



import warnings

from .generation import FlaxGenerationMixin

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						# warning at import time
						warnings.warn(
						    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."    ,_A    ,)




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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """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 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"mbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=5_02_65						,						__lowerCamelCase   :      str=10_24						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      Dict=40_96						,						__lowerCamelCase   :      str=16						,						__lowerCamelCase   :      int=12						,						__lowerCamelCase   :      Optional[Any]=40_96						,						__lowerCamelCase   :      Any=16						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      List[str]=True						,						__lowerCamelCase   :      Optional[int]="gelu"						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Optional[Any]=1						,						__lowerCamelCase   :      str=0						,						__lowerCamelCase   :      Optional[Any]=2						,						__lowerCamelCase   :      Dict=2						,						**__lowerCamelCase   :      int						,						)   ->					Union[str, Any]:
									A				:       Union[str, Any]														=    vocab_size
									A				:       List[str]														=    max_position_embeddings
									A				:       Optional[int]														=    d_model
									A				:       Any														=    encoder_ffn_dim
									A				:       Union[str, Any]														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Union[str, Any]														=    decoder_ffn_dim
									A				:       Any														=    decoder_layers
									A				:       Tuple														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       Union[str, Any]														=    attention_dropout
									A				:       str														=    activation_dropout
									A				:       Dict														=    activation_function
									A				:       Optional[int]														=    init_std
									A				:       Optional[Any]														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       Tuple														=    classifier_dropout
									A				:       Union[str, Any]														=    use_cache
									A				:       List[str]														=    encoder_layers
									A				:       List[str]														=    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__lowerCamelCase						,						bos_token_id=__lowerCamelCase						,						eos_token_id=__lowerCamelCase						,						is_encoder_decoder=__lowerCamelCase						,						forced_eos_token_id=__lowerCamelCase						,						**__lowerCamelCase						,						)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[str]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)

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

												if self.use_past:
															self.fill_with_past_key_values_(__lowerCamelCase						,						direction="inputs"			)
									elif self.task == "causal-lm":
												# TODO: figure this case out.
												A				:       Dict														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)
												if self.use_past:
															A							,			A				:       Union[str, Any]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[str]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       int														=    {0: "batch", 2: "past_sequence + sequence"}
									else:
												A				:       Optional[int]														=    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       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       str														=    super().outputs
									else:
												A				:       int														=    super(__lowerCamelCase						,						self			).outputs
												if self.use_past:
															A							,			A				:       Optional[int]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[Any]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       Tuple														=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       Tuple														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									# Generate decoder inputs
									A				:       Optional[int]														=    seq_length if not self.use_past else 1
									A				:       str														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									A				:       Tuple														=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									A				:       Union[str, Any]														=    dict(**__lowerCamelCase						,						**__lowerCamelCase			)

									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
												A				:       Any														=    common_inputs["decoder_input_ids"].shape[1]
												A							,			A				:       Union[str, Any]														=    self.num_attention_heads
												A				:       Tuple														=    (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												A				:       Dict														=    decoder_seq_length + 3
												A				:       Union[str, Any]														=    (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												A				:       int														=    torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase			)]						,						dim=1			)

												A				:       Optional[int]														=    []
												# 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(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Optional[Any]														=    max(__lowerCamelCase						,						__lowerCamelCase			) - min_num_layers
												A				:       Tuple														=    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									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				:       Any														=    seqlen + 2
												A							,			A				:       List[Any]														=    self.num_layers
												A							,			A				:       int														=    self.num_attention_heads
												A				:       int														=    (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A				:       Tuple														=    common_inputs["attention_mask"].dtype
												A				:       Dict														=    torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase						,						dtype=__lowerCamelCase			)]						,						dim=1			)
												A				:       Optional[Any]														=    [
												    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
												]
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									# 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				:       Any														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						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				:       Any														=    tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
									A				:       Tuple														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						fixed_dimension=OnnxConfig.default_fixed_sequence						,						num_token_to_add=__lowerCamelCase			)

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

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

									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      List[Any]			)   ->					Dict:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       Any														=    super()._flatten_past_key_values_(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									else:
												A				:       Optional[Any]														=    super(__lowerCamelCase						,						self			)._flatten_past_key_values_(
												    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)