



"""simple docstring"""
class 							lowerCamelCase_:  # Public class to implement a graph





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				row
									_lowerCamelCase        =				col
									_lowerCamelCase        =				graph

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									return (
									    0 <= i < self.ROW
									    and 0 <= j < self.COL
									    and not visited[i][j]
									    and self.graph[i][j]
									)

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									# Checking all 8 elements surrounding nth element
									_lowerCamelCase        =				[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
									_lowerCamelCase        =				[-1, 0, 1, -1, 1, -1, 0, 1]
									_lowerCamelCase        =				True  # Make those cells visited
									for k in range(8   ):
													if self.is_safe(i + row_nbr[k]				,       j + col_nbr[k]				,       snake_case_   ):
																	self.diffs(i + row_nbr[k]				,       j + col_nbr[k]				,       snake_case_   )





					def 			snake_case__      (    self   ):  # And finally, count all islands.
									_lowerCamelCase        =				[[False for j in range(self.COL   )] for i in range(self.ROW   )]
									_lowerCamelCase        =				0
									for i in range(self.ROW   ):
													for j in range(self.COL   ):
																	if visited[i][j] is False and self.graph[i][j] == 1:
																					self.diffs(snake_case_				,       snake_case_				,       snake_case_   )
																					count += 1
									return count





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      ['''SpeechT5Tokenizer''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SpeechT5ForSpeechToText''',
			    '''SpeechT5ForSpeechToSpeech''',
			    '''SpeechT5ForTextToSpeech''',
			    '''SpeechT5Model''',
			    '''SpeechT5PreTrainedModel''',
			    '''SpeechT5HifiGan''',
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						int    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
