

'''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 YolosImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Optional[int]=7					,      lowercase_			:					Any=3					,      lowercase_			:					Optional[int]=30					,      lowercase_			:					str=400					,      lowercase_			:					List[Any]=True					,      lowercase_			:					Union[str, Any]=None					,      lowercase_			:					List[Any]=True					,      lowercase_			:					Optional[Any]=[0.5, 0.5, 0.5]					,      lowercase_			:					Union[str, Any]=[0.5, 0.5, 0.5]					,      lowercase_			:					Optional[int]=True					,      lowercase_			:					Union[str, Any]=1 / 255					,      lowercase_			:					str=True					,      )						->     int:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      UpperCAmelCase	:     Tuple						 =			size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
      UpperCAmelCase	:     Optional[Any]						 =			parent
      UpperCAmelCase	:     Optional[int]						 =			batch_size
      UpperCAmelCase	:     Any						 =			num_channels
      UpperCAmelCase	:     List[str]						 =			min_resolution
      UpperCAmelCase	:     Any						 =			max_resolution
      UpperCAmelCase	:     Dict						 =			do_resize
      UpperCAmelCase	:     Tuple						 =			size
      UpperCAmelCase	:     List[Any]						 =			do_normalize
      UpperCAmelCase	:     Tuple						 =			image_mean
      UpperCAmelCase	:     Union[str, Any]						 =			image_std
      UpperCAmelCase	:     Tuple						 =			do_rescale
      UpperCAmelCase	:     Optional[int]						 =			rescale_factor
      UpperCAmelCase	:     Optional[Any]						 =			do_pad


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

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

      return expected_height, expected_width




@require_torch
@require_vision
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  YolosImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Tuple:
      UpperCAmelCase	:     Tuple						 =			YolosImageProcessingTester(self	)


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


     def        UpperCAmelCase_	(		self			:					int	)						->     List[str]:
      UpperCAmelCase	:     List[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_resize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Dict:
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 18, 'longest_edge': 1_333}	)
      self.assertEqual(image_processor.do_pad					,      lowercase_	)

      UpperCAmelCase	:     List[Any]						 =			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			:					List[str]	)						->     int:
      pass


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      # Initialize image_processing
      UpperCAmelCase	:     str						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      UpperCAmelCase	:     Union[str, Any]						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      Image.Image	)

      # Test not batched input
      UpperCAmelCase	:     List[str]						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase ,			UpperCAmelCase	:     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
      UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			self.image_processor_tester.get_expected_values(lowercase_					,      batched=lowercase_	)

      UpperCAmelCase	:     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        UpperCAmelCase_	(		self			:					str	)						->     Union[str, Any]:
      # Initialize image_processing
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Tuple						 =			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
      UpperCAmelCase	:     int						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase ,			UpperCAmelCase	:     List[str]						 =			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
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase ,			UpperCAmelCase	:     str						 =			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			:					List[str]	)						->     Optional[int]:
      # Initialize image_processing
      UpperCAmelCase	:     Union[str, Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     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
      UpperCAmelCase	:     List[Any]						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase ,			UpperCAmelCase	:     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
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase ,			UpperCAmelCase	:     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,
          )					,      )


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:
      # Initialize image_processings
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(do_resize=lowercase_					,      do_normalize=lowercase_					,      do_rescale=lowercase_	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Dict						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test whether the method "pad" and calling the image processor return the same tensors
      UpperCAmelCase	:     Any						 =			image_processing_a.pad(lowercase_					,      return_tensors='pt'	)
      UpperCAmelCase	:     Union[str, Any]						 =			image_processing_a(lowercase_					,      return_tensors='pt'	)

      self.assertTrue(
          torch.allclose(encoded_images_with_method['pixel_values']					,      encoded_images['pixel_values']					,      atol=1E-4	)	)


     @slow
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Union[str, Any]:
      # prepare image and target
      UpperCAmelCase	:     Tuple						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'					,      'r'	) as f:
       UpperCAmelCase	:     int						 =			json.loads(f.read()	)

      UpperCAmelCase	:     Dict						 =			{'image_id': 39_769, 'annotations': target}

      # encode them
      UpperCAmelCase	:     Any						 =			YolosImageProcessor.from_pretrained('hustvl/yolos-small'	)
      UpperCAmelCase	:     str						 =			image_processing(images=lowercase_					,      annotations=lowercase_					,      return_tensors='pt'	)

      # verify pixel values
      UpperCAmelCase	:     int						 =			torch.Size([1, 3, 800, 1_066]	)
      self.assertEqual(encoding['pixel_values'].shape					,      lowercase_	)

      UpperCAmelCase	:     Dict						 =			torch.tensor([0.2796, 0.3138, 0.3481]	)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,      lowercase_					,      atol=1E-4	)	)

      # verify area
      UpperCAmelCase	:     List[Any]						 =			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
      UpperCAmelCase	:     int						 =			torch.Size([6, 4]	)
      self.assertEqual(encoding['labels'][0]['boxes'].shape					,      lowercase_	)
      UpperCAmelCase	:     Any						 =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,      lowercase_					,      atol=1E-3	)	)
      # verify image_id
      UpperCAmelCase	:     List[str]						 =			torch.tensor([39_769]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,      lowercase_	)	)
      # verify is_crowd
      UpperCAmelCase	:     str						 =			torch.tensor([0, 0, 0, 0, 0, 0]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,      lowercase_	)	)
      # verify class_labels
      UpperCAmelCase	:     str						 =			torch.tensor([75, 75, 63, 65, 17, 17]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,      lowercase_	)	)
      # verify orig_size
      UpperCAmelCase	:     Optional[int]						 =			torch.tensor([480, 640]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,      lowercase_	)	)
      # verify size
      UpperCAmelCase	:     str						 =			torch.tensor([800, 1_066]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,      lowercase_	)	)




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

      UpperCAmelCase	:     List[str]						 =			{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

      UpperCAmelCase	:     int						 =			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'	)

      # encode them
      UpperCAmelCase	:     Optional[int]						 =			YolosImageProcessor(format='coco_panoptic'	)
      UpperCAmelCase	:     str						 =			image_processing(images=lowercase_					,      annotations=lowercase_					,      masks_path=lowercase_					,      return_tensors='pt'	)

      # verify pixel values
      UpperCAmelCase	:     Optional[int]						 =			torch.Size([1, 3, 800, 1_066]	)
      self.assertEqual(encoding['pixel_values'].shape					,      lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([0.2796, 0.3138, 0.3481]	)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,      lowercase_					,      atol=1E-4	)	)

      # verify area
      UpperCAmelCase	:     Union[str, Any]						 =			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
      UpperCAmelCase	:     str						 =			torch.Size([6, 4]	)
      self.assertEqual(encoding['labels'][0]['boxes'].shape					,      lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,      lowercase_					,      atol=1E-3	)	)
      # verify image_id
      UpperCAmelCase	:     Dict						 =			torch.tensor([39_769]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,      lowercase_	)	)
      # verify is_crowd
      UpperCAmelCase	:     int						 =			torch.tensor([0, 0, 0, 0, 0, 0]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,      lowercase_	)	)
      # verify class_labels
      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([17, 17, 63, 75, 75, 93]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,      lowercase_	)	)
      # verify masks
      UpperCAmelCase	:     Union[str, Any]						 =			822_873
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()					,      lowercase_	)
      # verify orig_size
      UpperCAmelCase	:     str						 =			torch.tensor([480, 640]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,      lowercase_	)	)
      # verify size
      UpperCAmelCase	:     Any						 =			torch.tensor([800, 1_066]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,      lowercase_	)	)







'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			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     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			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__":
 lowercase__         =      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."
 )

 lowercase__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )




