

'''simple docstring'''






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class     lowercase_			(__lowerCamelCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    '''unispeech'''






  def __init__(   self     :  Any      ,lowercase__     :  List[Any]=3_2      ,lowercase__     :  str=7_6_8      ,lowercase__     :  List[str]=1_2      ,lowercase__     :  Any=1_2      ,lowercase__     :  Any=3_0_7_2      ,lowercase__     :  int="gelu"      ,lowercase__     :  str=0.1      ,lowercase__     :  Union[str, Any]=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  List[Any]=0.0      ,lowercase__     :  Dict=0.0      ,lowercase__     :  str=0.1      ,lowercase__     :  Optional[Any]=0.1      ,lowercase__     :  str=0.0_2      ,lowercase__     :  Optional[int]=1e-5      ,lowercase__     :  List[str]="group"      ,lowercase__     :  Union[str, Any]="gelu"      ,lowercase__     :  Union[str, Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,lowercase__     :  Any=(5, 2, 2, 2, 2, 2, 2)      ,lowercase__     :  List[str]=(1_0, 3, 3, 3, 3, 2, 2)      ,lowercase__     :  int=False      ,lowercase__     :  List[str]=1_2_8      ,lowercase__     :  Union[str, Any]=1_6      ,lowercase__     :  Optional[Any]=False      ,lowercase__     :  Tuple=True      ,lowercase__     :  Union[str, Any]=0.0_5      ,lowercase__     :  Any=1_0      ,lowercase__     :  str=2      ,lowercase__     :  Tuple=0.0      ,lowercase__     :  Optional[int]=1_0      ,lowercase__     :  int=0      ,lowercase__     :  int=3_2_0      ,lowercase__     :  Union[str, Any]=2      ,lowercase__     :  int=0.1      ,lowercase__     :  Optional[Any]=1_0_0      ,lowercase__     :  Union[str, Any]=2_5_6      ,lowercase__     :  Union[str, Any]=2_5_6      ,lowercase__     :  Optional[Any]=0.1      ,lowercase__     :  str="mean"      ,lowercase__     :  Optional[int]=False      ,lowercase__     :  Any=False      ,lowercase__     :  int=2_5_6      ,lowercase__     :  Union[str, Any]=8_0      ,lowercase__     :  Dict=0      ,lowercase__     :  Optional[int]=1      ,lowercase__     :  Any=2      ,lowercase__     :  Optional[int]=0.5      ,**lowercase__     :  Union[str, Any]      ,):
   super().__init__(**a_      ,pad_token_id=a_      ,bos_token_id=a_      ,eos_token_id=a_    )
   __lowercase													=				hidden_size
   __lowercase													=				feat_extract_norm
   __lowercase													=				feat_extract_activation
   __lowercase													=				list(a_    )
   __lowercase													=				list(a_    )
   __lowercase													=				list(a_    )
   __lowercase													=				conv_bias
   __lowercase													=				num_conv_pos_embeddings
   __lowercase													=				num_conv_pos_embedding_groups
   __lowercase													=				len(self.conv_dim    )
   __lowercase													=				num_hidden_layers
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_act
   __lowercase													=				num_attention_heads
   __lowercase													=				hidden_dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				feat_proj_dropout
   __lowercase													=				final_dropout
   __lowercase													=				layerdrop
   __lowercase													=				layer_norm_eps
   __lowercase													=				initializer_range
   __lowercase													=				num_ctc_classes
   __lowercase													=				vocab_size
   __lowercase													=				do_stable_layer_norm
   __lowercase													=				use_weighted_layer_sum
   __lowercase													=				classifier_proj_size

   if (
       (len(self.conv_stride    ) != self.num_feat_extract_layers)
       or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
       or (len(self.conv_dim    ) != self.num_feat_extract_layers)
   ):
    raise ValueError(
        '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
        ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
        F" {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,"
        F" `len(config.conv_kernel) = {len(self.conv_kernel    )}`."    )

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   __lowercase													=				apply_spec_augment
   __lowercase													=				mask_time_prob
   __lowercase													=				mask_time_length
   __lowercase													=				mask_time_min_masks
   __lowercase													=				mask_feature_prob
   __lowercase													=				mask_feature_length
   __lowercase													=				mask_feature_min_masks

   # parameters for pretraining with codevector quantized representations
   __lowercase													=				num_codevectors_per_group
   __lowercase													=				num_codevector_groups
   __lowercase													=				contrastive_logits_temperature
   __lowercase													=				feat_quantizer_dropout
   __lowercase													=				num_negatives
   __lowercase													=				codevector_dim
   __lowercase													=				proj_codevector_dim
   __lowercase													=				diversity_loss_weight

   # ctc loss
   __lowercase													=				ctc_loss_reduction
   __lowercase													=				ctc_zero_infinity

   # pretraining loss
   __lowercase													=				replace_prob






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   return functools.reduce(operator.mul      ,self.conv_stride      ,1    )




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

