





from __future__ import annotations

from decimal import Decimal

from numpy import array


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      Decimal

		# Check if the provided matrix has 2 rows and 2 columns
		# since this implementation only works for 2x2 matrices
		if len(lowerCAmelCase__						) == 2 and len(matrix[0]						) == 2 and len(matrix[1]						) == 2:
				# Calculate the determinant of the matrix
				lowerCamelCase_	:     Dict 						=      float(
				    d(matrix[0][0]						) * d(matrix[1][1]						) - d(matrix[1][0]						) * d(matrix[0][1]						)						)
				if determinant == 0:
						raise ValueError('This matrix has no inverse.'						)

				# Creates a copy of the matrix with swapped positions of the elements
				lowerCamelCase_	:     Any 						=      [[0.0, 0.0], [0.0, 0.0]]
				lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      matrix[1][1], matrix[0][0]
				lowerCamelCase_						,      lowerCamelCase_	:     List[Any] 						=      -matrix[1][0], -matrix[0][1]

				# Calculate the inverse of the matrix
				return [
				    [(float(d(lowerCAmelCase__						)						) / determinant) or 0.0 for n in row] for row in swapped_matrix
				]
		elif (
		    len(lowerCAmelCase__						) == 3
		    and len(matrix[0]						) == 3
		    and len(matrix[1]						) == 3
		    and len(matrix[2]						) == 3
		):
				# Calculate the determinant of the matrix using Sarrus rule
				lowerCamelCase_	:     Union[str, Any] 						=      float(
				    (
				        (d(matrix[0][0]						) * d(matrix[1][1]						) * d(matrix[2][2]						))
				        + (d(matrix[0][1]						) * d(matrix[1][2]						) * d(matrix[2][0]						))
				        + (d(matrix[0][2]						) * d(matrix[1][0]						) * d(matrix[2][1]						))
				    )
				    - (
				        (d(matrix[0][2]						) * d(matrix[1][1]						) * d(matrix[2][0]						))
				        + (d(matrix[0][1]						) * d(matrix[1][0]						) * d(matrix[2][2]						))
				        + (d(matrix[0][0]						) * d(matrix[1][2]						) * d(matrix[2][1]						))
				    )						)
				if determinant == 0:
						raise ValueError('This matrix has no inverse.'						)

				# Creating cofactor matrix
				lowerCamelCase_	:     List[Any] 						=      [
				    [d(0.0						), d(0.0						), d(0.0						)],
				    [d(0.0						), d(0.0						), d(0.0						)],
				    [d(0.0						), d(0.0						), d(0.0						)],
				]
				lowerCamelCase_	:     int 						=      (d(matrix[1][1]						) * d(matrix[2][2]						)) - (
				    d(matrix[1][2]						) * d(matrix[2][1]						)
				)
				lowerCamelCase_	:     int 						=      -(
				    (d(matrix[1][0]						) * d(matrix[2][2]						)) - (d(matrix[1][2]						) * d(matrix[2][0]						))
				)
				lowerCamelCase_	:     str 						=      (d(matrix[1][0]						) * d(matrix[2][1]						)) - (
				    d(matrix[1][1]						) * d(matrix[2][0]						)
				)
				lowerCamelCase_	:     Optional[Any] 						=      -(
				    (d(matrix[0][1]						) * d(matrix[2][2]						)) - (d(matrix[0][2]						) * d(matrix[2][1]						))
				)
				lowerCamelCase_	:     Dict 						=      (d(matrix[0][0]						) * d(matrix[2][2]						)) - (
				    d(matrix[0][2]						) * d(matrix[2][0]						)
				)
				lowerCamelCase_	:     Optional[Any] 						=      -(
				    (d(matrix[0][0]						) * d(matrix[2][1]						)) - (d(matrix[0][1]						) * d(matrix[2][0]						))
				)
				lowerCamelCase_	:     List[Any] 						=      (d(matrix[0][1]						) * d(matrix[1][2]						)) - (
				    d(matrix[0][2]						) * d(matrix[1][1]						)
				)
				lowerCamelCase_	:     str 						=      -(
				    (d(matrix[0][0]						) * d(matrix[1][2]						)) - (d(matrix[0][2]						) * d(matrix[1][0]						))
				)
				lowerCamelCase_	:     Tuple 						=      (d(matrix[0][0]						) * d(matrix[1][1]						)) - (
				    d(matrix[0][1]						) * d(matrix[1][0]						)
				)

				# Transpose the cofactor matrix (Adjoint matrix)
				lowerCamelCase_	:     Dict 						=      array(lowerCAmelCase__						)
				for i in range(3						):
						for j in range(3						):
								lowerCamelCase_	:     Optional[int] 						=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
				lowerCamelCase_	:     Union[str, Any] 						=      array(lowerCAmelCase__						)
				for i in range(3						):
						for j in range(3						):
								inverse_matrix[i][j] /= d(lowerCAmelCase__						)

        # Calculate the inverse of the matrix
				return [[float(d(lowerCAmelCase__						)						) or 0.0 for n in row] for row in inverse_matrix]
		raise ValueError('Please provide a matrix of size 2x2 or 3x3.'						)


"""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


_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''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     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[Any]          =   """codegen"""
							_lowerCamelCase					:			List[str]          =   {
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}
							def __init__(     self  ,    _lowerCamelCase=50_400  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=4_096  ,    _lowerCamelCase=28  ,    _lowerCamelCase=16  ,    _lowerCamelCase=64  ,    _lowerCamelCase=None  ,    _lowerCamelCase="gelu_new"  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=1e-5  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=True  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       vocab_size
										__lowercase       =       n_ctx
										__lowercase       =       n_positions
										__lowercase       =       n_embd
										__lowercase       =       n_layer
										__lowercase       =       n_head
										__lowercase       =       n_inner
										__lowercase       =       rotary_dim
										__lowercase       =       activation_function
										__lowercase       =       resid_pdrop
										__lowercase       =       embd_pdrop
										__lowercase       =       attn_pdrop
										__lowercase       =       layer_norm_epsilon
										__lowercase       =       initializer_range
										__lowercase       =       use_cache

										__lowercase       =       bos_token_id
										__lowercase       =       eos_token_id

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





class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = "default"  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False  ,    )			->							str:




										'''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?
													__lowercase       =       0
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										__lowercase       =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
										if self.use_past:
													self.fill_with_past_key_values_(_lowerCamelCase  ,    direction="inputs"       )
													__lowercase       =       {0: "batch", 1: "past_sequence + sequence"}
										else:
													__lowercase       =       {0: "batch", 1: "sequence"}

										return common_inputs
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_layer
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_head
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    )			->							Mapping[str, Any]:




										'''simple docstring'''
										__lowercase       =       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()
										__lowercase       =       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

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

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

										return ordered_inputs





							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return 13





