

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import (
          AutoProcessor,
          BertTokenizerFast,
          BlipImageProcessor,
          GPTaTokenizer,
          InstructBlipProcessor,
          PreTrainedTokenizerFast,
      )




@require_vision
class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()

     SCREAMING_SNAKE_CASE								=			BlipImageProcessor()
     SCREAMING_SNAKE_CASE								=			GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'    )
     SCREAMING_SNAKE_CASE								=			BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'    )

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(snake_case__							,    snake_case__							,    snake_case__    )

     processor.save_pretrained(self.tmpdirname    )

    def UpperCamelCase			(			self :      Dict							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     return AutoProcessor.from_pretrained(self.tmpdirname							,    **snake_case__    ).tokenizer

    def UpperCamelCase			(			self :      Dict							,    **snake_case__ :      int    ):

     """simple docstring"""






     return AutoProcessor.from_pretrained(self.tmpdirname							,    **snake_case__    ).image_processor

    def UpperCamelCase			(			self :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     return AutoProcessor.from_pretrained(self.tmpdirname							,    **snake_case__    ).qformer_tokenizer

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     shutil.rmtree(self.tmpdirname    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[np.random.randint(2_5_5							,    size=(3, 3_0, 4_0_0)							,    dtype=np.uinta    )]

     SCREAMING_SNAKE_CASE								=			[Image.fromarray(np.moveaxis(snake_case__							,    0							,    -1    )    ) for x in image_inputs]

     return image_inputs

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(
         tokenizer=self.get_tokenizer()							,    image_processor=self.get_image_processor()							,    qformer_tokenizer=self.get_qformer_tokenizer()							,    )
     processor.save_pretrained(self.tmpdirname    )

     SCREAMING_SNAKE_CASE								=			self.get_tokenizer(bos_token='(BOS)'							,    eos_token='(EOS)'    )
     SCREAMING_SNAKE_CASE								=			self.get_image_processor(do_normalize=snake_case__							,    padding_value=1.0    )

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor.from_pretrained(
         self.tmpdirname							,    bos_token='(BOS)'							,    eos_token='(EOS)'							,    do_normalize=snake_case__							,    padding_value=1.0    )

     self.assertEqual(processor.tokenizer.get_vocab()							,    tokenizer_add_kwargs.get_vocab()    )
     self.assertIsInstance(processor.tokenizer							,    snake_case__    )

     self.assertEqual(processor.image_processor.to_json_string()							,    image_processor_add_kwargs.to_json_string()    )
     self.assertIsInstance(processor.image_processor							,    snake_case__    )
     self.assertIsInstance(processor.qformer_tokenizer							,    snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_image_processor()
     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
     SCREAMING_SNAKE_CASE								=			self.get_qformer_tokenizer()

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(
         tokenizer=snake_case__							,    image_processor=snake_case__							,    qformer_tokenizer=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE								=			image_processor(snake_case__							,    return_tensors='np'    )
     SCREAMING_SNAKE_CASE								=			processor(images=snake_case__							,    return_tensors='np'    )

     for key in input_feat_extract.keys():
      self.assertAlmostEqual(input_feat_extract[key].sum()							,    input_processor[key].sum()							,    delta=1E-2    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_image_processor()
     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
     SCREAMING_SNAKE_CASE								=			self.get_qformer_tokenizer()

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(
         tokenizer=snake_case__							,    image_processor=snake_case__							,    qformer_tokenizer=snake_case__    )

     SCREAMING_SNAKE_CASE								=			'lower newer'

     SCREAMING_SNAKE_CASE								=			processor(text=snake_case__    )

     SCREAMING_SNAKE_CASE								=			tokenizer(snake_case__							,    return_token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			qformer_tokenizer(snake_case__							,    return_token_type_ids=snake_case__    )

     for key in encoded_tokens.keys():
      self.assertListEqual(encoded_tokens[key]							,    encoded_processor[key]    )

     for key in encoded_tokens_qformer.keys():
      self.assertListEqual(encoded_tokens_qformer[key]							,    encoded_processor['qformer_' + key]    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_image_processor()
     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
     SCREAMING_SNAKE_CASE								=			self.get_qformer_tokenizer()

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(
         tokenizer=snake_case__							,    image_processor=snake_case__							,    qformer_tokenizer=snake_case__    )

     SCREAMING_SNAKE_CASE								=			'lower newer'
     SCREAMING_SNAKE_CASE								=			self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE								=			processor(text=snake_case__							,    images=snake_case__    )

     self.assertListEqual(
         list(inputs.keys()    )							,    ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']							,    )

     # test if it raises when no input is passed
     with pytest.raises(snake_case__    ):
      processor()

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_image_processor()
     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
     SCREAMING_SNAKE_CASE								=			self.get_qformer_tokenizer()

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(
         tokenizer=snake_case__							,    image_processor=snake_case__							,    qformer_tokenizer=snake_case__    )

     SCREAMING_SNAKE_CASE								=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE								=			processor.batch_decode(snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.batch_decode(snake_case__    )

     self.assertListEqual(snake_case__							,    snake_case__    )




    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_image_processor()
     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()
     SCREAMING_SNAKE_CASE								=			self.get_qformer_tokenizer()

     SCREAMING_SNAKE_CASE								=			InstructBlipProcessor(
         tokenizer=snake_case__							,    image_processor=snake_case__							,    qformer_tokenizer=snake_case__    )

     SCREAMING_SNAKE_CASE								=			'lower newer'
     SCREAMING_SNAKE_CASE								=			self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE								=			processor(text=snake_case__							,    images=snake_case__    )

     self.assertListEqual(
         list(inputs.keys()    )							,    ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']							,    )




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,
      )

