

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '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 							UpperCAmelCase_						(     __snake_case  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       "bart"
							UpperCAmelCase__			:		Optional[int]             =       ["past_key_values"]
							UpperCAmelCase__			:		int             =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


							def __init__(     self			,				_a=5_0_2_6_5			,				_a=1_0_2_4			,				_a=1_2			,				_a=4_0_9_6			,				_a=1_6			,				_a=1_2			,				_a=4_0_9_6			,				_a=1_6			,				_a=0.0			,				_a=0.0			,				_a="gelu"			,				_a=1_0_2_4			,				_a=0.1			,				_a=0.0			,				_a=0.0			,				_a=0.02			,				_a=0.0			,				_a=False			,				_a=True			,				_a=3			,				_a=1			,				_a=0			,				_a=2			,				_a=True			,				_a=2			,				_a=2			,				**_a			,				)       ->       Any:
												_a      :       Tuple   =						vocab_size
												_a      :       Optional[int]   =						max_position_embeddings
												_a      :       Any   =						d_model
												_a      :       Any   =						encoder_ffn_dim
												_a      :       Any   =						encoder_layers
												_a      :       Optional[int]   =						encoder_attention_heads
												_a      :       Tuple   =						decoder_ffn_dim
												_a      :       Dict   =						decoder_layers
												_a      :       Optional[int]   =						decoder_attention_heads
												_a      :       List[Any]   =						dropout
												_a      :       str   =						attention_dropout
												_a      :       Dict   =						activation_dropout
												_a      :       Dict   =						activation_function
												_a      :       List[Any]   =						init_std
												_a      :       Tuple   =						encoder_layerdrop
												_a      :       Any   =						decoder_layerdrop
												_a      :       List[str]   =						classifier_dropout
												_a      :       Tuple   =						use_cache
												_a      :       Dict   =						encoder_layers
												_a      :       Any   =						scale_embedding  # scale factor will be sqrt(d_model) if True

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

												# ensure backward compatibility for BART CNN models
												if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''			,				_a			):
																	_a      :       List[Any]   =						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 							UpperCAmelCase_						(     __snake_case  ):
							"""simple docstring"""







							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:






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

																	if self.use_past:
																						_a      :       Optional[Any]   =						{0: '''batch'''}
																						_a      :       Any   =						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																	else:
																						_a      :       List[Any]   =						{0: '''batch''', 1: '''decoder_sequence'''}
																						_a      :       int   =						{0: '''batch''', 1: '''decoder_sequence'''}

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






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


							def 							__lowercase							(     self			,				_a			,				_a = -1			,				_a = -1			,				_a = False			,				_a = None			,				)       ->       Mapping[str, Any]:
												_a      :       str   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _a			,				_a			,				_a			,				_a			,				_a			)

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

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

																	_a      :       Tuple   =						torch.cat(
																	    [common_inputs['''decoder_attention_mask'''], torch.ones(_a			,				_a			)]			,				dim=1			)

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

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


							def 							__lowercase							(     self			,				_a			,				_a = -1			,				_a = -1			,				_a = False			,				_a = None			,				)       ->       Mapping[str, Any]:
												_a      :       Union[str, Any]   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _a			,				_a			,				_a			,				_a			,				_a			)

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

																	_a      :       Dict   =						common_inputs['''attention_mask'''].dtype
																	_a      :       List[Any]   =						torch.cat(
																	    [common_inputs['''attention_mask'''], torch.ones(_a			,				_a			,				dtype=_a			)]			,				dim=1			)
																	_a      :       Optional[Any]   =						[
																	    (torch.zeros(_a			), torch.zeros(_a			)) for _ in range(_a			)
																	]
												return common_inputs


							def 							__lowercase							(     self			,				_a			,				_a = -1			,				_a = -1			,				_a = False			,				_a = None			,				)       ->       Mapping[str, Any]:
												_a      :       Any   =						compute_effective_axis_dimension(
												    _a			,				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      :       Union[str, Any]   =						tokenizer.num_special_tokens_to_add(_a			)
												_a      :       Dict   =						compute_effective_axis_dimension(
												    _a			,				fixed_dimension=OnnxConfig.default_fixed_sequence			,				num_token_to_add=_a			)

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


							def 							__lowercase							(     self			,				_a			,				_a = -1			,				_a = -1			,				_a = False			,				_a = None			,				)       ->       Mapping[str, Any]:






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

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

												return common_inputs




							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			)       ->       int:






												if self.task in ["default", "seq2seq-lm"]:
																	_a      :       Dict   =						super()._flatten_past_key_values_(_a			,				_a			,				_a			,				_a			)
												else:
																	_a      :       Optional[int]   =						super(_a			,				self			)._flatten_past_key_values_(
																	    _a			,				_a			,				_a			,				_a			)





'''simple docstring'''




from random import randint, random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__ = False						,							snake_case__ = False						,							snake_case__ = 5						,							):




    '''simple docstring'''



    A			:  List[str]   =		[[-1] * number_of_cells]  # Create a highway without any car
    A			:  Optional[Any]   =		0
    A			:  Tuple   =		max(snake_case__						,							0		)
    while i < number_of_cells:
        A			:  Optional[int]   =		(
            randint(0						,							snake_case__		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1						,							max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    A			:  str   =		highway_now[car_index + 1 :]
    for cell in range(len(snake_case__		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(snake_case__						,							-1		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  int   =		len(snake_case__		)
    # Beforce calculations, the highway is empty
    A			:  int   =		[-1] * number_of_cells

    for car_index in range(snake_case__		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:  Optional[Any]   =		min(highway_now[car_index] + 1						,							snake_case__		)
            # Number of empty cell before the next car
            A			:  List[str]   =		get_distance(snake_case__						,							snake_case__		) - 1
            # We can't have the car causing an accident
            A			:  Union[str, Any]   =		min(next_highway[car_index]						,							snake_case__		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:  List[str]   =		max(next_highway[car_index] - 1						,							0		)
    return next_highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		len(highway[0]		)

    for i in range(snake_case__		):
        A			:  int   =		update(highway[i]						,							snake_case__						,							snake_case__		)
        A			:  Any   =		[-1] * number_of_cells

        for car_index in range(snake_case__		):
            A			:  Dict   =		next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:  Dict   =		(car_index + speed) % number_of_cells
                # Commit the change of position
                A			:  List[str]   =		speed
        highway.append(snake_case__		)

    return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




