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


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)


lowerCamelCase_    :		Dict							=	{
    """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__							(       __lowercase		):
			A__    :       str        =      '''codegen'''
			A__    :       Optional[int]        =      {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self				,						UpperCAmelCase=5_0_4_0_0				,						UpperCAmelCase=2_0_4_8				,						UpperCAmelCase=2_0_4_8				,						UpperCAmelCase=4_0_9_6				,						UpperCAmelCase=2_8				,						UpperCAmelCase=1_6				,						UpperCAmelCase=6_4				,						UpperCAmelCase=None				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.0				,						UpperCAmelCase=0.0				,						UpperCAmelCase=0.0				,						UpperCAmelCase=1e-5				,						UpperCAmelCase=0.02				,						UpperCAmelCase=True				,						UpperCAmelCase=5_0_2_5_6				,						UpperCAmelCase=5_0_2_5_6				,						UpperCAmelCase=False				,						**UpperCAmelCase				,						)			->   int:
								__a		 =       vocab_size
								__a		 =       n_ctx
								__a		 =       n_positions
								__a		 =       n_embd
								__a		 =       n_layer
								__a		 =       n_head
								__a		 =       n_inner
								__a		 =       rotary_dim
								__a		 =       activation_function
								__a		 =       resid_pdrop
								__a		 =       embd_pdrop
								__a		 =       attn_pdrop
								__a		 =       layer_norm_epsilon
								__a		 =       initializer_range
								__a		 =       use_cache

								__a		 =       bos_token_id
								__a		 =       eos_token_id

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






class 						a__							(       __lowercase		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase = "default"				,						UpperCAmelCase = None				,						UpperCAmelCase = False				,						)			->   Tuple:
								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?
													__a		 =       0



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Mapping[str, Mapping[int, str]]:
								__a		 =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
								if self.use_past:
													self.fill_with_past_key_values_(UpperCAmelCase				,						direction='inputs'   )
													__a		 =       {0: """batch""", 1: """past_sequence + sequence"""}
								else:
													__a		 =       {0: """batch""", 1: """sequence"""}

								return common_inputs



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return self._config.n_layer



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return self._config.n_head



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						)			->   Mapping[str, Any]:
								__a		 =       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()
								__a		 =       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

																		__a		 =       common_inputs["""input_ids"""].shape
																		# Not using the same length for past_key_values
																		__a		 =       seqlen + 2
																		__a		 =       (
																		    batch,
																		    self.num_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // self.num_attention_heads,
																		)
																		__a		 =       [
																		    (torch.zeros(UpperCAmelCase   ), torch.zeros(UpperCAmelCase   )) for _ in range(self.num_layers   )
																		]

								__a		 =       common_inputs["""attention_mask"""]
								if self.use_past:
													__a		 =       ordered_inputs["""attention_mask"""].dtype
													__a		 =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(UpperCAmelCase				,						UpperCAmelCase				,						dtype=UpperCAmelCase   )]				,						dim=1   )

								return ordered_inputs







			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return 1_3






from collections import defaultdict
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       first_str.lower().strip()
					__a		 =       second_str.lower().strip()

					# Remove whitespace
					__a		 =       first_str.replace(' '				,    ''						)
					__a		 =       second_str.replace(' '				,    ''						)

					# Strings of different lengths are not anagrams
					if len(__lowerCamelCase						) != len(__lowerCamelCase						):
										return False

					# Default values for count should be 0
					__a		 =       defaultdict(__lowerCamelCase						)

					# For each character in input strings,
					# increment count in the corresponding
					for i in range(len(__lowerCamelCase						)						):
										count[first_str[i]] += 1
										count[second_str[i]] -= 1

					return all(_count == 0 for _count in count.values()						)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	lowerCamelCase_    :		List[str]							=	input("""Enter the first string """).strip()
	lowerCamelCase_    :		Optional[Any]							=	input("""Enter the second string """).strip()

	lowerCamelCase_    :		str							=	check_anagrams(input_a, input_b)
	print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')




