'''simple docstring'''

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 DetrImageProcessor


class     __UpperCAmelCase       ( unittest.TestCase		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=7    ,     _lowerCamelCase=3    ,     _lowerCamelCase=30    ,     _lowerCamelCase=400    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase=1 / 255    ,     _lowerCamelCase=True    ,     _lowerCamelCase=[0.5, 0.5, 0.5]    ,     _lowerCamelCase=[0.5, 0.5, 0.5]    ,     _lowerCamelCase=True    ,     ):
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     lowerCAmelCase_			=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      min_resolution
     lowerCAmelCase_			=      max_resolution
     lowerCAmelCase_			=      do_resize
     lowerCAmelCase_			=      size
     lowerCAmelCase_			=      do_rescale
     lowerCAmelCase_			=      rescale_factor
     lowerCAmelCase_			=      do_normalize
     lowerCAmelCase_			=      image_mean
     lowerCAmelCase_			=      image_std
     lowerCAmelCase_			=      do_pad





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







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

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

     return expected_height, expected_width






@require_torch
@require_vision
class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				List[Any]											= DetrImageProcessor if is_vision_available() else None





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      DetrImageProcessingTester(self )





  @property
  def 						UpperCAmelCase_	(	self ):
     return self.image_processor_tester.prepare_image_processor_dict()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(_lowercase    ,     '''image_mean''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''image_std''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''do_normalize''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''do_rescale''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''rescale_factor''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''do_resize''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''size''' ) )
     self.assertTrue(hasattr(_lowercase    ,     '''do_pad''' ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,     {'''shortest_edge''': 18, '''longest_edge''': 1333} )
     self.assertEqual(image_processor.do_pad    ,     _lowercase )

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





  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self ):
     # Initialize image_processing
     lowerCAmelCase_			=      self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowerCAmelCase_			=      prepare_image_inputs(self.image_processor_tester    ,     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

     lowerCAmelCase_	,lowerCAmelCase_			=      self.image_processor_tester.get_expected_values(_lowercase )

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

     # Test batched
     lowerCAmelCase_	,lowerCAmelCase_			=      self.image_processor_tester.get_expected_values(_lowercase    ,     batched=_lowercase )

     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,
             expected_height,
             expected_width,
         )    ,     )





  def 						UpperCAmelCase_	(	self ):
     # Initialize image_processing
     lowerCAmelCase_			=      self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowerCAmelCase_			=      prepare_image_inputs(self.image_processor_tester    ,     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

     lowerCAmelCase_	,lowerCAmelCase_			=      self.image_processor_tester.get_expected_values(_lowercase )

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

     # Test batched
     lowerCAmelCase_			=      image_processing(_lowercase    ,     return_tensors='''pt''' ).pixel_values

     lowerCAmelCase_	,lowerCAmelCase_			=      self.image_processor_tester.get_expected_values(_lowercase    ,     batched=_lowercase )

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





  def 						UpperCAmelCase_	(	self ):
     # Initialize image_processing
     lowerCAmelCase_			=      self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowerCAmelCase_			=      prepare_image_inputs(self.image_processor_tester    ,     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

     lowerCAmelCase_	,lowerCAmelCase_			=      self.image_processor_tester.get_expected_values(_lowercase )

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

     # Test batched
     lowerCAmelCase_			=      image_processing(_lowercase    ,     return_tensors='''pt''' ).pixel_values

     lowerCAmelCase_	,lowerCAmelCase_			=      self.image_processor_tester.get_expected_values(_lowercase    ,     batched=_lowercase )

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





  @slow
  def 						UpperCAmelCase_	(	self ):
     # prepare image and target
     lowerCAmelCase_			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,     '''r''' ) as f:
        lowerCAmelCase_			=      json.loads(f.read() )

     lowerCAmelCase_			=      {'''image_id''': 3_9769, '''annotations''': target}

     # encode them
     lowerCAmelCase_			=      DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
     lowerCAmelCase_			=      image_processing(images=_lowercase    ,     annotations=_lowercase    ,     return_tensors='''pt''' )

     # verify pixel values
     lowerCAmelCase_			=      torch.Size([1, 3, 800, 1066] )
     self.assertEqual(encoding['''pixel_values'''].shape    ,     _lowercase )

     lowerCAmelCase_			=      torch.tensor([0.27_96, 0.31_38, 0.34_81] )
     self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,     _lowercase    ,     atol=1E-4 ) )

     # verify area
     lowerCAmelCase_			=      torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,     _lowercase ) )
     # verify boxes
     lowerCAmelCase_			=      torch.Size([6, 4] )
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,     _lowercase )
     lowerCAmelCase_			=      torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,     _lowercase    ,     atol=1E-3 ) )
     # verify image_id
     lowerCAmelCase_			=      torch.tensor([3_9769] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,     _lowercase ) )
     # verify is_crowd
     lowerCAmelCase_			=      torch.tensor([0, 0, 0, 0, 0, 0] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,     _lowercase ) )
     # verify class_labels
     lowerCAmelCase_			=      torch.tensor([75, 75, 63, 65, 17, 17] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,     _lowercase ) )
     # verify orig_size
     lowerCAmelCase_			=      torch.tensor([480, 640] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,     _lowercase ) )
     # verify size
     lowerCAmelCase_			=      torch.tensor([800, 1066] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,     _lowercase ) )







  @slow
  def 						UpperCAmelCase_	(	self ):
     # prepare image, target and masks_path
     lowerCAmelCase_			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,     '''r''' ) as f:
        lowerCAmelCase_			=      json.loads(f.read() )

     lowerCAmelCase_			=      {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

     # encode them
     lowerCAmelCase_			=      DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
     lowerCAmelCase_			=      image_processing(images=_lowercase    ,     annotations=_lowercase    ,     masks_path=_lowercase    ,     return_tensors='''pt''' )

     # verify pixel values
     lowerCAmelCase_			=      torch.Size([1, 3, 800, 1066] )
     self.assertEqual(encoding['''pixel_values'''].shape    ,     _lowercase )

     lowerCAmelCase_			=      torch.tensor([0.27_96, 0.31_38, 0.34_81] )
     self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,     _lowercase    ,     atol=1E-4 ) )

     # verify area
     lowerCAmelCase_			=      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,     _lowercase ) )
     # verify boxes
     lowerCAmelCase_			=      torch.Size([6, 4] )
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,     _lowercase )
     lowerCAmelCase_			=      torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,     _lowercase    ,     atol=1E-3 ) )
     # verify image_id
     lowerCAmelCase_			=      torch.tensor([3_9769] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,     _lowercase ) )
     # verify is_crowd
     lowerCAmelCase_			=      torch.tensor([0, 0, 0, 0, 0, 0] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,     _lowercase ) )
     # verify class_labels
     lowerCAmelCase_			=      torch.tensor([17, 17, 63, 75, 75, 93] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,     _lowercase ) )
     # verify masks
     lowerCAmelCase_			=      82_2873
     self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,     _lowercase )
     # verify orig_size
     lowerCAmelCase_			=      torch.tensor([480, 640] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,     _lowercase ) )
     # verify size
     lowerCAmelCase_			=      torch.tensor([800, 1066] )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,     _lowercase ) )











'''simple docstring'''
import os
import sys

_SCREAMING_SNAKE_CASE			=						os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_SCREAMING_SNAKE_CASE			=						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		int:
			return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		List[str]:
			return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModel.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Dict:
			return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		str:
			return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )




