





import numpy as np

class 		UpperCamelCase__ :



     '''simple docstring'''

     def __init__(							self						:    int       )     ->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						(0, 0)
           SCREAMING_SNAKE_CASE		=						None
           SCREAMING_SNAKE_CASE		=						0
           SCREAMING_SNAKE_CASE		=						0
           SCREAMING_SNAKE_CASE		=						0






     def __eq__(							self						:    Optional[int]						,lowerCamelCase__						:    Dict       )     ->		Optional[Any]:




           '''simple docstring'''


           return self.position == cell.position






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Union[str, Any]:




           '''simple docstring'''


           print(self.position       )

class 		UpperCamelCase__ :



     '''simple docstring'''

     def __init__(							self						:    Tuple						,lowerCamelCase__						:    Union[str, Any]=(5, 5)       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						np.zeros(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						world_size[0]
           SCREAMING_SNAKE_CASE		=						world_size[1]






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Any:




           '''simple docstring'''


           print(self.w       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    Union[str, Any]       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[
               (-1, -1),
               (-1, 0),
               (-1, 1),
               (0, -1),
               (0, 1),
               (1, -1),
               (1, 0),
               (1, 1),
           ]
           SCREAMING_SNAKE_CASE		=						cell.position[0]
           SCREAMING_SNAKE_CASE		=						cell.position[1]
           SCREAMING_SNAKE_CASE		=						[]
           for n in neughbour_cord:
                 SCREAMING_SNAKE_CASE		=						current_x + n[0]
                 SCREAMING_SNAKE_CASE		=						current_y + n[1]
                 if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                       SCREAMING_SNAKE_CASE		=						Cell()
                       SCREAMING_SNAKE_CASE		=						(x, y)
                       SCREAMING_SNAKE_CASE		=						cell
                       neighbours.append(lowerCamelCase__       )
           return neighbours



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[]
      SCREAMING_SNAKE_CASE		=						[]
      _open.append(_SCREAMING_SNAKE_CASE	)

      while _open:
            SCREAMING_SNAKE_CASE		=						np.argmin([n.f for n in _open]	)
            SCREAMING_SNAKE_CASE		=						_open[min_f]
            _closed.append(_open.pop(_SCREAMING_SNAKE_CASE	)	)
            if current == goal:
                  break
            for n in world.get_neigbours(_SCREAMING_SNAKE_CASE	):
                  for c in _closed:
                        if c == n:
                              continue
                  SCREAMING_SNAKE_CASE		=						current.g + 1
                  SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						n.position
                  SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						goal.position
                  SCREAMING_SNAKE_CASE		=						(ya - ya) ** 2 + (xa - xa) ** 2
                  SCREAMING_SNAKE_CASE		=						n.h + n.g

                  for c in _open:
                        if c == n and c.f < n.f:
                              continue
                  _open.append(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						[]
      while current.parent is not None:
            path.append(current.position	)
            SCREAMING_SNAKE_CASE		=						current.parent
      path.append(current.position	)
      return path[::-1]


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       Gridworld()
      # Start position and goal
      SCREAMING_SNAKE_CASE_						   =       Cell()
      SCREAMING_SNAKE_CASE_						   =       (0, 0)
      SCREAMING_SNAKE_CASE_						   =       Cell()
      SCREAMING_SNAKE_CASE_						   =       (4, 4)
      print(F'''path from {start.position} to {goal.position}''')
      SCREAMING_SNAKE_CASE_						   =       astar(world, start, goal)
      #   Just for visual reasons.
      for i in s:
            SCREAMING_SNAKE_CASE_						   =       1
      print(world.w)









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"""
)


