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


_UpperCAmelCase :			Optional[Any]              =	logging.get_logger(__name__)

_UpperCAmelCase :			Dict              =	{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Union[str, Any]     					=    "speech_to_text"
		__lowercase	:	Union[str, Any]     					=    ["past_key_values"]
		__lowercase	:	str     					=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__(			self    ,				A_=10_000    ,				A_=12    ,				A_=2_048    ,				A_=4    ,				A_=6    ,				A_=2_048    ,				A_=4    ,				A_=0.0    ,				A_=0.0    ,				A_=True    ,				A_=True    ,				A_="relu"    ,				A_=256    ,				A_=0.1    ,				A_=0.0    ,				A_=0.0    ,				A_=0.02    ,				A_=2    ,				A_=True    ,				A_=1    ,				A_=0    ,				A_=2    ,				A_=6_000    ,				A_=1_024    ,				A_=2    ,				A_=(5, 5)    ,				A_=1_024    ,				A_=80    ,				A_=1    ,				**A_    ,				)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					d_model
				UpperCamelCase          =					encoder_ffn_dim
				UpperCamelCase          =					encoder_layers
				UpperCamelCase          =					encoder_attention_heads
				UpperCamelCase          =					decoder_ffn_dim
				UpperCamelCase          =					decoder_layers
				UpperCamelCase          =					decoder_attention_heads
				UpperCamelCase          =					dropout
				UpperCamelCase          =					attention_dropout
				UpperCamelCase          =					activation_dropout
				UpperCamelCase          =					activation_function
				UpperCamelCase          =					init_std
				UpperCamelCase          =					encoder_layerdrop
				UpperCamelCase          =					decoder_layerdrop
				UpperCamelCase          =					use_cache
				UpperCamelCase          =					encoder_layers
				UpperCamelCase          =					scale_embedding  # scale factor will be sqrt(d_model) if True
				UpperCamelCase          =					max_source_positions
				UpperCamelCase          =					max_target_positions
				UpperCamelCase          =					num_conv_layers
				UpperCamelCase          =					list(A_	)
				UpperCamelCase          =					conv_channels
				UpperCamelCase          =					input_feat_per_channel
				UpperCamelCase          =					input_channels

				if len(self.conv_kernel_sizes	) != self.num_conv_layers:
						raise ValueError(
						    'Configuration for convolutional module is incorrect. '
						    'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
						    F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes	)}`, '''
						    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''	)

				super().__init__(
				    pad_token_id=A_    ,				bos_token_id=A_    ,				eos_token_id=A_    ,				is_encoder_decoder=A_    ,				decoder_start_token_id=A_    ,				**A_    ,				)


from __future__ import annotations


class     lowercase :



		def __init__(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					text, pattern
				UpperCamelCase     ,  UpperCamelCase          =					len(A_	), len(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if char == self.pattern[i]:
								return i
				return -1



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if self.pattern[i] != self.text[current_pos + i]:
								return current_pos + i
				return -1



		def __UpperCamelCase			(			self	)  -> list[int]:
				"""simple docstring"""
				# searches pattern in text and returns index positions
				UpperCamelCase          =					[]
				for i in range(self.textLen - self.patLen + 1	):
						UpperCamelCase          =					self.mismatch_in_text(A_	)
						if mismatch_index == -1:
								positions.append(A_	)
						else:
								UpperCamelCase          =					self.match_in_pattern(self.text[mismatch_index]	)
								UpperCamelCase          =					(
								    mismatch_index - match_index
								)  # shifting index lgtm [py/multiple-definition]
				return positions






_UpperCAmelCase :			Union[str, Any]              =	"ABAABA"
_UpperCAmelCase :			Any              =	"AB"
_UpperCAmelCase :			Dict              =	BoyerMooreSearch(text, pattern)
_UpperCAmelCase :			Optional[int]              =	bms.bad_character_heuristic()

if len(positions) == 0:
					print("No match found")
else:
					print("Pattern found in following positions: ")
					print(positions)
