




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


UpperCAmelCase_		:					Optional[int]          =  logging.get_logger(__name__)


UpperCAmelCase_		:					Any          =  {
    '''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 		__A   (      UpperCAmelCase_      ):
						UpperCamelCase      =       """codegen"""
						UpperCamelCase      =       {
						    """max_position_embeddings""": """n_positions""",
						    """hidden_size""": """n_embd""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}

						def __init__(		self :Optional[int]     ,			__snake_case :Optional[int]=5_04_00     ,			__snake_case :Optional[Any]=20_48     ,			__snake_case :List[str]=20_48     ,			__snake_case :Any=40_96     ,			__snake_case :int=28     ,			__snake_case :Any=16     ,			__snake_case :List[str]=64     ,			__snake_case :Any=None     ,			__snake_case :Tuple="gelu_new"     ,			__snake_case :int=0.0     ,			__snake_case :str=0.0     ,			__snake_case :List[str]=0.0     ,			__snake_case :str=1E-5     ,			__snake_case :str=0.02     ,			__snake_case :Dict=True     ,			__snake_case :List[Any]=5_02_56     ,			__snake_case :Optional[int]=5_02_56     ,			__snake_case :str=False     ,			**__snake_case :Optional[int]     ,			):
										'''simple docstring'''

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

										__magic_name__						:						Optional[int]										=bos_token_id
										__magic_name__						:						List[Any]										=eos_token_id

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




class 		__A   (      UpperCAmelCase_      ):

						def __init__(		self :Optional[int]     ,			__snake_case :PretrainedConfig     ,			__snake_case :str = "default"     ,			__snake_case :List[PatchingSpec] = None     ,			__snake_case :bool = False     ,			):
										'''simple docstring'''

										super().__init__(__lowerCamelCase     ,			task=__lowerCamelCase     ,			patching_specs=__lowerCamelCase     ,			use_past=__lowerCamelCase			)
										if not getattr(self._config     ,			"""pad_token_id"""     ,			__lowerCamelCase			):
														# TODO: how to do that better?
														__magic_name__						:						Optional[Any]										=0

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}			)
										if self.use_past:
														self.fill_with_past_key_values_(__lowerCamelCase     ,			direction="""inputs"""			)
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """past_sequence + sequence"""}
										else:
														__magic_name__						:						str										={0: """batch""", 1: """sequence"""}

										return common_inputs

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self._config.n_layer

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return self._config.n_head

						def      A__    (		self :Tuple     ,			__snake_case :PreTrainedTokenizer     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=super(__lowerCamelCase     ,			self			).generate_dummy_inputs(
										    __lowerCamelCase     ,			batch_size=__lowerCamelCase     ,			seq_length=__lowerCamelCase     ,			is_pair=__lowerCamelCase     ,			framework=__lowerCamelCase			)

										# We need to order the input in the way they appears in the forward()
										__magic_name__						:						Any										=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

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

										__magic_name__						:						Dict										=common_inputs["""attention_mask"""]
										if self.use_past:
														__magic_name__						:						Union[str, Any]										=ordered_inputs["""attention_mask"""].dtype
														__magic_name__						:						Tuple										=torch.cat(
														    [ordered_inputs["""attention_mask"""], torch.ones(__lowerCamelCase     ,			__lowerCamelCase     ,			dtype=__lowerCamelCase			)]     ,			dim=1			)

										return ordered_inputs


						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return 13











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    bool:
		__lowercase											=     len(snake_case							) + 1
		__lowercase											=     len(snake_case							) + 1

		# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
		# length i of input_string matches with prefix string of length j of
		# given pattern.
		# "dp" stands for dynamic programming.
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		# since string of zero length match pattern of zero length
		__lowercase											=     1

		# since pattern of zero length will never match with string of non-zero length
		for i in range(1						,   snake_case							):
				__lowercase											=     0

		# since string of zero length will match with pattern where there
		# is at least one * alternatively
		for j in range(1						,   snake_case							):
				__lowercase											=     dp[0][j - 2] if pattern[j - 1] == '*' else 0

		# now using bottom-up approach to find for all remaining lengths
		for i in range(1						,   snake_case							):
				for j in range(1						,   snake_case							):
						if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
								__lowercase											=     dp[i - 1][j - 1]

						elif pattern[j - 1] == "*":
								if dp[i][j - 2] == 1:
										__lowercase											=     1
								elif pattern[j - 2] in (input_string[i - 1], "."):
										__lowercase											=     dp[i - 1][j]
								else:
										__lowercase											=     0
						else:
								__lowercase											=     0

		return bool(dp[-1][-1]							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# inputing the strings
		# input_string = input("input a string :")
		# pattern = input("input a pattern :")

		SCREAMING_SNAKE_CASE_   :      Any			=   '''aab'''
		SCREAMING_SNAKE_CASE_   :      str			=   '''c*a*b'''

		# using function to check whether given string matches the given pattern
		if match_pattern(input_string, pattern):
				print(F"""{input_string} matches the given pattern {pattern}""")
		else:
				print(F"""{input_string} does not match with the given pattern {pattern}""")




