     snake_case__			:   List[str]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      super()._format_usage(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      usage.replace('<command> [<args>] '				,     ''     )
     return usage

