





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase						(		unittest.TestCase  ):







  @slow
  def      snake_case_							(     self						:   Tuple       )    ->		str:
        _a							:  Any				= AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''  ,					return_dict=__lowerCamelCase       ).to(__lowerCamelCase       )
        _a							:  Optional[Any]				= AutoTokenizer.from_pretrained('''google/mt5-small'''       )

        _a							:  Optional[int]				= tokenizer('''Hello there'''  ,					return_tensors='''pt'''       ).input_ids
        _a							:  str				= tokenizer('''Hi I am'''  ,					return_tensors='''pt'''       ).input_ids

        _a							:  Any				= model(input_ids.to(__lowerCamelCase       )  ,					labels=labels.to(__lowerCamelCase       )       ).loss
        _a							:  Tuple				= -(labels.shape[-1] * loss.item())

        _a							:  int				= -84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )












"""simple docstring"""



import functools
import operator

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


a_					=     logging.get_logger(__name__)

a_					=     {
    """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 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""unispeech"""

 def __init__(       self   ,		__lowerCamelCase=32   ,		__lowerCamelCase=768   ,		__lowerCamelCase=12   ,		__lowerCamelCase=12   ,		__lowerCamelCase=3072   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.0_2   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase="group"   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,		__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,		__lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,		__lowerCamelCase=False   ,		__lowerCamelCase=128   ,		__lowerCamelCase=16   ,		__lowerCamelCase=False   ,		__lowerCamelCase=True   ,		__lowerCamelCase=0.0_5   ,		__lowerCamelCase=10   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=10   ,		__lowerCamelCase=0   ,		__lowerCamelCase=320   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=100   ,		__lowerCamelCase=256   ,		__lowerCamelCase=256   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase="mean"   ,		__lowerCamelCase=False   ,		__lowerCamelCase=False   ,		__lowerCamelCase=256   ,		__lowerCamelCase=80   ,		__lowerCamelCase=0   ,		__lowerCamelCase=1   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.5   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase   ,		pad_token_id=__lowerCamelCase   ,		bos_token_id=__lowerCamelCase   ,		eos_token_id=__lowerCamelCase    )
     __A       :		Dict							=	hidden_size
     __A       :		Dict							=	feat_extract_norm
     __A       :		int							=	feat_extract_activation
     __A       :		Dict							=	list(__lowerCamelCase    )
     __A       :		str							=	list(__lowerCamelCase    )
     __A       :		Dict							=	list(__lowerCamelCase    )
     __A       :		str							=	conv_bias
     __A       :		Optional[int]							=	num_conv_pos_embeddings
     __A       :		int							=	num_conv_pos_embedding_groups
     __A       :		Union[str, Any]							=	len(self.conv_dim    )
     __A       :		List[str]							=	num_hidden_layers
     __A       :		Dict							=	intermediate_size
     __A       :		List[str]							=	hidden_act
     __A       :		int							=	num_attention_heads
     __A       :		str							=	hidden_dropout
     __A       :		str							=	attention_dropout
     __A       :		Optional[Any]							=	activation_dropout
     __A       :		Optional[int]							=	feat_proj_dropout
     __A       :		Optional[Any]							=	final_dropout
     __A       :		List[Any]							=	layerdrop
     __A       :		Any							=	layer_norm_eps
     __A       :		str							=	initializer_range
     __A       :		List[str]							=	num_ctc_classes
     __A       :		Dict							=	vocab_size
     __A       :		Dict							=	do_stable_layer_norm
     __A       :		Union[str, Any]							=	use_weighted_layer_sum
     __A       :		Tuple							=	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
     __A       :		List[Any]							=	apply_spec_augment
     __A       :		str							=	mask_time_prob
     __A       :		Dict							=	mask_time_length
     __A       :		List[str]							=	mask_time_min_masks
     __A       :		Union[str, Any]							=	mask_feature_prob
     __A       :		Tuple							=	mask_feature_length
     __A       :		Any							=	mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     __A       :		int							=	num_codevectors_per_group
     __A       :		Any							=	num_codevector_groups
     __A       :		Optional[Any]							=	contrastive_logits_temperature
     __A       :		Dict							=	feat_quantizer_dropout
     __A       :		Tuple							=	num_negatives
     __A       :		List[str]							=	codevector_dim
     __A       :		str							=	proj_codevector_dim
     __A       :		Union[str, Any]							=	diversity_loss_weight

     # ctc loss
     __A       :		Tuple							=	ctc_loss_reduction
     __A       :		Optional[int]							=	ctc_zero_infinity

     # pretraining loss
     __A       :		str							=	replace_prob







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return functools.reduce(operator.mul   ,		self.conv_stride   ,		1    )




