






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      :    Optional[Any]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    Any   		=   {
    """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__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"bart"
					a_			=	["past_key_values"]
					a_			=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



					def __init__(      self  :						int							,	__A  :						Dict=5_0_2_6_5							,	__A  :						Dict=1_0_2_4							,	__A  :						Tuple=1_2							,	__A  :						Union[str, Any]=4_0_9_6							,	__A  :						Tuple=1_6							,	__A  :						Tuple=1_2							,	__A  :						str=4_0_9_6							,	__A  :						Union[str, Any]=1_6							,	__A  :						Optional[int]=0.0							,	__A  :						Optional[Any]=0.0							,	__A  :						List[str]="gelu"							,	__A  :						Optional[Any]=1_0_2_4							,	__A  :						Any=0.1							,	__A  :						Optional[int]=0.0							,	__A  :						int=0.0							,	__A  :						Tuple=0.0_2							,	__A  :						str=0.0							,	__A  :						Optional[Any]=False							,	__A  :						Optional[int]=True							,	__A  :						List[Any]=3							,	__A  :						Union[str, Any]=1							,	__A  :						Dict=0							,	__A  :						int=2							,	__A  :						int=True							,	__A  :						Optional[Any]=2							,	__A  :						Tuple=2							,	**__A  :						Any							,	):
						snake_case__				:   Optional[int]     =						vocab_size
						snake_case__				:   List[str]     =						max_position_embeddings
						snake_case__				:   Tuple     =						d_model
						snake_case__				:   Any     =						encoder_ffn_dim
						snake_case__				:   Optional[Any]     =						encoder_layers
						snake_case__				:   Any     =						encoder_attention_heads
						snake_case__				:   List[str]     =						decoder_ffn_dim
						snake_case__				:   Union[str, Any]     =						decoder_layers
						snake_case__				:   int     =						decoder_attention_heads
						snake_case__				:   Optional[int]     =						dropout
						snake_case__				:   Union[str, Any]     =						attention_dropout
						snake_case__				:   str     =						activation_dropout
						snake_case__				:   Optional[Any]     =						activation_function
						snake_case__				:   Any     =						init_std
						snake_case__				:   Optional[int]     =						encoder_layerdrop
						snake_case__				:   List[str]     =						decoder_layerdrop
						snake_case__				:   int     =						classifier_dropout
						snake_case__				:   Tuple     =						use_cache
						snake_case__				:   int     =						encoder_layers
						snake_case__				:   Optional[int]     =						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 ):
							snake_case__				:   Dict     =						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__		(       UpperCamelCase_		):





					"""simple docstring"""






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

							if self.use_past:
								snake_case__				:   Optional[Any]     =						{0: "batch"}
								snake_case__				:   List[Any]     =						{0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
								snake_case__				:   Any     =						{0: "batch", 1: "decoder_sequence"}
								snake_case__				:   Union[str, Any]     =						{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.
							snake_case__				:   Any     =						OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
							        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
							    ] )
							if self.use_past:
								snake_case__,		snake_case__				:   List[str]     =						self.num_layers
								for i in range(__A ):
									snake_case__				:   List[Any]     =						{0: "batch", 2: "past_sequence + sequence"}
									snake_case__				:   List[Any]     =						{0: "batch", 2: "past_sequence + sequence"}
						else:
							snake_case__				:   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  _lowercase  (      self  :						int ):
						if self.task in ["default", "seq2seq-lm"]:
							snake_case__				:   List[Any]     =						super().outputs
						else:
							snake_case__				:   str     =						super(__A							,	self ).outputs
							if self.use_past:
								snake_case__,		snake_case__				:   Dict     =						self.num_layers
								for i in range(__A ):
									snake_case__				:   Optional[Any]     =						{0: "batch", 2: "past_sequence + sequence"}
									snake_case__				:   Tuple     =						{0: "batch", 2: "past_sequence + sequence"}
						return common_outputs



					def  _lowercase  (      self  :						Any							,	__A  :						PreTrainedTokenizer							,	__A  :						int = -1							,	__A  :						int = -1							,	__A  :						bool = False							,	__A  :						Optional[TensorType] = None							,	):
						snake_case__				:   Union[str, Any]     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __A							,	__A							,	__A							,	__A							,	__A )

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

							snake_case__				:   Dict     =						torch.cat(
							    [common_inputs["decoder_attention_mask"], torch.ones(__A							,	__A )]							,	dim=1 )

							snake_case__				:   Optional[int]     =						[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							snake_case__,		snake_case__				:   Tuple     =						self.num_layers
							snake_case__				:   List[str]     =						min(__A							,	__A )
							snake_case__				:   Any     =						max(__A							,	__A ) - min_num_layers
							snake_case__				:   Optional[Any]     =						"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.
							snake_case__				:   Any     =						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  :						List[Any]							,	__A  :						PreTrainedTokenizer							,	__A  :						int = -1							,	__A  :						int = -1							,	__A  :						bool = False							,	__A  :						Optional[TensorType] = None							,	):
						snake_case__				:   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
							snake_case__,		snake_case__				:   Union[str, Any]     =						common_inputs["input_ids"].shape
							# Not using the same length for past_key_values
							snake_case__				:   Any     =						seqlen + 2
							snake_case__,		snake_case__				:   Union[str, Any]     =						self.num_layers
							snake_case__,		snake_case__				:   List[str]     =						self.num_attention_heads
							snake_case__				:   List[Any]     =						(
							    batch,
							    num_encoder_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)

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



					def  _lowercase  (      self  :						str							,	__A  :						PreTrainedTokenizer							,	__A  :						int = -1							,	__A  :						int = -1							,	__A  :						bool = False							,	__A  :						Optional[TensorType] = None							,	):
						# 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
						snake_case__				:   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
						snake_case__				:   int     =						tokenizer.num_special_tokens_to_add(__A )
						snake_case__				:   Tuple     =						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
						snake_case__				:   List[str]     =						[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
						snake_case__				:   Union[str, Any]     =						dict(tokenizer(__A							,	return_tensors=__A ) )
						return common_inputs



					def  _lowercase  (      self  :						Tuple							,	__A  :						PreTrainedTokenizer							,	__A  :						int = -1							,	__A  :						int = -1							,	__A  :						bool = False							,	__A  :						Optional[TensorType] = None							,	):
						if self.task in ["default", "seq2seq-lm"]:
							snake_case__				:   Dict     =						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":
							snake_case__				:   Optional[Any]     =						self._generate_dummy_inputs_for_causal_lm(
							    __A							,	batch_size=__A							,	seq_length=__A							,	is_pair=__A							,	framework=__A )
						else:
							snake_case__				:   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  :						int							,	__A  :						Dict							,	__A  :						Dict							,	__A  :						Any							,	__A  :						Optional[int] ):
						if self.task in ["default", "seq2seq-lm"]:
							snake_case__				:   Union[str, Any]     =						super()._flatten_past_key_values_(__A							,	__A							,	__A							,	__A )
						else:
							snake_case__				:   Union[str, Any]     =						super(__A							,	self )._flatten_past_key_values_(
							    __A							,	__A							,	__A							,	__A )











from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



