


'''simple docstring'''

from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
					from .features import FeatureType





@dataclass
class   snake_case					:





		"""simple docstring"""




		_lowerCamelCase						=						42
		_lowerCamelCase						=						None
		# Automatically constructed
		_lowerCamelCase						=						"dict"
		_lowerCamelCase						=						None
		_lowerCamelCase						=						field(default="Translation"    ,		init=lowercase    ,		repr=lowercase    )






		def __call__(					self  ):







			"""simple docstring"""
			return pa.struct({lang: pa.string() for lang in sorted(self.languages  )}  )







		def 						snake_case	(					self  ):







			"""simple docstring"""
			from .features import Value

			return {k: Value("string"  ) for k in sorted(self.languages  )}





@dataclass
class   snake_case					:





		"""simple docstring"""




		_lowerCamelCase						=						None
		_lowerCamelCase						=						None
		_lowerCamelCase						=						None
		# Automatically constructed
		_lowerCamelCase						=						"dict"
		_lowerCamelCase						=						None
		_lowerCamelCase						=						field(default="TranslationVariableLanguages"    ,		init=lowercase    ,		repr=lowercase    )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						sorted(set(self.languages  )  ) if self.languages else None
			lowerCamelCase_  				=						len(self.languages  ) if self.languages else None






		def __call__(					self  ):







			"""simple docstring"""
			return pa.struct({"language": pa.list_(pa.string()  ), "translation": pa.list_(pa.string()  )}  )






		def 						snake_case	(					self    ,						UpperCamelCase  ):







			"""simple docstring"""
			lowerCamelCase_  				=						set(self.languages  )
			if self.languages and set(__lowerCAmelCase  ) - lang_set:
				raise ValueError(
				    f'''Some languages in example ({", ".join(sorted(set(__lowerCAmelCase  ) - lang_set  )  )}) are not in valid set ({", ".join(__lowerCAmelCase  )}).'''  )

			# Convert dictionary into tuples, splitting out cases where there are
			# multiple translations for a single language.
			lowerCamelCase_  				=						[]
			for lang, text in translation_dict.items():
				if isinstance(__lowerCAmelCase    ,						__lowerCAmelCase  ):
					translation_tuples.append((lang, text)  )
				else:
					translation_tuples.extend([(lang, el) for el in text]  )

        # Ensure translations are in ascending order by language code.
			lowerCamelCase_				,lowerCamelCase_  				=						zip(*sorted(__lowerCAmelCase  )  )

			return {"language": languages, "translation": translations}







		def 						snake_case	(					self  ):







			"""simple docstring"""
			from .features import Sequence, Value

			return {
			    "language": Sequence(Value("string"  )  ),
			    "translation": Sequence(Value("string"  )  ),
			}







'''simple docstring'''

import functools
import operator

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


a_						:       List[str]      			=				logging.get_logger(__name__)

a_						:       Dict      			=				{
    """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					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"unispeech"






  def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=0.5    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase    ,						pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase  )
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						feat_extract_norm
   lowerCamelCase_  				=						feat_extract_activation
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						conv_bias
   lowerCamelCase_  				=						num_conv_pos_embeddings
   lowerCamelCase_  				=						num_conv_pos_embedding_groups
   lowerCamelCase_  				=						len(self.conv_dim  )
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						hidden_dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						feat_proj_dropout
   lowerCamelCase_  				=						final_dropout
   lowerCamelCase_  				=						layerdrop
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_ctc_classes
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						do_stable_layer_norm
   lowerCamelCase_  				=						use_weighted_layer_sum
   lowerCamelCase_  				=						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
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

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

   # ctc loss
   lowerCamelCase_  				=						ctc_loss_reduction
   lowerCamelCase_  				=						ctc_zero_infinity

   # pretraining loss
   lowerCamelCase_  				=						replace_prob







  @property
  def 						snake_case	(					self  ):







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



