
"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Union[str, Any]       =     '''facebook/bart-large-mnli'''
				__UpperCamelCase   :		Dict       =     (
				    '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
				    '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
				    '''It returns the most likely label in the list of provided `labels` for the input text.'''
				)
				__UpperCamelCase   :		Optional[int]       =     '''text_classifier'''
				__UpperCamelCase   :		Union[str, Any]       =     AutoTokenizer
				__UpperCamelCase   :		Optional[int]       =     AutoModelForSequenceClassification

				__UpperCamelCase   :		Optional[int]       =     ['''text''', ['''text''']]
				__UpperCamelCase   :		int       =     ['''text''']



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											super().setup()
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.model.config
											SCREAMING_SNAKE_CASE__		: str     =     -1
											for idx, label in config.idalabel.items():
																		if label.lower().startswith("""entail"""					):
																									SCREAMING_SNAKE_CASE__		: Dict     =     int(SCREAMING_SNAKE_CASE__					)
											if self.entailment_id == -1:
																		raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     labels
											return self.pre_processor(
											    [text] * len(SCREAMING_SNAKE_CASE__					)     , [F'''This example is {label}''' for label in labels]     , return_tensors="""pt"""     , padding="""max_length"""     , )



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     outputs.logits
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.argmax(logits[:, 2]					).item()
											return self._labels[label_id]






"""simple docstring"""




import functools
import operator

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


UpperCAmelCase__   :					str												=				logging.get_logger(__name__)

UpperCAmelCase__   :					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 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[str]       =     '''unispeech'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=30_72     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1E-5     , SCREAMING_SNAKE_CASE__="group"     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     , SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)     , SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1_28     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=0.05     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=3_20     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__="mean"     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=80     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.5     , **SCREAMING_SNAKE_CASE__     , )      ->	List[Any]:



											"""simple docstring"""




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

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

											# ctc loss
											SCREAMING_SNAKE_CASE__		: int     =     ctc_loss_reduction
											SCREAMING_SNAKE_CASE__		: str     =     ctc_zero_infinity

											# pretraining loss
											SCREAMING_SNAKE_CASE__		: List[str]     =     replace_prob



				@property
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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




