

from __future__ import annotations

import time

a		=list[tuple[int, int]]

a		=[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a		=[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right






class        A_		:
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Node | None):
								__lowerCamelCase				:				Tuple					       = pos_x
								__lowerCamelCase				:				List[str]					       = pos_y
								__lowerCamelCase				:				str					       = (pos_y, pos_x)
								__lowerCamelCase				:				str					       = goal_x
								__lowerCamelCase				:				int					       = goal_y
								__lowerCamelCase				:				List[Any]					       = parent






class        A_		:
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			tuple[int, int]   ,SCREAMING_SNAKE_CASE__		:			tuple[int, int]):
								__lowerCamelCase				:				Any					       = Node(start[1]   ,start[0]   ,goal[1]   ,goal[0]   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = Node(goal[1]   ,goal[0]   ,goal[1]   ,goal[0]   ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = [self.start]
								__lowerCamelCase				:				List[str]					       = False
							def     lowerCAmelCase     (		self		:			List[Any]):
								while self.node_queue:
									__lowerCamelCase				:				Any					       = self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
										__lowerCamelCase				:				Dict					       = True
										return self.retrace_path(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				Tuple					       = self.get_successors(SCREAMING_SNAKE_CASE__)

									for node in successors:
										self.node_queue.append(SCREAMING_SNAKE_CASE__)

								if not self.reached:
									return [self.start.pos]
								return None
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Node):
								__lowerCamelCase				:				Union[str, Any]					       = []
								for action in delta:
									__lowerCamelCase				:				Optional[Any]					       = parent.pos_x + action[1]
									__lowerCamelCase				:				Optional[int]					       = parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(SCREAMING_SNAKE_CASE__) - 1):
										continue

									if grid[pos_y][pos_x] != 0:
										continue

									successors.append(
									    Node(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,self.target.pos_y   ,self.target.pos_x   ,SCREAMING_SNAKE_CASE__))
								return successors


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Node | None):
								__lowerCamelCase				:				List[Any]					       = node
								__lowerCamelCase				:				int					       = []
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									__lowerCamelCase				:				int					       = current_node.parent
								path.reverse()
								return path






class        A_		:
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				int					       = BreadthFirstSearch(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = BreadthFirstSearch(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = False
							def     lowerCAmelCase     (		self		:			str):
								while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									__lowerCamelCase				:				Any					       = self.fwd_bfs.node_queue.pop(0)
									__lowerCamelCase				:				Any					       = self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
										__lowerCamelCase				:				List[str]					       = True
										return self.retrace_bidirectional_path(
										    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				Optional[Any]					       = current_bwd_node
									__lowerCamelCase				:				int					       = current_fwd_node

									__lowerCamelCase				:				str					       = {
									    self.fwd_bfs: self.fwd_bfs.get_successors(SCREAMING_SNAKE_CASE__),
									    self.bwd_bfs: self.bwd_bfs.get_successors(SCREAMING_SNAKE_CASE__),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
											bfs.node_queue.append(SCREAMING_SNAKE_CASE__)

								if not self.reached:
									return [self.fwd_bfs.start.pos]
								return None


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Node   ,SCREAMING_SNAKE_CASE__		:			Node):
								__lowerCamelCase				:				List[Any]					       = self.fwd_bfs.retrace_path(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = self.bwd_bfs.retrace_path(SCREAMING_SNAKE_CASE__)
								bwd_path.pop()
								bwd_path.reverse()
								__lowerCamelCase				:				List[Any]					       = fwd_path + bwd_path
								return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					import doctest

					doctest.testmod()
					a		=(0, 0)
					a		=(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					a		=time.time()
					a		=BreadthFirstSearch(init, goal)
					a		=bfs.search()
					a		=time.time() - start_bfs_time

					print("""Unidirectional BFS computation time : """, bfs_time)

					a		=time.time()
					a		=BidirectionalBreadthFirstSearch(init, goal)
					a		=bd_bfs.search()
					a		=time.time() - start_bd_bfs_time

					print("""Bidirectional BFS computation time : """, bd_bfs_time)











from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModel)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Any			=			FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	int			=			FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Optional[int]			=			FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	List[Any]			=			FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Tuple			=			FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)


