




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Any          =  {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """blenderbot-small"""
						UpperCamelCase      =       ["""past_key_values"""]
						UpperCamelCase      =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

						def __init__(		self :Any     ,			__snake_case :Optional[int]=5_02_65     ,			__snake_case :Any=5_12     ,			__snake_case :Tuple=8     ,			__snake_case :Optional[Any]=20_48     ,			__snake_case :List[Any]=16     ,			__snake_case :Any=8     ,			__snake_case :Union[str, Any]=20_48     ,			__snake_case :Any=16     ,			__snake_case :List[str]=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :str=True     ,			__snake_case :Optional[int]=True     ,			__snake_case :Optional[int]="gelu"     ,			__snake_case :Dict=5_12     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Tuple=0.0     ,			__snake_case :Optional[Any]=0.0     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Optional[int]=1     ,			__snake_case :str=False     ,			__snake_case :List[Any]=0     ,			__snake_case :int=1     ,			__snake_case :List[Any]=2     ,			__snake_case :Optional[Any]=2     ,			**__snake_case :str     ,			):
										'''simple docstring'''

										__magic_name__						:						int										=vocab_size
										__magic_name__						:						Optional[Any]										=max_position_embeddings
										__magic_name__						:						Optional[Any]										=d_model
										__magic_name__						:						str										=encoder_ffn_dim
										__magic_name__						:						Tuple										=encoder_layers
										__magic_name__						:						List[str]										=encoder_attention_heads
										__magic_name__						:						Union[str, Any]										=decoder_ffn_dim
										__magic_name__						:						int										=decoder_layers
										__magic_name__						:						Tuple										=decoder_attention_heads
										__magic_name__						:						Tuple										=dropout
										__magic_name__						:						List[str]										=attention_dropout
										__magic_name__						:						int										=activation_dropout
										__magic_name__						:						Union[str, Any]										=activation_function
										__magic_name__						:						Any										=init_std
										__magic_name__						:						Any										=encoder_layerdrop
										__magic_name__						:						Optional[int]										=decoder_layerdrop
										__magic_name__						:						int										=use_cache
										__magic_name__						:						Dict										=encoder_layers
										__magic_name__						:						Tuple										=scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			is_encoder_decoder=__snake_case     ,			decoder_start_token_id=__snake_case     ,			forced_eos_token_id=__snake_case     ,			**__snake_case     ,			)




class 		__A   (      UpperCamelCase__      ):

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

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						List[Any]										=OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]			)

														if self.use_past:
																		__magic_name__						:						Tuple										={0: """batch"""}
																		__magic_name__						:						Tuple										={0: """batch""", 1: """past_decoder_sequence + sequence"""}
														else:
																		__magic_name__						:						Optional[Any]										={0: """batch""", 1: """decoder_sequence"""}
																		__magic_name__						:						Union[str, Any]										={0: """batch""", 1: """decoder_sequence"""}

														if self.use_past:
																		self.fill_with_past_key_values_(__snake_case     ,			direction="""inputs"""			)
										elif self.task == "causal-lm":
														# TODO: figure this case out.
														__magic_name__						:						Optional[Any]										=OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]			)
														if self.use_past:
																		__magic_name__					,			__magic_name__						:						List[Any]										=self.num_layers
																		for i in range(__snake_case			):
																						__magic_name__						:						Optional[int]										={0: """batch""", 2: """past_sequence + sequence"""}
																						__magic_name__						:						str										={0: """batch""", 2: """past_sequence + sequence"""}
										else:
														__magic_name__						:						Dict										=OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
														        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
														    ]			)

										return common_inputs

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

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						List[Any]										=super().outputs
										else:
														__magic_name__						:						Optional[Any]										=super(__snake_case     ,			self			).outputs
														if self.use_past:
																		__magic_name__					,			__magic_name__						:						int										=self.num_layers
																		for i in range(__snake_case			):
																						__magic_name__						:						Optional[int]										={0: """batch""", 2: """past_sequence + sequence"""}
																						__magic_name__						:						str										={0: """batch""", 2: """past_sequence + sequence"""}
										return common_outputs

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

										__magic_name__						:						str										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)

										# Generate decoder inputs
										__magic_name__						:						List[str]										=seq_length if not self.use_past else 1
										__magic_name__						:						Any										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										__magic_name__						:						Any										={f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
										__magic_name__						:						List[str]										=dict(**__snake_case     ,			**__snake_case			)

										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__						:						int										=common_inputs["""input_ids"""].shape
														__magic_name__						:						Any										=common_inputs["""decoder_input_ids"""].shape[1]
														__magic_name__					,			__magic_name__						:						Optional[int]										=self.num_attention_heads
														__magic_name__						:						int										=(
														    batch,
														    num_encoder_attention_heads,
														    encoder_seq_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														__magic_name__						:						List[Any]										=decoder_seq_length + 3
														__magic_name__						:						List[Any]										=(
														    batch,
														    num_decoder_attention_heads,
														    decoder_past_length,
														    self._config.hidden_size // num_decoder_attention_heads,
														)

														__magic_name__						:						Any										=torch.cat(
														    [common_inputs["""decoder_attention_mask"""], torch.ones(__snake_case     ,			__snake_case			)]     ,			dim=1			)

														__magic_name__						:						Optional[Any]										=[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														__magic_name__					,			__magic_name__						:						Dict										=self.num_layers
														__magic_name__						:						List[Any]										=min(__snake_case     ,			__snake_case			)
														__magic_name__						:						List[Any]										=max(__snake_case     ,			__snake_case			) - min_num_layers
														__magic_name__						:						Optional[Any]										="""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

														for _ in range(__snake_case			):
																		common_inputs["past_key_values"].append(
																		    (
																		        torch.zeros(__snake_case			),
																		        torch.zeros(__snake_case			),
																		        torch.zeros(__snake_case			),
																		        torch.zeros(__snake_case			),
																		    )			)
														# TODO: test this.
														__magic_name__						:						int										=encoder_shape if remaining_side_name == """encoder""" else decoder_shape
														for _ in range(__snake_case     ,			__snake_case			):
																		common_inputs["past_key_values"].append((torch.zeros(__snake_case			), torch.zeros(__snake_case			))			)
										return common_inputs

						def      A__    (		self :List[str]     ,			__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]										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)

										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__						:						int										=common_inputs["""input_ids"""].shape
														# Not using the same length for past_key_values
														__magic_name__						:						int										=seqlen + 2
														__magic_name__					,			__magic_name__						:						Tuple										=self.num_layers
														__magic_name__					,			__magic_name__						:						List[str]										=self.num_attention_heads
														__magic_name__						:						List[Any]										=(
														    batch,
														    num_encoder_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)

														__magic_name__						:						Union[str, Any]										=common_inputs["""attention_mask"""].dtype
														__magic_name__						:						int										=torch.cat(
														    [common_inputs["""attention_mask"""], torch.ones(__snake_case     ,			__snake_case     ,			dtype=__snake_case			)]     ,			dim=1			)
														__magic_name__						:						Dict										=[
														    (torch.zeros(__snake_case			), torch.zeros(__snake_case			)) for _ in range(__snake_case			)
														]
										return common_inputs

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

										__magic_name__						:						Tuple										=compute_effective_axis_dimension(
										    __snake_case     ,			fixed_dimension=OnnxConfig.default_fixed_batch     ,			num_token_to_add=0			)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										__magic_name__						:						Any										=tokenizer.num_special_tokens_to_add(__snake_case			)
										__magic_name__						:						Optional[Any]										=compute_effective_axis_dimension(
										    __snake_case     ,			fixed_dimension=OnnxConfig.default_fixed_sequence     ,			num_token_to_add=__snake_case			)

										# Generate dummy inputs according to compute batch and sequence
										__magic_name__						:						List[str]										=[""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
										__magic_name__						:						int										=dict(tokenizer(__snake_case     ,			return_tensors=__snake_case			)			)
										return common_inputs

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

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						Any										=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    __snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)

										elif self.task == "causal-lm":
														__magic_name__						:						List[Any]										=self._generate_dummy_inputs_for_causal_lm(
														    __snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)
										else:
														__magic_name__						:						int										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    __snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)

										return common_inputs


						def      A__    (		self :List[str]     ,			__snake_case :Any     ,			__snake_case :Dict     ,			__snake_case :Any     ,			__snake_case :Any			):
										'''simple docstring'''

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						Union[str, Any]										=super()._flatten_past_key_values_(__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										else:
														__magic_name__						:						Optional[Any]										=super(__snake_case     ,			self			)._flatten_past_key_values_(
														    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)











from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       )				-> list[float]:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   List[str]  				=     coefficient_matrix.shape
							lowercase_				,		lowercase_	:   Any  				=     constant_matrix.shape

							if rowsa != colsa:
														lowercase_	:   List[Any]  				=     f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if colsa != 1:
														lowercase_	:   Optional[int]  				=     f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if rowsa != rowsa:
														lowercase_	:   Tuple  				=     (
														    """Coefficient and constant matrices dimensions must be nxn and nx1 but """
														    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(lowercase				)

							if len(lowercase				) != rowsa:
														lowercase_	:   int  				=     (
														    """Number of initial values must be equal to number of rows in coefficient """
														    f"""matrix but received {len(lowercase				)} and {rowsa}"""
														)
														raise ValueError(lowercase				)

							if iterations <= 0:
														raise ValueError("""Iterations must be at least 1"""				)

							lowercase_	:   NDArray[floataa]  				=     np.concatenate(
							    (coefficient_matrix, constant_matrix)				,       axis=1				)

							lowercase_				,		lowercase_	:   Dict  				=     table.shape

							strictly_diagonally_dominant(lowercase				)

							# Iterates the whole matrix for given number of times
							for _ in range(lowercase				):
														lowercase_	:   str  				=     []
														for row in range(lowercase				):
																					lowercase_	:   Dict  				=     0
																					for col in range(lowercase				):
																												if col == row:
																																			lowercase_	:   Optional[Any]  				=     table[row][col]
																												elif col == cols - 1:
																																			lowercase_	:   Tuple  				=     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowercase_	:   List[Any]  				=     (temp + val) / denom
																					new_val.append(lowercase				)
														lowercase_	:   Optional[int]  				=     new_val

							return [float(lowercase				) for i in new_val]





def  __magic_name__  (				lowercase				)				-> bool:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   str  				=     table.shape

							lowercase_	:   str  				=     True

							for i in range(0				,       lowercase				):
														lowercase_	:   Any  				=     0
														for j in range(0				,       cols - 1				):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""				)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
							import doctest

							doctest.testmod()