

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
  import torch


lowercase_   							=  random.Random()
def 	a (     A__      :  Tuple ,  A__      :  int=1.0 ,  A__      :  Tuple=None ,  A__      :  Tuple=None	)       -> str:





      """simple docstring"""



      if rng is None:
            _lowercase      			=global_rng

      _lowercase      			=[]
      for batch_idx in range(shape[0]	):
            values.append([]	)
            for _ in range(shape[1]	):
                  values[-1].append(rng.random() * scale	)

      return values





@require_torch
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase=7				,  lowerCAmelCase=400				,  lowerCAmelCase=2_000				,  lowerCAmelCase=1				,  lowerCAmelCase=0.0				,  lowerCAmelCase=16_000				,  lowerCAmelCase=True				,  lowerCAmelCase=80				,  lowerCAmelCase=16				,  lowerCAmelCase=64				,  lowerCAmelCase="hann_window"				,  lowerCAmelCase=80				,  lowerCAmelCase=7_600				,  lowerCAmelCase=1e-10				,  lowerCAmelCase=True				,  )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=parent
        _lowercase      			=batch_size
        _lowercase      			=min_seq_length
        _lowercase      			=max_seq_length
        _lowercase      			=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _lowercase      			=feature_size
        _lowercase      			=padding_value
        _lowercase      			=sampling_rate
        _lowercase      			=do_normalize
        _lowercase      			=num_mel_bins
        _lowercase      			=hop_length
        _lowercase      			=win_length
        _lowercase      			=win_function
        _lowercase      			=fmin
        _lowercase      			=fmax
        _lowercase      			=mel_floor
        _lowercase      			=return_attention_mask




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "do_normalize": self.do_normalize,
            "num_mel_bins": self.num_mel_bins,
            "hop_length": self.hop_length,
            "win_length": self.win_length,
            "win_function": self.win_function,
            "fmin": self.fmin,
            "fmax": self.fmax,
            "mel_floor": self.mel_floor,
            "return_attention_mask": self.return_attention_mask,
        }




  def     A__	(							self				,  lowerCAmelCase=False				,  lowerCAmelCase=False   )    ->					List[Any]:
        '''simple docstring'''






        def _flatten(lowerCAmelCase   ):
              return list(itertools.chain(*lowerCAmelCase   )   )

        if equal_length:
              _lowercase      			=floats_list((self.batch_size, self.max_seq_length)   )
        else:
              # make sure that inputs increase in size
              _lowercase      			=[
                  _flatten(floats_list((x, self.feature_size)   )   )
                  for x in range(self.min_seq_length				,  self.max_seq_length				,  self.seq_length_diff   )
              ]

        if numpify:
              _lowercase      			=[np.asarray(lowerCAmelCase   ) for x in speech_inputs]

        return speech_inputs




  def     A__	(							self				,  lowerCAmelCase=False				,  lowerCAmelCase=False   )    ->					Any:
        '''simple docstring'''






        if equal_length:
              _lowercase      			=[floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
        else:
              # make sure that inputs increase in size
              _lowercase      			=[
                  floats_list((x, self.num_mel_bins)   )
                  for x in range(self.min_seq_length				,  self.max_seq_length				,  self.seq_length_diff   )
              ]

        if numpify:
              _lowercase      			=[np.asarray(lowerCAmelCase   ) for x in speech_inputs]

        return speech_inputs





@require_torch
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _a		     =    SpeechTaFeatureExtractor




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=SpeechTaFeatureExtractionTester(self   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        self.assertTrue(np.all(np.mean(lowerCAmelCase				,  axis=0   ) < 1e-3   )   )
        self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase				,  axis=0   ) - 1   ) < 1e-3   )   )




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






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        # create three inputs of length 800, 1000, and 1200
        _lowercase      			=[floats_list((1, x)   )[0] for x in range(800				,  1_400				,  200   )]
        _lowercase      			=[np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

        # Test not batched input
        _lowercase      			=feat_extract(speech_inputs[0]				,  return_tensors='np'   ).input_values
        _lowercase      			=feat_extract(np_speech_inputs[0]				,  return_tensors='np'   ).input_values
        self.assertTrue(np.allclose(lowerCAmelCase				,  lowerCAmelCase				,  atol=1e-3   )   )

        # Test batched
        _lowercase      			=feat_extract(lowerCAmelCase				,  return_tensors='np'   ).input_values
        _lowercase      			=feat_extract(lowerCAmelCase				,  return_tensors='np'   ).input_values
        for enc_seq_a, enc_seq_a in zip(lowerCAmelCase				,  lowerCAmelCase   ):
              self.assertTrue(np.allclose(lowerCAmelCase				,  lowerCAmelCase				,  atol=1e-3   )   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        _lowercase      			=[floats_list((1, x)   )[0] for x in range(800				,  1_400				,  200   )]

        _lowercase      			=['longest', 'max_length', 'do_not_pad']
        _lowercase      			=[None, 1_600, None]
        for max_length, padding in zip(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=feat_extract(lowerCAmelCase				,  padding=lowerCAmelCase				,  max_length=lowerCAmelCase				,  return_tensors='np'   )
              _lowercase      			=processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]   )
              self.assertTrue(input_values[0][800:].sum() < 1e-6   )
              self._check_zero_mean_unit_variance(input_values[1][:1_000]   )
              self.assertTrue(input_values[0][1_000:].sum() < 1e-6   )
              self._check_zero_mean_unit_variance(input_values[2][:1_200]   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        _lowercase      			=range(800				,  1_400				,  200   )
        _lowercase      			=[floats_list((1, x)   )[0] for x in lengths]

        _lowercase      			=['longest', 'max_length', 'do_not_pad']
        _lowercase      			=[None, 1_600, None]

        for max_length, padding in zip(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=feat_extract(lowerCAmelCase				,  max_length=lowerCAmelCase				,  padding=lowerCAmelCase   )
              _lowercase      			=processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]   )
              self._check_zero_mean_unit_variance(input_values[1][:1_000]   )
              self._check_zero_mean_unit_variance(input_values[2][:1_200]   )




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        _lowercase      			=[floats_list((1, x)   )[0] for x in range(800				,  1_400				,  200   )]
        _lowercase      			=feat_extract(
            lowerCAmelCase				,  truncation=lowerCAmelCase				,  max_length=1_000				,  padding='max_length'				,  return_tensors='np'   )
        _lowercase      			=processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]   )
        self._check_zero_mean_unit_variance(input_values[1]   )
        self._check_zero_mean_unit_variance(input_values[2]   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        _lowercase      			=[floats_list((1, x)   )[0] for x in range(800				,  1_400				,  200   )]
        _lowercase      			=feat_extract(
            lowerCAmelCase				,  truncation=lowerCAmelCase				,  max_length=1_000				,  padding='longest'				,  return_tensors='np'   )
        _lowercase      			=processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]   )
        self._check_zero_mean_unit_variance(input_values[1, :1_000]   )
        self._check_zero_mean_unit_variance(input_values[2]   )

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 1_000)   )

        _lowercase      			=[floats_list((1, x)   )[0] for x in range(800				,  1_400				,  200   )]
        _lowercase      			=feat_extract(
            lowerCAmelCase				,  truncation=lowerCAmelCase				,  max_length=2_000				,  padding='longest'				,  return_tensors='np'   )
        _lowercase      			=processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]   )
        self._check_zero_mean_unit_variance(input_values[1, :1_000]   )
        self._check_zero_mean_unit_variance(input_values[2]   )

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 1_200)   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        _lowercase      			=np.random.rand(100   ).astype(np.floataa   )
        _lowercase      			=np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
              _lowercase      			=feature_extractor.pad([{'input_values': inputs}]				,  return_tensors='np'   )
              self.assertTrue(np_processed.input_values.dtype == np.floataa   )
              _lowercase      			=feature_extractor.pad([{'input_values': inputs}]				,  return_tensors='pt'   )
              self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        # create three inputs of length 800, 1000, and 1200
        _lowercase      			=[floats_list((1, x)   )[0] for x in range(800				,  1_400				,  200   )]
        _lowercase      			=[np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

        # Test feature size
        _lowercase      			=feature_extractor(audio_target=lowerCAmelCase				,  padding=lowerCAmelCase				,  return_tensors='np'   ).input_values
        self.assertTrue(input_values.ndim == 3   )
        self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

        # Test not batched input
        _lowercase      			=feature_extractor(speech_inputs[0]				,  return_tensors='np'   ).input_values
        _lowercase      			=feature_extractor(np_speech_inputs[0]				,  return_tensors='np'   ).input_values
        self.assertTrue(np.allclose(lowerCAmelCase				,  lowerCAmelCase				,  atol=1e-3   )   )

        # Test batched
        _lowercase      			=feature_extractor(lowerCAmelCase				,  return_tensors='np'   ).input_values
        _lowercase      			=feature_extractor(lowerCAmelCase				,  return_tensors='np'   ).input_values
        for enc_seq_a, enc_seq_a in zip(lowerCAmelCase				,  lowerCAmelCase   ):
              self.assertTrue(np.allclose(lowerCAmelCase				,  lowerCAmelCase				,  atol=1e-3   )   )

        # Test 2-D numpy arrays are batched.
        _lowercase      			=[floats_list((1, x)   )[0] for x in (800, 800, 800)]
        _lowercase      			=np.asarray(lowerCAmelCase   )
        _lowercase      			=feature_extractor(lowerCAmelCase				,  return_tensors='np'   ).input_values
        _lowercase      			=feature_extractor(lowerCAmelCase				,  return_tensors='np'   ).input_values
        for enc_seq_a, enc_seq_a in zip(lowerCAmelCase				,  lowerCAmelCase   ):
              self.assertTrue(np.allclose(lowerCAmelCase				,  lowerCAmelCase				,  atol=1e-3   )   )




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






        _lowercase      			=self.feat_extract_tester.prepare_inputs_for_target()
        _lowercase      			=self.feature_extraction_class(**self.feat_extract_dict   )
        _lowercase      			=feat_extract.model_input_names[0]

        _lowercase      			=BatchFeature({input_name: speech_inputs}   )

        self.assertTrue(all(len(lowerCAmelCase   ) == len(lowerCAmelCase   ) for x, y in zip(lowerCAmelCase				,  processed_features[input_name]   )   )   )

        _lowercase      			=self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase   )
        _lowercase      			=BatchFeature({input_name: speech_inputs}				,  tensor_type='np'   )

        _lowercase      			=processed_features[input_name]

        if len(batch_features_input.shape   ) < 3:
              _lowercase      			=batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )




  @require_torch
  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase   )
        _lowercase      			=self.feature_extraction_class(**self.feat_extract_dict   )
        _lowercase      			=feat_extract.model_input_names[0]

        _lowercase      			=BatchFeature({input_name: speech_inputs}				,  tensor_type='pt'   )

        _lowercase      			=processed_features[input_name]

        if len(batch_features_input.shape   ) < 3:
              _lowercase      			=batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )




  @require_torch
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=self.feature_extraction_class(**self.feat_extract_dict   )
        _lowercase      			=self.feat_extract_tester.prepare_inputs_for_target()
        _lowercase      			=feat_extract.model_input_names[0]

        _lowercase      			=BatchFeature({input_name: speech_inputs}   )

        _lowercase      			=feat_extract.num_mel_bins  # hack!

        _lowercase      			=feat_extract.pad(lowerCAmelCase				,  padding='longest'				,  return_tensors='np'   )[input_name]
        _lowercase      			=feat_extract.pad(lowerCAmelCase				,  padding='longest'				,  return_tensors='pt'   )[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1e-2   )




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






        _lowercase      			=self.feat_extract_dict
        _lowercase      			=True
        _lowercase      			=self.feature_extraction_class(**lowerCAmelCase   )
        _lowercase      			=self.feat_extract_tester.prepare_inputs_for_target()
        _lowercase      			=[len(lowerCAmelCase   ) for x in speech_inputs]
        _lowercase      			=feat_extract.model_input_names[0]

        _lowercase      			=BatchFeature({input_name: speech_inputs}   )

        _lowercase      			=feat_extract.num_mel_bins  # hack!

        _lowercase      			=feat_extract.pad(lowerCAmelCase				,  padding='longest'				,  return_tensors='np'   )
        self.assertIn('attention_mask'				,  lowerCAmelCase   )
        self.assertListEqual(list(processed.attention_mask.shape   )				,  list(processed[input_name].shape[:2]   )   )
        self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()				,  lowerCAmelCase   )




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






        _lowercase      			=self.feat_extract_dict
        _lowercase      			=True
        _lowercase      			=self.feature_extraction_class(**lowerCAmelCase   )
        _lowercase      			=self.feat_extract_tester.prepare_inputs_for_target()
        _lowercase      			=[len(lowerCAmelCase   ) for x in speech_inputs]
        _lowercase      			=feat_extract.model_input_names[0]

        _lowercase      			=BatchFeature({input_name: speech_inputs}   )
        _lowercase      			=min(lowerCAmelCase   )

        _lowercase      			=feat_extract.num_mel_bins  # hack!

        _lowercase      			=feat_extract.pad(
            lowerCAmelCase				,  padding='max_length'				,  max_length=lowerCAmelCase				,  truncation=lowerCAmelCase				,  return_tensors='np'   )
        self.assertIn('attention_mask'				,  lowerCAmelCase   )
        self.assertListEqual(
            list(processed_pad.attention_mask.shape   )				,  [processed_pad[input_name].shape[0], max_length]   )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()				,  [max_length for x in speech_inputs]   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






        from datasets import load_dataset

        _lowercase      			=load_dataset('hf-internal-testing/librispeech_asr_dummy'				,  'clean'				,  split='validation'   )
        # automatic decoding with librispeech
        _lowercase      			=ds.sort('id'   ).select(range(lowerCAmelCase   )   )[:num_samples]['audio']

        return [x["array"] for x in speech_samples]




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






        _lowercase      			=torch.tensor(
            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]   )
        # fmt: on

        _lowercase      			=self._load_datasamples(1   )
        _lowercase      			=SpeechTaFeatureExtractor()
        _lowercase      			=feature_extractor(lowerCAmelCase				,  return_tensors='pt'   ).input_values
        self.assertEquals(input_values.shape				,  (1, 93_680)   )
        self.assertTrue(torch.allclose(input_values[0, :30]				,  lowerCAmelCase				,  atol=1e-6   )   )




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]   )
        # fmt: on

        _lowercase      			=self._load_datasamples(1   )
        _lowercase      			=SpeechTaFeatureExtractor()
        _lowercase      			=feature_extractor(audio_target=lowerCAmelCase				,  return_tensors='pt'   ).input_values
        self.assertEquals(input_values.shape				,  (1, 366, 80)   )
        self.assertTrue(torch.allclose(input_values[0, 0, :30]				,  lowerCAmelCase				,  atol=1e-4   )   )





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()

  parser.add_argument(
      '--repo_path',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      'image_size': 'sample_size',
      'num_res_blocks': 'layers_per_block',
      'block_channels': 'block_out_channels',
      'down_blocks': 'down_block_types',
      'up_blocks': 'up_block_types',
      'downscale_freq_shift': 'freq_shift',
      'resnet_num_groups': 'norm_num_groups',
      'resnet_act_fn': 'act_fn',
      'resnet_eps': 'norm_eps',
      'num_head_channels': 'attention_head_dim',
  }

  lowercase_   							=  {
      'time_steps': 'time_proj',
      'mid': 'mid_block',
      'downsample_blocks': 'down_blocks',
      'upsample_blocks': 'up_blocks',
  }

  lowercase_   							=  '' if has_file(args.repo_path, 'config.json') else 'unet'

  with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
    lowercase_   							=  reader.read()
    lowercase_   							=  json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, 'config.json'):
    lowercase_   							=  UNetaDModel(**config)
  else:
    lowercase_   							=  UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
    lowercase_   							=  class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowercase_   							=  dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowercase_   							=  config[key]
        del config[key]

    lowercase_   							=  [k.replace('UNetRes', '') for k in config['down_block_types']]
    lowercase_   							=  [k.replace('UNetRes', '') for k in config['up_block_types']]

  if do_only_weights:
    lowercase_   							=  torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

    lowercase_   							=  {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
        continue
      lowercase_   							=  False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split('.')[0] == key:
          lowercase_   							=  param_value
          lowercase_   							=  True
      if not has_changed:
        lowercase_   							=  param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))


