






"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowerCAmelCase         =							random.Random()






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=1.0   ,   snake_case__=None   ,   snake_case__=None					):
 if rng is None:
  A_		:						str	    =   global_rng

 A_		:						Union[str, Any]	    =   []
 for batch_idx in range(shape[0]					):
  values.append([]					)
  for _ in range(shape[1]					):
   values[-1].append(rng.random() * scale					)

 return values



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





 """simple docstring"""




 def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=400     ,     lowerCAmelCase_=2000     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=16000     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     ):
  A_		:						List[str]	    =   parent
  A_		:						int	    =   batch_size
  A_		:						str	    =   min_seq_length
  A_		:						int	    =   max_seq_length
  A_		:						Dict	    =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  A_		:						str	    =   feature_size
  A_		:						Optional[Any]	    =   padding_value
  A_		:						Dict	    =   sampling_rate
  A_		:						List[str]	    =   return_attention_mask
  A_		:						str	    =   do_normalize





 def    lowerCamelCase(self							):
  return {
      "feature_size": self.feature_size,
      "padding_value": self.padding_value,
      "sampling_rate": self.sampling_rate,
      "return_attention_mask": self.return_attention_mask,
      "do_normalize": self.do_normalize,
  }





 def    lowerCamelCase(self     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False							):
  def _flatten(lowerCAmelCase_							):
   return list(itertools.chain(*lowercase_							)							)

  if equal_length:
   A_		:						str	    =   floats_list((self.batch_size, self.max_seq_length)							)
  else:
   # make sure that inputs increase in size
   A_		:						Optional[Any]	    =   [
       _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:
   A_		:						List[Any]	    =   [np.asarray(lowercase_							) for x in speech_inputs]

  return speech_inputs






class      SCREAMING_SNAKE_CASE  (				_UpperCAmelCase       ,       unittest.TestCase						):





 """simple docstring"""




 _A       :				Union[str, Any]    =				WavaVecaFeatureExtractor





 def    lowerCamelCase(self							):
  A_		:						Tuple	    =   WavaVecaFeatureExtractionTester(self							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  self.assertTrue(np.all(np.mean(lowercase_     ,     axis=0							) < 1e-3							)							)
  self.assertTrue(np.all(np.abs(np.var(lowercase_     ,     axis=0							) - 1							) < 1e-3							)							)





 def    lowerCamelCase(self							):
  # Tests that all call wrap to encode_plus and batch_encode_plus
  A_		:						int	    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  # create three inputs of length 800, 1000, and 1200
  A_		:						List[Any]	    =   [floats_list((1, x)							)[0] for x in range(800     ,     1400     ,     200							)]
  A_		:						Dict	    =   [np.asarray(lowercase_							) for speech_input in speech_inputs]

  # Test not batched input
  A_		:						str	    =   feat_extract(speech_inputs[0]     ,     return_tensors="""np"""							).input_values
  A_		:						List[str]	    =   feat_extract(np_speech_inputs[0]     ,     return_tensors="""np"""							).input_values
  self.assertTrue(np.allclose(lowercase_     ,     lowercase_     ,     atol=1e-3							)							)

  # Test batched
  A_		:						List[str]	    =   feat_extract(lowercase_     ,     return_tensors="""np"""							).input_values
  A_		:						Union[str, Any]	    =   feat_extract(lowercase_     ,     return_tensors="""np"""							).input_values
  for enc_seq_a, enc_seq_a in zip(lowercase_     ,     lowercase_							):
   self.assertTrue(np.allclose(lowercase_     ,     lowercase_     ,     atol=1e-3							)							)

  # Test 2-D numpy arrays are batched.
  A_		:						Union[str, Any]	    =   [floats_list((1, x)							)[0] for x in (800, 800, 800)]
  A_		:						str	    =   np.asarray(lowercase_							)
  A_		:						Any	    =   feat_extract(lowercase_     ,     return_tensors="""np"""							).input_values
  A_		:						Any	    =   feat_extract(lowercase_     ,     return_tensors="""np"""							).input_values
  for enc_seq_a, enc_seq_a in zip(lowercase_     ,     lowercase_							):
   self.assertTrue(np.allclose(lowercase_     ,     lowercase_     ,     atol=1e-3							)							)





 def    lowerCamelCase(self							):
  A_		:						List[Any]	    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  A_		:						Any	    =   [floats_list((1, x)							)[0] for x in range(800     ,     1400     ,     200							)]

  A_		:						List[str]	    =   ["""longest""", """max_length""", """do_not_pad"""]
  A_		:						Optional[int]	    =   [None, 1600, None]
  for max_length, padding in zip(lowercase_     ,     lowercase_							):
   A_		:						Union[str, Any]	    =   feat_extract(lowercase_     ,     padding=lowercase_     ,     max_length=lowercase_     ,     return_tensors="""np"""							)
   A_		:						Optional[Any]	    =   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][:1000]							)
   self.assertTrue(input_values[0][1000:].sum() < 1e-6							)
   self._check_zero_mean_unit_variance(input_values[2][:1200]							)





 def    lowerCamelCase(self							):
  A_		:						Optional[Any]	    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  A_		:						List[str]	    =   range(800     ,     1400     ,     200							)
  A_		:						Any	    =   [floats_list((1, x)							)[0] for x in lengths]

  A_		:						List[str]	    =   ["""longest""", """max_length""", """do_not_pad"""]
  A_		:						Union[str, Any]	    =   [None, 1600, None]

  for max_length, padding in zip(lowercase_     ,     lowercase_							):
   A_		:						Optional[Any]	    =   feat_extract(lowercase_     ,     max_length=lowercase_     ,     padding=lowercase_							)
   A_		:						Union[str, Any]	    =   processed.input_values

   self._check_zero_mean_unit_variance(input_values[0][:800]							)
   self._check_zero_mean_unit_variance(input_values[1][:1000]							)
   self._check_zero_mean_unit_variance(input_values[2][:1200]							)





 def    lowerCamelCase(self							):
  A_		:						Any	    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  A_		:						List[str]	    =   [floats_list((1, x)							)[0] for x in range(800     ,     1400     ,     200							)]
  A_		:						Any	    =   feat_extract(
      lowercase_     ,     truncation=lowercase_     ,     max_length=1000     ,     padding="""max_length"""     ,     return_tensors="""np"""							)
  A_		:						List[Any]	    =   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    lowerCamelCase(self							):
  A_		:						int	    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  A_		:						Any	    =   [floats_list((1, x)							)[0] for x in range(800     ,     1400     ,     200							)]
  A_		:						Tuple	    =   feat_extract(
      lowercase_     ,     truncation=lowercase_     ,     max_length=1000     ,     padding="""longest"""     ,     return_tensors="""np"""							)
  A_		:						Optional[Any]	    =   processed.input_values

  self._check_zero_mean_unit_variance(input_values[0, :800]							)
  self._check_zero_mean_unit_variance(input_values[1, :1000]							)
  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, 1000)							)

  A_		:						Union[str, Any]	    =   [floats_list((1, x)							)[0] for x in range(800     ,     1400     ,     200							)]
  A_		:						Optional[int]	    =   feat_extract(
      lowercase_     ,     truncation=lowercase_     ,     max_length=2000     ,     padding="""longest"""     ,     return_tensors="""np"""							)
  A_		:						Dict	    =   processed.input_values

  self._check_zero_mean_unit_variance(input_values[0, :800]							)
  self._check_zero_mean_unit_variance(input_values[1, :1000]							)
  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, 1200)							)





 @require_torch
 def    lowerCamelCase(self							):
  import torch

  A_		:						Tuple	    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  A_		:						Any	    =   np.random.rand(100							).astype(np.floataa							)
  A_		:						List[str]	    =   np_speech_inputs.tolist()

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





 @slow
 @require_torch
 def    lowerCamelCase(self							):
  # this test makes sure that models that are using
  # group norm don't have their feature extractor return the
  # attention_mask
  for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
   A_		:						Any	    =   WavaVecaConfig.from_pretrained(lowercase_							)
   A_		:						Dict	    =   WavaVecaFeatureExtractor.from_pretrained(lowercase_							)

   # only "layer" feature extraction norm should make use of
   # attention_mask
   self.assertEqual(feat_extract.return_attention_mask     ,     config.feat_extract_norm == """layer"""							)









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   DDIMScheduler(
	    timesteps=config.model.params.timesteps   ,   beta_schedule="""scaled_linear"""   ,   beta_start=config.model.params.linear_start   ,   beta_end=config.model.params.linear_end   ,   clip_sample=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							parser.add_argument("--checkpoint_path", type=str, required=True)
							parser.add_argument("--config_path", type=str, required=True)
							parser.add_argument("--output_path", type=str, required=True)
							_lowerCAmelCase         =							parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

