






from collections import OrderedDict

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


snake_case__   :						int													=   logging.get_logger(__name__)


snake_case__   :						List[str]													=   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"""),
    ]
)

snake_case__   :						Optional[Any]													=   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"""),
    ]
)

snake_case__   :						Tuple													=   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"""),
    ]
)

snake_case__   :						Dict													=   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"""),
    ]
)

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

snake_case__   :						Optional[int]													=   OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

snake_case__   :						Union[str, Any]													=   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"""),
    ]
)

snake_case__   :						Optional[int]													=   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"""),
    ]
)

snake_case__   :						str													=   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"""),
    ]
)

snake_case__   :						Dict													=   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"""),
    ]
)

snake_case__   :						Dict													=   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"""),
    ]
)

snake_case__   :						Any													=   OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

snake_case__   :						Optional[int]													=   OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

snake_case__   :						List[str]													=   OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

snake_case__   :						Optional[int]													=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
snake_case__   :						str													=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
snake_case__   :						List[Any]													=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
snake_case__   :						Tuple													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
snake_case__   :						Any													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
snake_case__   :						str													=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
snake_case__   :						Tuple													=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
snake_case__   :						Optional[int]													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
snake_case__   :						List[str]													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
snake_case__   :						Union[str, Any]													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
snake_case__   :						List[Any]													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
snake_case__   :						Optional[int]													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
snake_case__   :						List[Any]													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
snake_case__   :						Tuple													=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_MAPPING




snake_case__   :						Optional[int]													=   auto_class_update(FlaxAutoModel)


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_PRETRAINING_MAPPING




snake_case__   :						Optional[Any]													=   auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




snake_case__   :						Tuple													=   auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_MASKED_LM_MAPPING




snake_case__   :						List[Any]													=   auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




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


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




snake_case__   :						Optional[int]													=   auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




snake_case__   :						int													=   auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




snake_case__   :						List[Any]													=   auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




snake_case__   :						Any													=   auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




snake_case__   :						Union[str, Any]													=   auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




snake_case__   :						Optional[Any]													=   auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




snake_case__   :						int													=   auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")


class     _a							(	_BaseAutoModelClass     ):






  """simple docstring"""





  A_						       = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




snake_case__   :						Optional[int]													=   auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)








from __future__ import annotations

__A			:					str						=			list[tuple[int, int]]

__A			:					Optional[int]						=			[
    [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			:					List[str]						=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float     , __lowerCamelCase					:			Node | None     , ):
       SCREAMING_SNAKE_CASE										=   pos_x
       SCREAMING_SNAKE_CASE										=   pos_y
       SCREAMING_SNAKE_CASE										=   (pos_y, pos_x)
       SCREAMING_SNAKE_CASE										=   goal_x
       SCREAMING_SNAKE_CASE										=   goal_y
       SCREAMING_SNAKE_CASE										=   g_cost
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   self.calculate_heuristic()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   abs(self.pos_x - self.goal_x			)
       SCREAMING_SNAKE_CASE										=   abs(self.pos_y - self.goal_y			)
       return dx + dy





   def __lt__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return self.f_cost < other.f_cost





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			tuple[int, int]     , __lowerCamelCase					:			tuple[int, int]			):
       SCREAMING_SNAKE_CASE										=   Node(start[1]     , start[0]     , goal[1]     , goal[0]     , 0     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   Node(goal[1]     , goal[0]     , goal[1]     , goal[0]     , 99999     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   [self.start]
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   False





   def      _snake_case		(       self					:			Optional[Any]			):
       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(0			)

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

           self.closed_nodes.append(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.get_successors(__lowerCamelCase			)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(__lowerCamelCase			)
               else:
                   # retrieve the best current path
                   SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase			)			)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(__lowerCamelCase			)
                   else:
                       self.open_nodes.append(__lowerCamelCase			)

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





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Node			):
       SCREAMING_SNAKE_CASE										=   []
       for action in delta:
           SCREAMING_SNAKE_CASE										=   parent.pos_x + action[1]
           SCREAMING_SNAKE_CASE										=   parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__lowerCamelCase			) - 1):
               continue

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

           successors.append(
               Node(
                   __lowerCamelCase     , __lowerCamelCase     , self.target.pos_y     , self.target.pos_x     , parent.g_cost + 1     , __lowerCamelCase     , )			)
       return successors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Node | None			):
       SCREAMING_SNAKE_CASE										=   node
       SCREAMING_SNAKE_CASE										=   []
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)			)
           SCREAMING_SNAKE_CASE										=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
    __A			:					Optional[Any]						=			(0, 0)
    __A			:					Optional[int]						=			(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __A			:					List[str]						=			GreedyBestFirst(init, goal)
    __A			:					Tuple						=			greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __A			:					Optional[Any]						=			2

        for elem in grid:
            print(elem)