
'''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


snake_case									=    random.Random()


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_=1.0      ,			lowerCamelCase_=None      ,			lowerCamelCase_=None			):







 """simple docstring"""


 if rng is None:
  lowerCAmelCase__     :   str     		=	global_rng

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

 return values




class    lowerCAmelCase       (     unittest.TestCase  ):


 def __init__(    self  : List[Any]			,							a__  : Dict			,							a__  : Any=7			,							a__  : Dict=400			,							a__  : int=2000			,							a__  : int=1			,							a__  : str=0.0			,							a__  : Optional[Any]=1_6000			,							a__  : Dict=True			,							a__  : Tuple=True			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	parent
  lowerCAmelCase__     :   Tuple     		=	batch_size
  lowerCAmelCase__     :   Tuple     		=	min_seq_length
  lowerCAmelCase__     :   Any     		=	max_seq_length
  lowerCAmelCase__     :   Optional[Any]     		=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  lowerCAmelCase__     :   List[Any]     		=	feature_size
  lowerCAmelCase__     :   List[Any]     		=	padding_value
  lowerCAmelCase__     :   Union[str, Any]     		=	sampling_rate
  lowerCAmelCase__     :   Union[str, Any]     		=	return_attention_mask
  lowerCAmelCase__     :   List[Any]     		=	do_normalize


 def    _A	(    self  : str    ):
  '''simple docstring'''

  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    _A	(    self  : Optional[int]			,							a__  : List[Any]=False			,							a__  : Optional[int]=False    ):
  '''simple docstring'''

  def _flatten(a__  : List[str]    ):
   return list(itertools.chain(*lowerCAmelCase_    )    )

  if equal_length:
   lowerCAmelCase__     :   Tuple     		=	floats_list((self.batch_size, self.max_seq_length)    )
  else:
   # make sure that inputs increase in size
   lowerCAmelCase__     :   List[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:
   lowerCAmelCase__     :   str     		=	[np.asarray(lowerCAmelCase_    ) for x in speech_inputs]

  return speech_inputs




class    lowerCAmelCase       (     __A    ,  unittest.TestCase  ):
 A_				:				List[str]	=			WavaVecaFeatureExtractor


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	WavaVecaFeatureExtractionTester(self    )


 def    _A	(    self  : Optional[int]			,							a__  : 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  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  # create three inputs of length 800, 1000, and 1200
  lowerCAmelCase__     :   List[Any]     		=	[floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
  lowerCAmelCase__     :   Dict     		=	[np.asarray(lowerCAmelCase_    ) for speech_input in speech_inputs]

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

  # Test batched
  lowerCAmelCase__     :   Optional[int]     		=	feat_extract(lowerCAmelCase_			,							return_tensors="np"    ).input_values
  lowerCAmelCase__     :   List[str]     		=	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    )    )

  # Test 2-D numpy arrays are batched.
  lowerCAmelCase__     :   Tuple     		=	[floats_list((1, x)    )[0] for x in (800, 800, 800)]
  lowerCAmelCase__     :   str     		=	np.asarray(lowerCAmelCase_    )
  lowerCAmelCase__     :   Optional[Any]     		=	feat_extract(lowerCAmelCase_			,							return_tensors="np"    ).input_values
  lowerCAmelCase__     :   Optional[int]     		=	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  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  lowerCAmelCase__     :   Dict     		=	[floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]

  lowerCAmelCase__     :   Optional[int]     		=	["longest", "max_length", "do_not_pad"]
  lowerCAmelCase__     :   Union[str, Any]     		=	[None, 1600, None]
  for max_length, padding in zip(lowerCAmelCase_			,							lowerCAmelCase_    ):
   lowerCAmelCase__     :   str     		=	feat_extract(lowerCAmelCase_			,							padding=lowerCAmelCase_			,							max_length=lowerCAmelCase_			,							return_tensors="np"    )
   lowerCAmelCase__     :   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    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  lowerCAmelCase__     :   Dict     		=	range(800			,							1400			,							200    )
  lowerCAmelCase__     :   Any     		=	[floats_list((1, x)    )[0] for x in lengths]

  lowerCAmelCase__     :   Tuple     		=	["longest", "max_length", "do_not_pad"]
  lowerCAmelCase__     :   Optional[Any]     		=	[None, 1600, None]

  for max_length, padding in zip(lowerCAmelCase_			,							lowerCAmelCase_    ):
   lowerCAmelCase__     :   List[Any]     		=	feat_extract(lowerCAmelCase_			,							max_length=lowerCAmelCase_			,							padding=lowerCAmelCase_    )
   lowerCAmelCase__     :   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][:1200]    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  lowerCAmelCase__     :   Tuple     		=	[floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
  lowerCAmelCase__     :   Optional[Any]     		=	feat_extract(
      lowerCAmelCase_			,							truncation=lowerCAmelCase_			,							max_length=1000			,							padding="max_length"			,							return_tensors="np"    )
  lowerCAmelCase__     :   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    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  lowerCAmelCase__     :   int     		=	[floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
  lowerCAmelCase__     :   Optional[int]     		=	feat_extract(
      lowerCAmelCase_			,							truncation=lowerCAmelCase_			,							max_length=1000			,							padding="longest"			,							return_tensors="np"    )
  lowerCAmelCase__     :   List[str]     		=	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)    )

  lowerCAmelCase__     :   str     		=	[floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
  lowerCAmelCase__     :   List[str]     		=	feat_extract(
      lowerCAmelCase_			,							truncation=lowerCAmelCase_			,							max_length=2000			,							padding="longest"			,							return_tensors="np"    )
  lowerCAmelCase__     :   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 longest
  self.assertTrue(input_values.shape == (3, 1200)    )


 @require_torch
 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  import torch

  lowerCAmelCase__     :   List[Any]     		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  lowerCAmelCase__     :   Optional[int]     		=	np.random.rand(100    ).astype(np.floataa    )
  lowerCAmelCase__     :   Union[str, Any]     		=	np_speech_inputs.tolist()

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


 @slow
 @require_torch
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
   lowerCAmelCase__     :   Optional[int]     		=	WavaVecaConfig.from_pretrained(lowerCAmelCase_    )
   lowerCAmelCase__     :   Optional[Any]     		=	WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_    )

   # 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"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 							snake_case (  A__		):
			return ConvertCommand(
			    args.model_type      ,args.tf_checkpoint      ,args.pytorch_dump_output      ,args.config      ,args.finetuning_task_name		)


lowerCamelCase_							      = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class      UpperCamelCase_			(__A	):





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					ArgumentParser		)		->     Optional[Any]:
				UpperCAmelCase_	:					Any     	=   parser.add_parser(
				    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
				train_parser.add_argument("--model_type"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Model's type."		)
				train_parser.add_argument(
				    "--tf_checkpoint"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="TensorFlow checkpoint path or folder."		)
				train_parser.add_argument(
				    "--pytorch_dump_output"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Path to the PyTorch saved model output."		)
				train_parser.add_argument("--config"      ,      type=lowerCAmelCase_      ,      default=""      ,      help="Configuration file path or folder."		)
				train_parser.add_argument(
				    "--finetuning_task_name"      ,      type=lowerCAmelCase_      ,      default=lowerCAmelCase_      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
				train_parser.set_defaults(func=lowerCAmelCase_		)





	def __init__( self   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      *lowerCAmelCase_   :					Tuple      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   logging.get_logger("transformers-cli/converting"		)

				self._logger.info(f"""Loading model {model_type}"""		)
				UpperCAmelCase_	:					Optional[int]     	=   model_type
				UpperCAmelCase_	:					Union[str, Any]     	=   tf_checkpoint
				UpperCAmelCase_	:					Tuple     	=   pytorch_dump_output
				UpperCAmelCase_	:					Tuple     	=   config
				UpperCAmelCase_	:					List[str]     	=   finetuning_task_name





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[Any]:
				if self._model_type == "albert":
							try:
										from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "bert":
							try:
										from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "funnel":
							try:
										from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "t5":
							try:
										from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "transfo_xl":
							try:
										from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
										    convert_transfo_xl_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							if "ckpt" in self._tf_checkpoint.lower():
										UpperCAmelCase_	:					Union[str, Any]     	=   self._tf_checkpoint
										UpperCAmelCase_	:					List[str]     	=   ""
							else:
										UpperCAmelCase_	:					Any     	=   self._tf_checkpoint
										UpperCAmelCase_	:					Any     	=   ""
							convert_transfo_xl_checkpoint_to_pytorch(
							    lowerCAmelCase_      ,      self._config      ,      self._pytorch_dump_output      ,      lowerCAmelCase_		)
				elif self._model_type == "gpt2":
							try:
										from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
										    convert_gpta_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "xlnet":
							try:
										from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
										    convert_xlnet_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name		)
				elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				else:
							raise ValueError(
							    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"		)



