


import functools
import operator

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


snake_case__				:	int					       =					logging.get_logger(__name__)

snake_case__				:	Union[str, Any]					       =					{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"sew"



       def __init__(						self				:				str		,				__a				:				Optional[int]=32		,				__a				:				int=768		,				__a				:				Optional[int]=12		,				__a				:				List[str]=12		,				__a				:				str=3_072		,				__a				:				Optional[int]=2		,				__a				:				Dict="gelu"		,				__a				:				str=0.1		,				__a				:				List[Any]=0.1		,				__a				:				Dict=0.1		,				__a				:				List[Any]=0.0		,				__a				:				Optional[int]=0.1		,				__a				:				Union[str, Any]=0.1		,				__a				:				int=0.02		,				__a				:				Dict=1e-5		,				__a				:				str="group"		,				__a				:				Tuple="gelu"		,				__a				:				Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				__a				:				str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				__a				:				str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				__a				:				Dict=False		,				__a				:				Dict=128		,				__a				:				List[str]=16		,				__a				:				Optional[int]=True		,				__a				:				Optional[Any]=0.05		,				__a				:				int=10		,				__a				:				Union[str, Any]=2		,				__a				:				Optional[int]=0.0		,				__a				:				List[str]=10		,				__a				:				Any=0		,				__a				:				Tuple="mean"		,				__a				:				Union[str, Any]=False		,				__a				:				Union[str, Any]=False		,				__a				:				List[Any]=256		,				__a				:				str=0		,				__a				:				Union[str, Any]=1		,				__a				:				Optional[Any]=2		,				**__a				:				Any		,				)					->List[Any]:
          super().__init__(**__a		,				pad_token_id=__a		,				bos_token_id=__a		,				eos_token_id=__a							)
          lowerCamelCase_						:   str		= hidden_size
          lowerCamelCase_						:   Dict		= feat_extract_norm
          lowerCamelCase_						:   Tuple		= feat_extract_activation
          lowerCamelCase_						:   Union[str, Any]		= list(__a							)
          lowerCamelCase_						:   str		= list(__a							)
          lowerCamelCase_						:   Dict		= list(__a							)
          lowerCamelCase_						:   Optional[int]		= conv_bias
          lowerCamelCase_						:   Any		= num_conv_pos_embeddings
          lowerCamelCase_						:   int		= num_conv_pos_embedding_groups
          lowerCamelCase_						:   Optional[int]		= len(self.conv_dim							)
          lowerCamelCase_						:   Dict		= num_hidden_layers
          lowerCamelCase_						:   Any		= intermediate_size
          lowerCamelCase_						:   Optional[Any]		= squeeze_factor
          lowerCamelCase_						:   List[str]		= hidden_act
          lowerCamelCase_						:   str		= num_attention_heads
          lowerCamelCase_						:   Optional[int]		= hidden_dropout
          lowerCamelCase_						:   str		= attention_dropout
          lowerCamelCase_						:   int		= activation_dropout
          lowerCamelCase_						:   Optional[Any]		= feat_proj_dropout
          lowerCamelCase_						:   Optional[int]		= final_dropout
          lowerCamelCase_						:   str		= layerdrop
          lowerCamelCase_						:   List[str]		= layer_norm_eps
          lowerCamelCase_						:   Optional[Any]		= initializer_range
          lowerCamelCase_						:   List[str]		= vocab_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)`,"""
                 F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
                 F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          lowerCamelCase_						:   Tuple		= apply_spec_augment
          lowerCamelCase_						:   List[Any]		= mask_time_prob
          lowerCamelCase_						:   str		= mask_time_length
          lowerCamelCase_						:   List[str]		= mask_time_min_masks
          lowerCamelCase_						:   str		= mask_feature_prob
          lowerCamelCase_						:   Optional[int]		= mask_feature_length
          lowerCamelCase_						:   Any		= mask_feature_min_masks

          # ctc loss
          lowerCamelCase_						:   Optional[Any]		= ctc_loss_reduction
          lowerCamelCase_						:   str		= ctc_zero_infinity

          # sequence classification
          lowerCamelCase_						:   Any		= use_weighted_layer_sum
          lowerCamelCase_						:   str		= classifier_proj_size




       @property
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Any:
          return functools.reduce(operator.mul		,				self.conv_stride		,				1							)









import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case__				:	List[Any]					       =					'\nimport os\n'

snake_case__				:	List[str]					       =					'\ndef foo():\n    import os\n    return False\n'

snake_case__				:	List[Any]					       =					'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case__				:	List[str]					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case__				:	Tuple					       =					'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case__				:	str					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case__				:	Any					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Union[str, Any]					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case__				:	Any					       =					[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""						,						A__					)
def    __lowerCamelCase   (      A__						: Dict						,						A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   List[str]		= os.path.join(A__						,						"""test_file.py"""					)
   with open(A__						,						"""w"""					) as _tmp_file:
      _tmp_file.write(A__					)

   lowerCamelCase_						:   str		= get_imports(A__					)
   assert parsed_imports == ["os"]





