
'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase  :      Dict							      =		{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		['''CLIPTokenizerFast''']

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		['''CLIPFeatureExtractor''']
							__UpperCamelCase  :      Optional[Any]							      =		['''CLIPImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CLIPModel''',
							    '''CLIPPreTrainedModel''',
							    '''CLIPTextModel''',
							    '''CLIPTextModelWithProjection''',
							    '''CLIPVisionModel''',
							    '''CLIPVisionModelWithProjection''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		[
							    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFCLIPModel''',
							    '''TFCLIPPreTrainedModel''',
							    '''TFCLIPTextModel''',
							    '''TFCLIPVisionModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[Any]							      =		[
							    '''FlaxCLIPModel''',
							    '''FlaxCLIPPreTrainedModel''',
							    '''FlaxCLIPTextModel''',
							    '''FlaxCLIPTextPreTrainedModel''',
							    '''FlaxCLIPVisionModel''',
							    '''FlaxCLIPVisionPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      Tuple							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




