



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       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Tuple=7			,				lowerCAmelCase  :			Optional[Any]=3			,				lowerCAmelCase  :			List[str]=30			,				lowerCAmelCase  :			List[str]=4_00			,				lowerCAmelCase  :			List[str]=True			,				lowerCAmelCase  :			Any=None			,				lowerCAmelCase  :			List[Any]=True			,				lowerCAmelCase  :			Any=[0.5, 0.5, 0.5]			,				lowerCAmelCase  :			int=[0.5, 0.5, 0.5]			,				lowerCAmelCase  :			Dict=True			,				lowerCAmelCase  :			Optional[Any]=1 / 2_55			,				lowerCAmelCase  :			Optional[int]=True			,				)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  num_channels
         lowercase__     =  min_resolution
         lowercase__     =  max_resolution
         lowercase__     =  do_resize
         lowercase__     =  size
         lowercase__     =  do_normalize
         lowercase__     =  image_mean
         lowercase__     =  image_std
         lowercase__     =  do_rescale
         lowercase__     =  rescale_factor
         lowercase__     =  do_pad


   def 			UpperCAmelCase		(						self  :			str)	->				Any:




         """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  :			List[str]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[Any]=False)	->				Optional[Any]:




         """simple docstring"""

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

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

         return expected_height, expected_width



@require_torch
@require_vision
class       UpperCAmelCase__(							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  List[Any]       				=    DeformableDetrImageProcessor if is_vision_available() else None


   def 			UpperCAmelCase		(						self  :			Tuple)	->				str:




         """simple docstring"""

         lowercase__     =  DeformableDetrImageProcessingTester(self)


   @property
   def 			UpperCAmelCase		(						self  :			str)	->				int:




         """simple docstring"""

         return self.image_processor_tester.prepare_image_processor_dict()


   def 			UpperCAmelCase		(						self  :			str)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(lowerCAmelCase			,				'image_mean'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'image_std'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'do_normalize'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'do_resize'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'do_rescale'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'do_pad'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'size'))


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  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			,				lowerCAmelCase)

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Any:




         """simple docstring"""

         pass


   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

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

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

         lowercase__,	lowercase__     =  self.image_processor_tester.get_expected_values(lowerCAmelCase)

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

         # Test batched
         lowercase__,	lowercase__     =  self.image_processor_tester.get_expected_values(lowerCAmelCase			,				batched=lowerCAmelCase)

         lowercase__     =  image_processing(lowerCAmelCase			,				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  :			str)	->				int:




         """simple docstring"""

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

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

         lowercase__,	lowercase__     =  self.image_processor_tester.get_expected_values(lowerCAmelCase)

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

         # Test batched
         lowercase__     =  image_processing(lowerCAmelCase			,				return_tensors='pt').pixel_values

         lowercase__,	lowercase__     =  self.image_processor_tester.get_expected_values(lowerCAmelCase			,				batched=lowerCAmelCase)

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


   def 			UpperCAmelCase		(						self  :			Any)	->				Optional[int]:




         """simple docstring"""

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

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

         lowercase__,	lowercase__     =  self.image_processor_tester.get_expected_values(lowerCAmelCase)

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

         # Test batched
         lowercase__     =  image_processing(lowerCAmelCase			,				return_tensors='pt').pixel_values

         lowercase__,	lowercase__     =  self.image_processor_tester.get_expected_values(lowerCAmelCase			,				batched=lowerCAmelCase)

         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  :			Union[str, Any])	->				str:




         """simple docstring"""

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

         lowercase__     =  {'image_id': 3_97_69, 'annotations': target}

         # encode them
         lowercase__     =  DeformableDetrImageProcessor()
         lowercase__     =  image_processing(images=lowerCAmelCase			,				annotations=lowerCAmelCase			,				return_tensors='pt')

         # verify pixel values
         lowercase__     =  torch.Size([1, 3, 8_00, 10_66])
         self.assertEqual(encoding['pixel_values'].shape			,				lowerCAmelCase)

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

         # verify area
         lowercase__     =  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']			,				lowerCAmelCase))
         # verify boxes
         lowercase__     =  torch.Size([6, 4])
         self.assertEqual(encoding['labels'][0]['boxes'].shape			,				lowerCAmelCase)
         lowercase__     =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,				lowerCAmelCase			,				atol=1E-3))
         # verify image_id
         lowercase__     =  torch.tensor([3_97_69])
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,				lowerCAmelCase))
         # verify is_crowd
         lowercase__     =  torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,				lowerCAmelCase))
         # verify class_labels
         lowercase__     =  torch.tensor([75, 75, 63, 65, 17, 17])
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,				lowerCAmelCase))
         # verify orig_size
         lowercase__     =  torch.tensor([4_80, 6_40])
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,				lowerCAmelCase))
         # verify size
         lowercase__     =  torch.tensor([8_00, 10_66])
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,				lowerCAmelCase))



   @slow
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Tuple:




         """simple docstring"""

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

         lowercase__     =  {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

         # encode them
         lowercase__     =  DeformableDetrImageProcessor(format='coco_panoptic')
         lowercase__     =  image_processing(images=lowerCAmelCase			,				annotations=lowerCAmelCase			,				masks_path=lowerCAmelCase			,				return_tensors='pt')

         # verify pixel values
         lowercase__     =  torch.Size([1, 3, 8_00, 10_66])
         self.assertEqual(encoding['pixel_values'].shape			,				lowerCAmelCase)

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

         # verify area
         lowercase__     =  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']			,				lowerCAmelCase))
         # verify boxes
         lowercase__     =  torch.Size([6, 4])
         self.assertEqual(encoding['labels'][0]['boxes'].shape			,				lowerCAmelCase)
         lowercase__     =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,				lowerCAmelCase			,				atol=1E-3))
         # verify image_id
         lowercase__     =  torch.tensor([3_97_69])
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,				lowerCAmelCase))
         # verify is_crowd
         lowercase__     =  torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,				lowerCAmelCase))
         # verify class_labels
         lowercase__     =  torch.tensor([17, 17, 63, 75, 75, 93])
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,				lowerCAmelCase))
         # verify masks
         lowercase__     =  82_28_73
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,				lowerCAmelCase)
         # verify orig_size
         lowercase__     =  torch.tensor([4_80, 6_40])
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,				lowerCAmelCase))
         # verify size
         lowercase__     =  torch.tensor([8_00, 10_66])
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,				lowerCAmelCase))





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


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    "speech_to_text"
   A    :  Optional[Any]       				=    ["past_key_values"]
   A    :  Optional[int]       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple=1_00_00			,				lowerCAmelCase  :			int=12			,				lowerCAmelCase  :			int=20_48			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			str=6			,				lowerCAmelCase  :			Dict=20_48			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Optional[int]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict="relu"			,				lowerCAmelCase  :			Tuple=2_56			,				lowerCAmelCase  :			Dict=0.1			,				lowerCAmelCase  :			Optional[Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			List[str]=0			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			Any=60_00			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Optional[Any]=(5, 5)			,				lowerCAmelCase  :			Union[str, Any]=10_24			,				lowerCAmelCase  :			List[Any]=80			,				lowerCAmelCase  :			List[str]=1			,				**lowerCAmelCase  :			List[str]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  max_source_positions
         lowercase__     =  max_target_positions
         lowercase__     =  num_conv_layers
         lowercase__     =  list(lowerCAmelCase)
         lowercase__     =  conv_channels
         lowercase__     =  input_feat_per_channel
         lowercase__     =  input_channels

         if len(self.conv_kernel_sizes) != self.num_conv_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                   f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
                   f'''`config.num_conv_layers = {self.num_conv_layers}`.''')

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