






"""simple docstring"""

import doctest
from collections import deque

import numpy as np


class 		__lowerCamelCase				:





		'''simple docstring'''






		def __init__(   self							:	int	):
				lowerCAmelCase_   :							Optional[Any]									=			[2, 1, 2, -1]
				lowerCAmelCase_   :							int									=			[1, 2, 3, 4]





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							int									=			len(self.first_signal	)
				lowerCAmelCase_   :							Any									=			len(self.second_signal	)

				lowerCAmelCase_   :							Dict									=			max(_snake_case						,			_snake_case	)

				# create a zero matrix of max_length x max_length
				lowerCAmelCase_   :							List[Any]									=			[[0] * max_length for i in range(_snake_case	)]

				# fills the smaller signal with zeros to make both signals of same length
				if length_first_signal < length_second_signal:
						self.first_signal += [0] * (max_length - length_first_signal)
				elif length_first_signal > length_second_signal:
						self.second_signal += [0] * (max_length - length_second_signal)
				for i in range(_snake_case	):
						lowerCAmelCase_   :							Dict									=			deque(self.second_signal	)
						rotated_signal.rotate(_snake_case	)
						for j, item in enumerate(_snake_case	):
								matrix[i][j] += item

        # multiply the matrix with the first signal
				lowerCAmelCase_   :							Optional[int]									=			np.matmul(np.transpose(_snake_case	)						,			np.transpose(self.first_signal	)	)

				# rounding-off to two decimal places
				return [round(_snake_case						,			2	) for i in final_signal]


if __name__ == "__main__":
					doctest.testmod()






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_ : List[str]    		=       logging.get_logger(__name__)


snake_case_ : Tuple    		=       {
    "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 __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''codegen'''
   UpperCAmelCase__  :    int          =  {
       '''max_position_embeddings''': '''n_positions''',
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						Union[str, Any]=50400					,						_snake_case		:						Optional[int]=2048					,						_snake_case		:						Union[str, Any]=2048					,						_snake_case		:						List[str]=4096					,						_snake_case		:						Any=28					,						_snake_case		:						List[str]=16					,						_snake_case		:						int=64					,						_snake_case		:						Tuple=None					,						_snake_case		:						Dict="gelu_new"					,						_snake_case		:						Union[str, Any]=0.0					,						_snake_case		:						Optional[Any]=0.0					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						List[Any]=1e-5					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Optional[Any]=True					,						_snake_case		:						int=50256					,						_snake_case		:						Tuple=50256					,						_snake_case		:						int=False					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							n_ctx
        UpperCAmelCase_          =							n_positions
        UpperCAmelCase_          =							n_embd
        UpperCAmelCase_          =							n_layer
        UpperCAmelCase_          =							n_head
        UpperCAmelCase_          =							n_inner
        UpperCAmelCase_          =							rotary_dim
        UpperCAmelCase_          =							activation_function
        UpperCAmelCase_          =							resid_pdrop
        UpperCAmelCase_          =							embd_pdrop
        UpperCAmelCase_          =							attn_pdrop
        UpperCAmelCase_          =							layer_norm_epsilon
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_cache

        UpperCAmelCase_          =							bos_token_id
        UpperCAmelCase_          =							eos_token_id

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







class __snake_case			(			a		):







   def __init__(      self		:						Tuple					,						_snake_case		:						PretrainedConfig					,						_snake_case		:						str = "default"					,						_snake_case		:						List[PatchingSpec] = None					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__(_snake_case					,						task=_snake_case					,						patching_specs=_snake_case					,						use_past=_snake_case)
        if not getattr(self._config					,						'''pad_token_id'''					,						_snake_case):
             # TODO: how to do that better?
             UpperCAmelCase_          =							0







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
             self.fill_with_past_key_values_(_snake_case					,						direction='''inputs''')
             UpperCAmelCase_          =							{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
             UpperCAmelCase_          =							{0: '''batch''', 1: '''sequence'''}

        return common_inputs







   @property
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        return self._config.n_layer







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self._config.n_head







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						PreTrainedTokenizer					,						_snake_case		:						int = -1					,						_snake_case		:						int = -1					,						_snake_case		:						bool = False					,						_snake_case		:						Optional[TensorType] = None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							super(_snake_case					,						self).generate_dummy_inputs(
            _snake_case					,						batch_size=_snake_case					,						seq_length=_snake_case					,						is_pair=_snake_case					,						framework=_snake_case)

        # We need to order the input in the way they appears in the forward()
        UpperCAmelCase_          =							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

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

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

        return ordered_inputs







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return 13






