'''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 DeformableDetrImageProcessor






class 		__A       (unittest.TestCase   ):






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






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

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

              return expected_height, expected_width






@require_torch
@require_vision
class 		__A       (__magic_name__ ,					unittest.TestCase   ):
       snake_case       :List[Any]				    =					DeformableDetrImageProcessor if is_vision_available() else None






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		DeformableDetrImageProcessingTester(self      )






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				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_				,     "do_rescale"      )      )
              self.assertTrue(hasattr(UpperCamelCase_				,     "do_pad"      )      )
              self.assertTrue(hasattr(UpperCamelCase_				,     "size"      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		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				,     UpperCamelCase_      )

              __UpperCAmelCase  :				Optional[Any]       				=		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 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self      ):
              # Initialize image_processing
              __UpperCAmelCase  :				Optional[int]       				=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              __UpperCAmelCase  :				Dict       				=		prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCamelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCamelCase_				,     Image.Image      )

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

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		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
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self.image_processor_tester.get_expected_values(UpperCamelCase_				,     batched=UpperCamelCase_      )

              __UpperCAmelCase  :				Union[str, 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 			_snake_case						(      self      ):
              # Initialize image_processing
              __UpperCAmelCase  :				int       				=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              __UpperCAmelCase  :				int       				=		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
              __UpperCAmelCase  :				List[Any]       				=		image_processing(image_inputs[0]				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		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
              __UpperCAmelCase  :				List[Any]       				=		image_processing(UpperCamelCase_				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		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 			_snake_case						(      self      ):
              # Initialize image_processing
              __UpperCAmelCase  :				Tuple       				=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              __UpperCAmelCase  :				Union[str, 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
              __UpperCAmelCase  :				Optional[Any]       				=		image_processing(image_inputs[0]				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase				, __UpperCAmelCase  :				List[Any]       				=		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
              __UpperCAmelCase  :				Union[str, Any]       				=		image_processing(UpperCamelCase_				,     return_tensors="pt"      ).pixel_values

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

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

              # encode them
              __UpperCAmelCase  :				List[str]       				=		DeformableDetrImageProcessor()
              __UpperCAmelCase  :				Union[str, Any]       				=		image_processing(images=UpperCamelCase_				,     annotations=UpperCamelCase_				,     return_tensors="pt"      )

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

              __UpperCAmelCase  :				List[Any]       				=		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
              __UpperCAmelCase  :				List[Any]       				=		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
              __UpperCAmelCase  :				Optional[int]       				=		torch.Size([6, 4]      )
              self.assertEqual(encoding["labels"][0]["boxes"].shape				,     UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		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
              __UpperCAmelCase  :				str       				=		torch.tensor([3_97_69]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     UpperCamelCase_      )      )
              # verify is_crowd
              __UpperCAmelCase  :				Tuple       				=		torch.tensor([0, 0, 0, 0, 0, 0]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     UpperCamelCase_      )      )
              # verify class_labels
              __UpperCAmelCase  :				Any       				=		torch.tensor([75, 75, 63, 65, 17, 17]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     UpperCamelCase_      )      )
              # verify orig_size
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.tensor([4_80, 6_40]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     UpperCamelCase_      )      )
              # verify size
              __UpperCAmelCase  :				List[str]       				=		torch.tensor([8_00, 10_66]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     UpperCamelCase_      )      )







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

              __UpperCAmelCase  :				Optional[int]       				=		{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

              __UpperCAmelCase  :				Tuple       				=		pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

              # encode them
              __UpperCAmelCase  :				int       				=		DeformableDetrImageProcessor(format="coco_panoptic"      )
              __UpperCAmelCase  :				Optional[int]       				=		image_processing(images=UpperCamelCase_				,     annotations=UpperCamelCase_				,     masks_path=UpperCamelCase_				,     return_tensors="pt"      )

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

              __UpperCAmelCase  :				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
              __UpperCAmelCase  :				str       				=		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
              __UpperCAmelCase  :				Any       				=		torch.Size([6, 4]      )
              self.assertEqual(encoding["labels"][0]["boxes"].shape				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		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
              __UpperCAmelCase  :				List[str]       				=		torch.tensor([3_97_69]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     UpperCamelCase_      )      )
              # verify is_crowd
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.tensor([0, 0, 0, 0, 0, 0]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     UpperCamelCase_      )      )
              # verify class_labels
              __UpperCAmelCase  :				Any       				=		torch.tensor([17, 17, 63, 75, 75, 93]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     UpperCamelCase_      )      )
              # verify masks
              __UpperCAmelCase  :				str       				=		82_28_73
              self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     UpperCamelCase_      )
              # verify orig_size
              __UpperCAmelCase  :				str       				=		torch.tensor([4_80, 6_40]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     UpperCamelCase_      )      )
              # verify size
              __UpperCAmelCase  :				int       				=		torch.tensor([8_00, 10_66]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     UpperCamelCase_      )      )







'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	int 							=   {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					"speech_to_text_2"
       snake_case       :List[Any]				    =					["past_key_values"]
       snake_case       :str				    =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






       def __init__(      self				,     UpperCamelCase_=1_00_00				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=4				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_=10_24				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Any       				=		vocab_size
              __UpperCAmelCase  :				Optional[int]       				=		d_model
              __UpperCAmelCase  :				Tuple       				=		decoder_ffn_dim
              __UpperCAmelCase  :				List[str]       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Dict       				=		dropout
              __UpperCAmelCase  :				Optional[Any]       				=		attention_dropout
              __UpperCAmelCase  :				int       				=		activation_dropout
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Tuple       				=		init_std
              __UpperCAmelCase  :				Any       				=		decoder_layerdrop
              __UpperCAmelCase  :				str       				=		use_cache
              __UpperCAmelCase  :				int       				=		decoder_layers
              __UpperCAmelCase  :				Any       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
              __UpperCAmelCase  :				Union[str, Any]       				=		max_target_positions

              super().__init__(
                  pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     decoder_start_token_id=UpperCamelCase_				,     **UpperCamelCase_				,     )






