

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   ):

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

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			DeformableDetrImageProcessingTester(self    )

    @property
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()

    def UpperCamelCase			(			self :      str    ):

     """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__							,    'do_rescale'    )    )
     self.assertTrue(hasattr(snake_case__							,    'do_pad'    )    )
     self.assertTrue(hasattr(snake_case__							,    'size'    )    )

    def UpperCamelCase			(			self :      List[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 :      List[Any]    ):

     """simple docstring"""






     pass

    def UpperCamelCase			(			self :      Any    ):

     """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 :      str    ):

     """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 :      Any    ):

     """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,
         )							,    )

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

     """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								=			DeformableDetrImageProcessor()
     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 :      Union[str, Any]    ):

     """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								=			DeformableDetrImageProcessor(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__    )    )




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a_    :						List[Any]    		=      logging.get_logger("transformers.models.speecht5")


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


 hf_model.apply_weight_norm()

 SCREAMING_SNAKE_CASE								=			checkpoint['input_conv.weight_g']
 SCREAMING_SNAKE_CASE								=			checkpoint['input_conv.weight_v']
 SCREAMING_SNAKE_CASE								=			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates	)	):
  SCREAMING_SNAKE_CASE								=			checkpoint[f"""upsamples.{i}.1.weight_g"""]
  SCREAMING_SNAKE_CASE								=			checkpoint[f"""upsamples.{i}.1.weight_v"""]
  SCREAMING_SNAKE_CASE								=			checkpoint[f"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates	) * len(config.resblock_kernel_sizes	)	):
  for j in range(len(config.resblock_dilation_sizes	)	):
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

 SCREAMING_SNAKE_CASE								=			checkpoint['output_conv.1.weight_g']
 SCREAMING_SNAKE_CASE								=			checkpoint['output_conv.1.weight_v']
 SCREAMING_SNAKE_CASE								=			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any=None		,							_UpperCamelCase		:  List[str]=None		,							)		->							Tuple:
 '''simple docstring'''


 if config_path is not None:
  SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig.from_pretrained(_UpperCamelCase	)
 else:
  SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig()

 SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			torch.load(_UpperCamelCase	)
 load_weights(orig_checkpoint['model']['generator']		,							_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			np.load(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			stats[0].reshape(-1	)
 SCREAMING_SNAKE_CASE								=			stats[1].reshape(-1	)
 SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	).float()
 SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	).float()

 model.save_pretrained(_UpperCamelCase	)

 if repo_id:
  print('Pushing to the hub...'	)
  model.push_to_hub(_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						Dict    		=      argparse.ArgumentParser()
      parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
      parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
      )

      a_    :						Tuple    		=      parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )

