
"""simple docstring"""
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


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Tuple =					{
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''mbart'''
							SCREAMING_SNAKE_CASE		     =						['''past_key_values''']
							SCREAMING_SNAKE_CASE		     =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






							def __init__(					self						:			List[Any]						, snake_case__						:			Union[str, Any]=5_0_2_6_5						, snake_case__						:			Tuple=1_0_2_4						, snake_case__						:			Union[str, Any]=1_2						, snake_case__						:			Dict=4_0_9_6						, snake_case__						:			Optional[int]=1_6						, snake_case__						:			Optional[int]=1_2						, snake_case__						:			Optional[int]=4_0_9_6						, snake_case__						:			Optional[int]=1_6						, snake_case__						:			str=0.0						, snake_case__						:			Union[str, Any]=0.0						, snake_case__						:			List[Any]=True						, snake_case__						:			Tuple=True						, snake_case__						:			Dict="gelu"						, snake_case__						:			Dict=1_0_2_4						, snake_case__						:			Optional[Any]=0.1						, snake_case__						:			Any=0.0						, snake_case__						:			List[Any]=0.0						, snake_case__						:			int=0.02						, snake_case__						:			List[str]=0.0						, snake_case__						:			Any=False						, snake_case__						:			Any=1						, snake_case__						:			Dict=0						, snake_case__						:			Any=2						, snake_case__						:			Optional[int]=2						, **snake_case__						:			Dict						, ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					vocab_size
												__a:						int		    =					max_position_embeddings
												__a:						Optional[Any]		    =					d_model
												__a:						List[str]		    =					encoder_ffn_dim
												__a:						Any		    =					encoder_layers
												__a:						Optional[Any]		    =					encoder_attention_heads
												__a:						Optional[Any]		    =					decoder_ffn_dim
												__a:						Any		    =					decoder_layers
												__a:						Any		    =					decoder_attention_heads
												__a:						List[Any]		    =					dropout
												__a:						Optional[int]		    =					attention_dropout
												__a:						List[str]		    =					activation_dropout
												__a:						Any		    =					activation_function
												__a:						Any		    =					init_std
												__a:						Dict		    =					encoder_layerdrop
												__a:						Dict		    =					decoder_layerdrop
												__a:						Optional[Any]		    =					classifier_dropout
												__a:						Tuple		    =					use_cache
												__a:						Dict		    =					encoder_layers
												__a:						Optional[Any]		    =					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__						, forced_eos_token_id=snake_case__						, **snake_case__						, )






class    _snake_case  (						_lowercase       ):






							@property
							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



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

																	if self.use_past:
																						__a:						Dict		    =					{0: """batch"""}
																						__a:						List[str]		    =					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																	else:
																						__a:						Optional[Any]		    =					{0: """batch""", 1: """decoder_sequence"""}
																						__a:						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.
																	__a:						Any		    =					OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	    ]    )
																	if self.use_past:
																						__a    ,				__a:						List[Any]		    =					self.num_layers
																						for i in range(snake_case__    ):
																											__a:						List[str]		    =					{0: """batch""", 2: """past_sequence + sequence"""}
																											__a:						Optional[int]		    =					{0: """batch""", 2: """past_sequence + sequence"""}
												else:
																	__a:						Optional[Any]		    =					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						:			List[str]    ):






												"""simple docstring"""



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






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






												"""simple docstring"""



												__a:						int		    =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__    )

												# Generate decoder inputs
												__a:						Dict		    =					seq_length if not self.use_past else 1
												__a:						int		    =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__    )
												__a:						List[str]		    =					{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
												__a:						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
																	__a    ,				__a:						Tuple		    =					common_inputs["""input_ids"""].shape
																	__a:						int		    =					common_inputs["""decoder_input_ids"""].shape[1]
																	__a    ,				__a:						List[str]		    =					self.num_attention_heads
																	__a:						Union[str, Any]		    =					(
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	__a:						Union[str, Any]		    =					decoder_seq_length + 3
																	__a:						int		    =					(
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	__a:						Optional[Any]		    =					torch.cat(
																	    [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case__						, snake_case__    )]						, dim=1    )

																	__a:						List[str]		    =					[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	__a    ,				__a:						Dict		    =					self.num_layers
																	__a:						Dict		    =					min(snake_case__						, snake_case__    )
																	__a:						Optional[int]		    =					max(snake_case__						, snake_case__    ) - min_num_layers
																	__a:						Dict		    =					"""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.
																	__a:						Tuple		    =					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 							__lowercase  (					self						:			Tuple						, snake_case__						:			PreTrainedTokenizer						, snake_case__						:			int = -1						, snake_case__						:			int = -1						, snake_case__						:			bool = False						, snake_case__						:			Optional[TensorType] = None						, ):






												"""simple docstring"""



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

																	__a:						List[str]		    =					common_inputs["""attention_mask"""].dtype
																	__a:						Any		    =					torch.cat(
																	    [common_inputs["""attention_mask"""], torch.ones(snake_case__						, snake_case__						, dtype=snake_case__    )]						, dim=1    )
																	__a:						Any		    =					[
																	    (torch.zeros(snake_case__    ), torch.zeros(snake_case__    )) for _ in range(snake_case__    )
																	]
												return common_inputs






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






												"""simple docstring"""



												__a:						str		    =					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
												__a:						Dict		    =					tokenizer.num_special_tokens_to_add(snake_case__    )
												__a:						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
												__a:						str		    =					[""" """.join([tokenizer.unk_token]    ) * seq_length] * batch_size
												__a:						Dict		    =					dict(tokenizer(snake_case__						, return_tensors=snake_case__    )    )
												return common_inputs






							def 							__lowercase  (					self						:			str						, 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"]:
																	__a:						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":
																	__a:						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:
																	__a:						Optional[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 							__lowercase  (					self						:			str						, snake_case__						:			List[Any]						, snake_case__						:			List[Any]						, snake_case__						:			List[str]						, snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												if self.task in ["default", "seq2seq-lm"]:
																	__a:						Union[str, Any]		    =					super()._flatten_past_key_values_(snake_case__						, snake_case__						, snake_case__						, snake_case__    )
												else:
																	__a:						List[Any]		    =					super(snake_case__						, self    )._flatten_past_key_values_(
																	    snake_case__						, snake_case__						, snake_case__						, snake_case__    )


"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    _snake_case  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






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






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
