
'''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 ,       lowercase_ ,       lowercase_=7 ,       lowercase_=3 ,       lowercase_=30 ,       lowercase_=4_00 ,       lowercase_=True ,       lowercase_=None ,       lowercase_=True ,       lowercase_=1 / 2_55 ,       lowercase_=True ,       lowercase_=[0.5, 0.5, 0.5] ,       lowercase_=[0.5, 0.5, 0.5] ,       lowercase_=True ,       ):
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     snake_case_ :    Union[str, Any]     			=						size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
     snake_case_ :    Optional[Any]     			=						parent
     snake_case_ :    List[Any]     			=						batch_size
     snake_case_ :    str     			=						num_channels
     snake_case_ :    Optional[int]     			=						min_resolution
     snake_case_ :    List[str]     			=						max_resolution
     snake_case_ :    str     			=						do_resize
     snake_case_ :    Tuple     			=						size
     snake_case_ :    Tuple     			=						do_rescale
     snake_case_ :    List[str]     			=						rescale_factor
     snake_case_ :    Optional[int]     			=						do_normalize
     snake_case_ :    List[Any]     			=						image_mean
     snake_case_ :    List[Any]     			=						image_std
     snake_case_ :    Optional[Any]     			=						do_pad





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

     else:
         snake_case_ :    str     			=						[]
         for image in image_inputs:
             snake_case_ :    Optional[Any]     			=						self.get_expected_values([image])
             expected_values.append((expected_height, expected_width))
         snake_case_ :    Optional[int]     			=						max(lowercase_ ,       key=lambda lowercase_: item[0])[0]
         snake_case_ :    List[str]     			=						max(lowercase_ ,       key=lambda lowercase_: item[1])[1]

     return expected_height, expected_width






@require_torch
@require_vision
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						DetrImageProcessor if is_vision_available() else None





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						DetrImageProcessingTester(self)





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





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						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        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size ,       {"shortest_edge": 18, "longest_edge": 13_33})
     self.assertEqual(image_processor.do_pad ,       lowercase_)

     snake_case_ :    int     			=						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        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    List[Any]     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     snake_case_ :    Dict     			=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=lowercase_)
     for image in image_inputs:
         self.assertIsInstance(lowercase_ ,       Image.Image)

     # Test not batched input
     snake_case_ :    List[Any]     			=						image_processing(image_inputs[0] ,       return_tensors="pt").pixel_values

     snake_case_ :    Dict     			=						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
     snake_case_ :    List[Any]     			=						self.image_processor_tester.get_expected_values(lowercase_ ,       batched=lowercase_)

     snake_case_ :    Dict     			=						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        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Optional[Any]     			=						self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     snake_case_ :    int     			=						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
     snake_case_ :    Dict     			=						image_processing(image_inputs[0] ,       return_tensors="pt").pixel_values

     snake_case_ :    Any     			=						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
     snake_case_ :    Tuple     			=						image_processing(lowercase_ ,       return_tensors="pt").pixel_values

     snake_case_ :    int     			=						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        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    str     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     snake_case_ :    Tuple     			=						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
     snake_case_ :    int     			=						image_processing(image_inputs[0] ,       return_tensors="pt").pixel_values

     snake_case_ :    Optional[Any]     			=						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
     snake_case_ :    List[Any]     			=						image_processing(lowercase_ ,       return_tensors="pt").pixel_values

     snake_case_ :    Tuple     			=						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        snake_case__		(    self):
     # prepare image and target
     snake_case_ :    Dict     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,       "r") as f:
         snake_case_ :    List[str]     			=						json.loads(f.read())

     snake_case_ :    Optional[Any]     			=						{"image_id": 3_97_69, "annotations": target}

     # encode them
     snake_case_ :    str     			=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
     snake_case_ :    int     			=						image_processing(images=lowercase_ ,       annotations=lowercase_ ,       return_tensors="pt")

     # verify pixel values
     snake_case_ :    List[str]     			=						torch.Size([1, 3, 8_00, 10_66])
     self.assertEqual(encoding["pixel_values"].shape ,       lowercase_)

     snake_case_ :    Optional[int]     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481])
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,       lowercase_ ,       atol=1E-4))

     # verify area
     snake_case_ :    List[Any]     			=						torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,       lowercase_))
     # verify boxes
     snake_case_ :    Any     			=						torch.Size([6, 4])
     self.assertEqual(encoding["labels"][0]["boxes"].shape ,       lowercase_)
     snake_case_ :    Optional[Any]     			=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215])
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,       lowercase_ ,       atol=1E-3))
     # verify image_id
     snake_case_ :    List[str]     			=						torch.tensor([3_97_69])
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,       lowercase_))
     # verify is_crowd
     snake_case_ :    Optional[int]     			=						torch.tensor([0, 0, 0, 0, 0, 0])
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,       lowercase_))
     # verify class_labels
     snake_case_ :    Tuple     			=						torch.tensor([75, 75, 63, 65, 17, 17])
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,       lowercase_))
     # verify orig_size
     snake_case_ :    int     			=						torch.tensor([4_80, 6_40])
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,       lowercase_))
     # verify size
     snake_case_ :    Any     			=						torch.tensor([8_00, 10_66])
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,       lowercase_))





 @slow
 def        snake_case__		(    self):
     # prepare image, target and masks_path
     snake_case_ :    int     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,       "r") as f:
         snake_case_ :    Optional[Any]     			=						json.loads(f.read())

     snake_case_ :    List[Any]     			=						{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

     snake_case_ :    Union[str, Any]     			=						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

     # encode them
     snake_case_ :    Any     			=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
     snake_case_ :    Optional[Any]     			=						image_processing(images=lowercase_ ,       annotations=lowercase_ ,       masks_path=lowercase_ ,       return_tensors="pt")

     # verify pixel values
     snake_case_ :    Any     			=						torch.Size([1, 3, 8_00, 10_66])
     self.assertEqual(encoding["pixel_values"].shape ,       lowercase_)

     snake_case_ :    Optional[int]     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481])
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,       lowercase_ ,       atol=1E-4))

     # verify area
     snake_case_ :    Optional[Any]     			=						torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,       lowercase_))
     # verify boxes
     snake_case_ :    Optional[int]     			=						torch.Size([6, 4])
     self.assertEqual(encoding["labels"][0]["boxes"].shape ,       lowercase_)
     snake_case_ :    Tuple     			=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625])
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,       lowercase_ ,       atol=1E-3))
     # verify image_id
     snake_case_ :    List[str]     			=						torch.tensor([3_97_69])
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,       lowercase_))
     # verify is_crowd
     snake_case_ :    Union[str, Any]     			=						torch.tensor([0, 0, 0, 0, 0, 0])
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,       lowercase_))
     # verify class_labels
     snake_case_ :    Tuple     			=						torch.tensor([17, 17, 63, 75, 75, 93])
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,       lowercase_))
     # verify masks
     snake_case_ :    Any     			=						82_28_73
     self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,       lowercase_)
     # verify orig_size
     snake_case_ :    int     			=						torch.tensor([4_80, 6_40])
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,       lowercase_))
     # verify size
     snake_case_ :    Optional[int]     			=						torch.tensor([8_00, 10_66])
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,       lowercase_))






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




