



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

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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Tuple    ='bart'
							__lowerCamelCase	:	Optional[Any]    =['past_key_values']
							__lowerCamelCase	:	Tuple    ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





							def __init__(   self		:		Tuple      ,				__lowercase		:		Tuple=50265      ,				__lowercase		:		int=1024      ,				__lowercase		:		int=12      ,				__lowercase		:		str=4096      ,				__lowercase		:		Any=16      ,				__lowercase		:		List[str]=12      ,				__lowercase		:		Optional[Any]=4096      ,				__lowercase		:		Union[str, Any]=16      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Dict=0.0      ,				__lowercase		:		int="gelu"      ,				__lowercase		:		Optional[Any]=1024      ,				__lowercase		:		Union[str, Any]=0.1      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Optional[int]=0.02      ,				__lowercase		:		Any=0.0      ,				__lowercase		:		Dict=False      ,				__lowercase		:		int=True      ,				__lowercase		:		Optional[Any]=3      ,				__lowercase		:		Optional[Any]=1      ,				__lowercase		:		Dict=0      ,				__lowercase		:		Union[str, Any]=2      ,				__lowercase		:		List[Any]=True      ,				__lowercase		:		Any=2      ,				__lowercase		:		str=2      ,				**__lowercase		:		Tuple      ,				):

											'''simple docstring'''
											__a       =		vocab_size
											__a       =		max_position_embeddings
											__a       =		d_model
											__a       =		encoder_ffn_dim
											__a       =		encoder_layers
											__a       =		encoder_attention_heads
											__a       =		decoder_ffn_dim
											__a       =		decoder_layers
											__a       =		decoder_attention_heads
											__a       =		dropout
											__a       =		attention_dropout
											__a       =		activation_dropout
											__a       =		activation_function
											__a       =		init_std
											__a       =		encoder_layerdrop
											__a       =		decoder_layerdrop
											__a       =		classifier_dropout
											__a       =		use_cache
											__a       =		encoder_layers
											__a       =		scale_embedding  # scale factor will be sqrt(d_model) if True

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

											# ensure backward compatibility for BART CNN models
											if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""      ,				__lowercase	):
															__a       =		self.bos_token_id
															warnings.warn(
															    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
															    """The config can simply be saved and uploaded again to be fixed."""	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





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

											'''simple docstring'''
											if self.task in ["default", "seq2seq-lm"]:
															__a       =		OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
															    ]	)

															if self.use_past:
																			__a       =		{0: """batch"""}
																			__a       =		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
															else:
																			__a       =		{0: """batch""", 1: """decoder_sequence"""}
																			__a       =		{0: """batch""", 1: """decoder_sequence"""}

															if self.use_past:
																			self.fill_with_past_key_values_(__lowercase      ,				direction="""inputs"""	)
											elif self.task == "causal-lm":
															# TODO: figure this case out.
															__a       =		OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
															    ]	)
															if self.use_past:
																			__a     ,					__a       =		self.num_layers
																			for i in range(__lowercase	):
																							__a       =		{0: """batch""", 2: """past_sequence + sequence"""}
																							__a       =		{0: """batch""", 2: """past_sequence + sequence"""}
											else:
															__a       =		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 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											if self.task in ["default", "seq2seq-lm"]:
															__a       =		super().outputs
											else:
															__a       =		super(__lowercase      ,				self	).outputs
															if self.use_past:
																			__a     ,					__a       =		self.num_layers
																			for i in range(__lowercase	):
																							__a       =		{0: """batch""", 2: """past_sequence + sequence"""}
																							__a       =		{0: """batch""", 2: """past_sequence + sequence"""}
											return common_outputs





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		PreTrainedTokenizer      ,				__lowercase		:		int = -1      ,				__lowercase		:		int = -1      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[TensorType] = None      ,				):

											'''simple docstring'''
											__a       =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											# Generate decoder inputs
											__a       =		seq_length if not self.use_past else 1
											__a       =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
											__a       =		dict(**__lowercase      ,				**__lowercase	)

											if self.use_past:
															if not is_torch_available():
																			raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
															else:
																			import torch
															__a     ,					__a       =		common_inputs["""input_ids"""].shape
															__a       =		common_inputs["""decoder_input_ids"""].shape[1]
															__a     ,					__a       =		self.num_attention_heads
															__a       =		(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															__a       =		decoder_seq_length + 3
															__a       =		(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															__a       =		torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(__lowercase      ,				__lowercase	)]      ,				dim=1	)

															__a       =		[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															__a     ,					__a       =		self.num_layers
															__a       =		min(__lowercase      ,				__lowercase	)
															__a       =		max(__lowercase      ,				__lowercase	) - min_num_layers
															__a       =		"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		PreTrainedTokenizer      ,				__lowercase		:		int = -1      ,				__lowercase		:		int = -1      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[TensorType] = None      ,				):

											'''simple docstring'''
											__a       =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											if self.use_past:
															if not is_torch_available():
																			raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
															else:
																			import torch
															__a     ,					__a       =		common_inputs["""input_ids"""].shape
															# Not using the same length for past_key_values
															__a       =		seqlen + 2
															__a     ,					__a       =		self.num_layers
															__a     ,					__a       =		self.num_attention_heads
															__a       =		(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															__a       =		common_inputs["""attention_mask"""].dtype
															__a       =		torch.cat(
															    [common_inputs["""attention_mask"""], torch.ones(__lowercase      ,				__lowercase      ,				dtype=__lowercase	)]      ,				dim=1	)
															__a       =		[
															    (torch.zeros(__lowercase	), torch.zeros(__lowercase	)) for _ in range(__lowercase	)
															]
											return common_inputs





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		PreTrainedTokenizer      ,				__lowercase		:		int = -1      ,				__lowercase		:		int = -1      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[TensorType] = None      ,				):

											'''simple docstring'''
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											__a       =		compute_effective_axis_dimension(
											    __lowercase      ,				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
											__a       =		tokenizer.num_special_tokens_to_add(__lowercase	)
											__a       =		compute_effective_axis_dimension(
											    __lowercase      ,				fixed_dimension=OnnxConfig.default_fixed_sequence      ,				num_token_to_add=__lowercase	)

											# Generate dummy inputs according to compute batch and sequence
											__a       =		[""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
											__a       =		dict(tokenizer(__lowercase      ,				return_tensors=__lowercase	)	)
											return common_inputs





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		PreTrainedTokenizer      ,				__lowercase		:		int = -1      ,				__lowercase		:		int = -1      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[TensorType] = None      ,				):

											'''simple docstring'''
											if self.task in ["default", "seq2seq-lm"]:
															__a       =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    __lowercase      ,				batch_size=__lowercase      ,				seq_length=__lowercase      ,				is_pair=__lowercase      ,				framework=__lowercase	)

											elif self.task == "causal-lm":
															__a       =		self._generate_dummy_inputs_for_causal_lm(
															    __lowercase      ,				batch_size=__lowercase      ,				seq_length=__lowercase      ,				is_pair=__lowercase      ,				framework=__lowercase	)
											else:
															__a       =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    __lowercase      ,				batch_size=__lowercase      ,				seq_length=__lowercase      ,				is_pair=__lowercase      ,				framework=__lowercase	)

											return common_inputs







							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		str      ,				__lowercase		:		Optional[int]	):

											'''simple docstring'''
											if self.task in ["default", "seq2seq-lm"]:
															__a       =		super()._flatten_past_key_values_(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											else:
															__a       =		super(__lowercase      ,				self	)._flatten_past_key_values_(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
