




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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """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 			a__    (					snake_case__      ):
						_a							:    Union[str, Any]					=    """bart"""
						_a							:    List[str]					=    ["""past_key_values"""]
						_a							:    List[Any]					=    {"""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		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       max_position_embeddings
											__lowerCAmelCase				   =       d_model
											__lowerCAmelCase				   =       encoder_ffn_dim
											__lowerCAmelCase				   =       encoder_layers
											__lowerCAmelCase				   =       encoder_attention_heads
											__lowerCAmelCase				   =       decoder_ffn_dim
											__lowerCAmelCase				   =       decoder_layers
											__lowerCAmelCase				   =       decoder_attention_heads
											__lowerCAmelCase				   =       dropout
											__lowerCAmelCase				   =       attention_dropout
											__lowerCAmelCase				   =       activation_dropout
											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       init_std
											__lowerCAmelCase				   =       encoder_layerdrop
											__lowerCAmelCase				   =       decoder_layerdrop
											__lowerCAmelCase				   =       classifier_dropout
											__lowerCAmelCase				   =       use_cache
											__lowerCAmelCase				   =       encoder_layers
											__lowerCAmelCase				   =       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			):
																__lowerCAmelCase				   =       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 			a__    (					snake_case__      ):






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

																if self.use_past:
																					__lowerCAmelCase				   =       {0: "batch"}
																					__lowerCAmelCase				   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
																else:
																					__lowerCAmelCase				   =       {0: "batch", 1: "decoder_sequence"}
																					__lowerCAmelCase				   =       {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.
																__lowerCAmelCase				   =       OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																    ]			)
																if self.use_past:
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																					for i in range(_A			):
																										__lowerCAmelCase				   =       {0: "batch", 2: "past_sequence + sequence"}
																										__lowerCAmelCase				   =       {0: "batch", 2: "past_sequence + sequence"}
											else:
																__lowerCAmelCase				   =       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			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _A		,			_A		,			_A		,			_A		,			_A			)

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

																__lowerCAmelCase				   =       torch.cat(
																    [common_inputs["decoder_attention_mask"], torch.ones(_A		,			_A			)]		,			dim=1			)

																__lowerCAmelCase				   =       []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																__lowerCAmelCase				   =       min(_A		,			_A			)
																__lowerCAmelCase				   =       max(_A		,			_A			) - min_num_layers
																__lowerCAmelCase				   =       "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.
																__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       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
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																__lowerCAmelCase				   =       seqlen + 2
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_attention_heads
																__lowerCAmelCase				   =       (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       tokenizer.num_special_tokens_to_add(_A			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
											__lowerCAmelCase				   =       dict(tokenizer(_A		,			return_tensors=_A			)			)
											return common_inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase				   =       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":
																__lowerCAmelCase				   =       self._generate_dummy_inputs_for_causal_lm(
																    _A		,			batch_size=_A		,			seq_length=_A		,			is_pair=_A		,			framework=_A			)
											else:
																__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase				   =       super()._flatten_past_key_values_(_A		,			_A		,			_A		,			_A			)
											else:
																__lowerCAmelCase				   =       super(_A		,			self			)._flatten_past_key_values_(
																    _A		,			_A		,			_A		,			_A			)










import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)




