






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  							= {
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}




class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			'''gpt2'''
	lowerCAmelCase													=			['''past_key_values''']
	lowerCAmelCase													=			{
	    '''hidden_size''': '''n_embd''',
	    '''max_position_embeddings''': '''n_positions''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(	self	,      a=5_02_57	,      a=10_24	,      a=7_68	,      a=12	,      a=12	,      a=None	,      a="gelu_new"	,      a=0.1	,      a=0.1	,      a=0.1	,      a=1E-5	,      a=0.02	,      a="cls_index"	,      a=True	,      a=None	,      a=True	,      a=0.1	,      a=True	,      a=True	,      a=5_02_56	,      a=5_02_56	,      a=False	,      a=False	,      **a	,      )	->   List[str]:
								snake_case_           =							vocab_size
								snake_case_           =							n_positions
								snake_case_           =							n_embd
								snake_case_           =							n_layer
								snake_case_           =							n_head
								snake_case_           =							n_inner
								snake_case_           =							activation_function
								snake_case_           =							resid_pdrop
								snake_case_           =							embd_pdrop
								snake_case_           =							attn_pdrop
								snake_case_           =							layer_norm_epsilon
								snake_case_           =							initializer_range
								snake_case_           =							summary_type
								snake_case_           =							summary_use_proj
								snake_case_           =							summary_activation
								snake_case_           =							summary_first_dropout
								snake_case_           =							summary_proj_to_labels
								snake_case_           =							scale_attn_weights
								snake_case_           =							use_cache
								snake_case_           =							scale_attn_by_inverse_layer_idx
								snake_case_           =							reorder_and_upcast_attn

								snake_case_           =							bos_token_id
								snake_case_           =							eos_token_id

								super().__init__(bos_token_id=a	,      eos_token_id=a	,      **a )






class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	def __init__(	self	,      a	,      a = "default"	,      a = None	,      a = False	,      )	->   List[str]:
								super().__init__(a	,      task=a	,      patching_specs=a	,      use_past=a )
								if not getattr(self._config	,      'pad_token_id'	,      a ):
															# TODO: how to do that better?
															snake_case_           =							0




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

								return common_inputs




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




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




	def 							_UpperCamelCase      (	self	,      a	,      a = -1	,      a = -1	,      a = False	,      a = None	,      )	->   Mapping[str, Any]:
								snake_case_           =							super(a	,      self ).generate_dummy_inputs(
								    a	,      batch_size=a	,      seq_length=a	,      is_pair=a	,      framework=a )

								# We need to order the input in the way they appears in the forward()
								snake_case_           =							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_      ,		snake_case_           =							common_inputs['input_ids'].shape
																						# Not using the same length for past_key_values
																						snake_case_           =							seqlen + 2
																						snake_case_           =							(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						snake_case_           =							[
																						    (torch.zeros(a ), torch.zeros(a )) for _ in range(self.num_layers )
																						]

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

								return ordered_inputs






	@property
	def 							_UpperCamelCase      (	self )	->   int:
								return 13













from __future__ import annotations

from typing import Any





def 						__UpperCAmelCase      (					*a_):
							if all(_check_not_integer(a_) for m in matrix_s):
														for i in matrix_s[1:]:
																					_verify_matrix_sizes(matrix_s[0]							,					a_)
														return [[sum(a_) for t in zip(*a_)] for m in zip(*a_)]
							raise TypeError('Expected a matrix, got int/list instead')





def 						__UpperCAmelCase      (					a_							,					a_):
							if (
							    _check_not_integer(a_)
							    and _check_not_integer(a_)
							    and _verify_matrix_sizes(a_							,					a_)
							):
														return [[i - j for i, j in zip(*a_)] for m in zip(a_							,					a_)]
							raise TypeError('Expected a matrix, got int/list instead')





def 						__UpperCAmelCase      (					a_							,					a_):
							return [[x * n for x in row] for row in matrix]





def 						__UpperCAmelCase      (					a_							,					a_):
							if _check_not_integer(a_) and _check_not_integer(a_):
														snake_case_      ,		snake_case_           =							_verify_matrix_sizes(a_							,					a_)

							if cols[0] != rows[1]:
														snake_case_           =							(
														    'Cannot multiply matrix of dimensions '
														    f'''({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})'''
														)
														raise ValueError(a_)
							return [
							    [sum(m * n for m, n in zip(a_							,					a_)) for j in zip(*a_)] for i in matrix_a
							]





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							int(a_)
							return [[int(row == column) for column in range(a_)] for row in range(a_)]





def 						__UpperCAmelCase      (					a_							,					a_ = True):
							if _check_not_integer(a_):
														if return_map:
																					return map(a_							,					zip(*a_))
														else:
																					return list(map(a_							,					zip(*a_)))
							raise TypeError('Expected a matrix, got int/list instead')





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							snake_case_           =							matrix[:row] + matrix[row + 1 :]
							return [row[:column] + row[column + 1 :] for row in minor]





def 						__UpperCAmelCase      (					a_):
							if len(a_) == 1:
														return matrix[0][0]

							return sum(
							    x * determinant(minor(a_							,					0							,					a_)) * (-1) ** i
							    for i, x in enumerate(matrix[0]))





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							determinant(a_)
							if det == 0:
														return None

							snake_case_           =							[
							    [determinant(minor(a_							,					a_							,					a_)) for j in range(len(a_))]
							    for i in range(len(a_))
							]

							snake_case_           =							[
							    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row])]
							    for row in range(len(a_))
							]
							snake_case_           =							list(transpose(a_))
							return scalar_multiply(a_							,					1 / det)





def 						__UpperCAmelCase      (					a_):
							return not isinstance(a_							,					a_) and not isinstance(matrix[0]							,					a_)





def 						__UpperCAmelCase      (					a_):
							return len(a_), len(matrix[0])





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							_shape(a_) + _shape(a_)
							if shape[0] != shape[3] or shape[1] != shape[2]:
														snake_case_           =							(
														    'operands could not be broadcast together with shape '
														    f'''({shape[0], shape[1]}), ({shape[2], shape[3]})'''
														)
														raise ValueError(a_)
							return (shape[0], shape[2]), (shape[1], shape[3])





def 						__UpperCAmelCase      (					):
							snake_case_           =							[[12, 10], [3, 9]]
							snake_case_           =							[[3, 4], [7, 4]]
							snake_case_           =							[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
							snake_case_           =							[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
							print(f'''Add Operation, {add(a_							,					a_) = } \n''')
							print(
							    f'''Multiply Operation, {multiply(a_							,					a_) = } \n'''							,					)
							print(f'''Identity: {identity(5)}\n''')
							print(f'''Minor of {matrix_c} = {minor(a_							,					1							,					2)} \n''')
							print(f'''Determinant of {matrix_b} = {determinant(a_)} \n''')
							print(f'''Inverse of {matrix_d} = {inverse(a_)}\n''')


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





