






'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor






class     a_						(      lowerCamelCase    ):

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.config_class(**self.inputs_dict				)
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""tf_padding"""				)				)
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""depth_multiplier"""				)				)






class     a_						:

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=13      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=32      ,		_SCREAMING_SNAKE_CASE=0.2_5      ,		_SCREAMING_SNAKE_CASE=8      ,		_SCREAMING_SNAKE_CASE=8      ,		_SCREAMING_SNAKE_CASE=6      ,		_SCREAMING_SNAKE_CASE=32      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE="relu6"      ,		_SCREAMING_SNAKE_CASE=1280      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.0_2      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=10      ,		_SCREAMING_SNAKE_CASE=None      ,		)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = image_size
        UpperCamelCase      = depth_multiplier
        UpperCamelCase      = depth_divisible_by
        UpperCamelCase      = min_depth
        UpperCamelCase      = expand_ratio
        UpperCamelCase      = tf_padding
        UpperCamelCase      = output_stride
        UpperCamelCase      = first_layer_is_expansion
        UpperCamelCase      = finegrained_output
        UpperCamelCase      = hidden_act
        UpperCamelCase      = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier				)
        UpperCamelCase      = classifier_dropout_prob
        UpperCamelCase      = use_labels
        UpperCamelCase      = is_training
        UpperCamelCase      = num_labels
        UpperCamelCase      = initializer_range
        UpperCamelCase      = scope

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        UpperCamelCase      = None
        UpperCamelCase      = None
        if self.use_labels:
              UpperCamelCase      = ids_tensor([self.batch_size]      ,		self.num_labels				)
              UpperCamelCase      = ids_tensor([self.batch_size, self.image_size, self.image_size]      ,		self.num_labels				)

        UpperCamelCase      = self.get_config()

        return config, pixel_values, labels, pixel_labels

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        return MobileNetVaConfig(
            num_channels=self.num_channels      ,		image_size=self.image_size      ,		depth_multiplier=self.depth_multiplier      ,		depth_divisible_by=self.depth_divisible_by      ,		min_depth=self.min_depth      ,		expand_ratio=self.expand_ratio      ,		output_stride=self.output_stride      ,		first_layer_is_expansion=self.first_layer_is_expansion      ,		finegrained_output=self.finegrained_output      ,		hidden_act=self.hidden_act      ,		tf_padding=self.tf_padding      ,		classifier_dropout_prob=self.classifier_dropout_prob      ,		initializer_range=self.initializer_range      ,		)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaModel(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(
            result.last_hidden_state.shape      ,		(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )      ,		)
        self.parent.assertEqual(
            result.pooler_output.shape      ,		(self.batch_size, self.last_hidden_size)      ,		)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE      ,		labels=_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(
            result.logits.shape      ,		(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )      ,		)
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE      ,		labels=_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(
            result.logits.shape      ,		(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )      ,		)


  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.prepare_config_and_inputs()
        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = config_and_inputs
        UpperCamelCase      = {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class     a_						(      lowerCamelCase     ,	lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				(
      (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  lowercase							=				(
      {
          """feature-extraction""": MobileNetVaModel,
          """image-classification""": MobileNetVaForImageClassification,
          """image-segmentation""": MobileNetVaForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase							=				False
  lowercase							=				False
  lowercase							=				False
  lowercase							=				False

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaModelTester(self				)
        UpperCamelCase      = MobileNetVaConfigTester(self      ,		config_class=_SCREAMING_SNAKE_CASE      ,		has_text_modality=_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""				)
  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""				)
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV2 does not output attentions"""				)
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase      = model_class(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCamelCase      = [*signature.parameters.keys()]

              UpperCamelCase      = ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = model_class(_SCREAMING_SNAKE_CASE				)
              model.to(_SCREAMING_SNAKE_CASE				)
              model.eval()

              with torch.no_grad():
                    UpperCamelCase      = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)

              UpperCamelCase      = outputs.hidden_states

              UpperCamelCase      = 16
              self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase  ,UpperCamelCase      = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase      = True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase      = True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE				)


  @slow
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase      = MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)







def    lowercase__			(				)->			Any:
      UpperCamelCase      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
      return image






@require_torch
@require_vision
class     a_						(      unittest.TestCase    ):

  @cached_property
  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        return (
            MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""				) if is_vision_available() else None
        )

  @slow
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""				).to(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.default_image_processor
        UpperCamelCase      = prepare_img()
        UpperCamelCase      = image_processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).to(_SCREAMING_SNAKE_CASE				)

        # forward pass
        with torch.no_grad():
              UpperCamelCase      = model(**_SCREAMING_SNAKE_CASE				)

        # verify the logits
        UpperCamelCase      = torch.Size((1, 1001)				)
        self.assertEqual(outputs.logits.shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]				).to(_SCREAMING_SNAKE_CASE				)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)


  @slow
  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""				)
        UpperCamelCase      = model.to(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""				)

        UpperCamelCase      = prepare_img()
        UpperCamelCase      = image_processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).to(_SCREAMING_SNAKE_CASE				)

        # forward pass
        with torch.no_grad():
              UpperCamelCase      = model(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = outputs.logits

        # verify the logits
        UpperCamelCase      = torch.Size((1, 21, 65, 65)				)
        self.assertEqual(logits.shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor(
            [
                [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
            ]      ,		device=_SCREAMING_SNAKE_CASE      ,		)

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)








'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger('transformers.models.speecht5')







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      hf_model.apply_weight_norm()

      UpperCamelCase      = checkpoint["""input_conv.weight_g"""]
      UpperCamelCase      = checkpoint["""input_conv.weight_v"""]
      UpperCamelCase      = checkpoint["""input_conv.bias"""]

      for i in range(len(config.upsample_rates   )   ):
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.weight_g"]
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.weight_v"]
            UpperCamelCase      = 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      = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

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

      UpperCamelCase      = checkpoint["""output_conv.1.weight_g"""]
      UpperCamelCase      = checkpoint["""output_conv.1.weight_v"""]
      UpperCamelCase      = checkpoint["""output_conv.1.bias"""]

      hf_model.remove_weight_norm()







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

      UpperCamelCase      = np.load(__UpperCamelCase   )
      UpperCamelCase      = stats[0].reshape(-1   )
      UpperCamelCase      = stats[1].reshape(-1   )
      UpperCamelCase      = torch.from_numpy(__UpperCamelCase   ).float()
      UpperCamelCase      = 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__":
     SCREAMING_SNAKE_CASE__     =       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.'
     )

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