


"""simple docstring"""



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)


__snake_case         =					{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}







class _lowerCAmelCase       (   __lowercase			):
			__UpperCAmelCase				: Tuple													=						'''codegen'''
			__UpperCAmelCase				: Any													=						{
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}







			def __init__(			self		,			UpperCamelCase__=5_0400		,			UpperCamelCase__=2048		,			UpperCamelCase__=2048		,			UpperCamelCase__=4096		,			UpperCamelCase__=28		,			UpperCamelCase__=16		,			UpperCamelCase__=64		,			UpperCamelCase__=None		,			UpperCamelCase__="gelu_new"		,			UpperCamelCase__=0.0		,			UpperCamelCase__=0.0		,			UpperCamelCase__=0.0		,			UpperCamelCase__=1e-5		,			UpperCamelCase__=0.02		,			UpperCamelCase__=True		,			UpperCamelCase__=5_0256		,			UpperCamelCase__=5_0256		,			UpperCamelCase__=False		,			**UpperCamelCase__		,			)			->	Optional[Any]:

							'''simple docstring'''




							snake_case					:   Tuple         =					vocab_size
							snake_case					:   str         =					n_ctx
							snake_case					:   List[Any]         =					n_positions
							snake_case					:   str         =					n_embd
							snake_case					:   Optional[Any]         =					n_layer
							snake_case					:   Tuple         =					n_head
							snake_case					:   Dict         =					n_inner
							snake_case					:   Union[str, Any]         =					rotary_dim
							snake_case					:   List[str]         =					activation_function
							snake_case					:   List[str]         =					resid_pdrop
							snake_case					:   List[str]         =					embd_pdrop
							snake_case					:   Any         =					attn_pdrop
							snake_case					:   Optional[int]         =					layer_norm_epsilon
							snake_case					:   int         =					initializer_range
							snake_case					:   Tuple         =					use_cache

							snake_case					:   Union[str, Any]         =					bos_token_id
							snake_case					:   Dict         =					eos_token_id

							super().__init__(
							    bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			tie_word_embeddings=UpperCamelCase__		,			**UpperCamelCase__		)







class _lowerCAmelCase       (   __lowercase			):







			def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__ = "default"		,			UpperCamelCase__ = None		,			UpperCamelCase__ = False		,			)			->	Optional[Any]:

							'''simple docstring'''




							super().__init__(UpperCamelCase__		,			task=UpperCamelCase__		,			patching_specs=UpperCamelCase__		,			use_past=UpperCamelCase__		)
							if not getattr(self._config		,			"pad_token_id"		,			UpperCamelCase__		):
											# TODO: how to do that better?
											snake_case					:   Tuple         =					0







			@property
			def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

							'''simple docstring'''




							snake_case					:   int         =					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
							if self.use_past:
											self.fill_with_past_key_values_(UpperCamelCase__		,			direction="inputs"		)
											snake_case					:   Any         =					{0: "batch", 1: "past_sequence + sequence"}
							else:
											snake_case					:   Tuple         =					{0: "batch", 1: "sequence"}

							return common_inputs







			@property
			def 				lowerCamelCase	(			self		)			->	int:

							'''simple docstring'''




							return self._config.n_layer







			@property
			def 				lowerCamelCase	(			self		)			->	int:

							'''simple docstring'''




							return self._config.n_head







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			)			->	Mapping[str, Any]:

							'''simple docstring'''




							snake_case					:   int         =					super(UpperCamelCase__		,			self		).generate_dummy_inputs(
							    UpperCamelCase__		,			batch_size=UpperCamelCase__		,			seq_length=UpperCamelCase__		,			is_pair=UpperCamelCase__		,			framework=UpperCamelCase__		)

							# We need to order the input in the way they appears in the forward()
							snake_case					:   str         =					OrderedDict({"input_ids": common_inputs["input_ids"]}		)

							# Need to add the past_keys
							if self.use_past:
											if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
											else:
															import torch

															snake_case					:   List[Any]         =					common_inputs["input_ids"].shape
															# Not using the same length for past_key_values
															snake_case					:   Optional[Any]         =					seqlen + 2
															snake_case					:   List[Any]         =					(
															    batch,
															    self.num_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // self.num_attention_heads,
															)
															snake_case					:   Union[str, Any]         =					[
															    (torch.zeros(UpperCamelCase__		), torch.zeros(UpperCamelCase__		)) for _ in range(self.num_layers		)
															]

							snake_case					:   List[Any]         =					common_inputs["attention_mask"]
							if self.use_past:
											snake_case					:   int         =					ordered_inputs["attention_mask"].dtype
											snake_case					:   Any         =					torch.cat(
											    [ordered_inputs["attention_mask"], torch.ones(UpperCamelCase__		,			UpperCamelCase__		,			dtype=UpperCamelCase__		)]		,			dim=1		)

							return ordered_inputs







			@property
			def 				lowerCamelCase	(			self		)			->	int:

							'''simple docstring'''




							return 13









"""simple docstring"""



import numpy as np







class _lowerCAmelCase       :







   def __init__(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					(0, 0)
       snake_case					:   str         =					None
       snake_case					:   int         =					0
       snake_case					:   Optional[Any]         =					0
       snake_case					:   Tuple         =					0







   def __eq__(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return self.position == cell.position







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       print(self.position		)







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__=(5, 5)		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					np.zeros(UpperCamelCase__		)
       snake_case					:   str         =					world_size[0]
       snake_case					:   str         =					world_size[1]







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       print(self.w		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[
           (-1, -1),
           (-1, 0),
           (-1, 1),
           (0, -1),
           (0, 1),
           (1, -1),
           (1, 0),
           (1, 1),
       ]
       snake_case					:   Tuple         =					cell.position[0]
       snake_case					:   str         =					cell.position[1]
       snake_case					:   List[Any]         =					[]
       for n in neughbour_cord:
           snake_case					:   Any         =					current_x + n[0]
           snake_case					:   List[str]         =					current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
               snake_case					:   Tuple         =					Cell()
               snake_case					:   int         =					(x, y)
               snake_case					:   List[Any]         =					cell
               neighbours.append(UpperCamelCase__		)
       return neighbours







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					[]
    snake_case					:   List[Any]         =					[]
    _open.append(lowercase				)

    while _open:
        snake_case					:   Union[str, Any]         =					np.argmin([n.f for n in _open]				)
        snake_case					:   Tuple         =					_open[min_f]
        _closed.append(_open.pop(lowercase				)				)
        if current == goal:
            break
        for n in world.get_neigbours(lowercase				):
            for c in _closed:
                if c == n:
                    continue
            snake_case					:   int         =					current.g + 1
            snake_case      ,snake_case					:   Any         =					n.position
            snake_case      ,snake_case					:   Any         =					goal.position
            snake_case					:   int         =					(ya - ya) ** 2 + (xa - xa) ** 2
            snake_case					:   List[Any]         =					n.h + n.g

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


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





