
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	:      Any			   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:      str			   =   {
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}


class 						__magic_name__      (  __SCREAMING_SNAKE_CASE			):
		_lowerCAmelCase						=		"bigbird_pegasus"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "num_attention_heads": "encoder_attention_heads",
		    "hidden_size": "d_model",
		    "attention_probs_dropout_prob": "attention_dropout",
		}





		def __init__(							self			:		Optional[int]  ,     lowerCamelCase__			:		Optional[Any]=9_6_1_0_3  ,     lowerCamelCase__			:		Tuple=4_0_9_6  ,     lowerCamelCase__			:		Tuple=1_6  ,     lowerCamelCase__			:		Any=4_0_9_6  ,     lowerCamelCase__			:		Any=1_6  ,     lowerCamelCase__			:		Optional[Any]=1_6  ,     lowerCamelCase__			:		Any=4_0_9_6  ,     lowerCamelCase__			:		Optional[int]=1_6  ,     lowerCamelCase__			:		int=0.0  ,     lowerCamelCase__			:		Any=0.0  ,     lowerCamelCase__			:		Tuple=True  ,     lowerCamelCase__			:		Optional[int]=True  ,     lowerCamelCase__			:		Tuple="gelu_new"  ,     lowerCamelCase__			:		Any=1_0_2_4  ,     lowerCamelCase__			:		Optional[int]=0.1  ,     lowerCamelCase__			:		List[str]=0.0  ,     lowerCamelCase__			:		Union[str, Any]=0.0  ,     lowerCamelCase__			:		int=0.0_2  ,     lowerCamelCase__			:		Union[str, Any]=2  ,     lowerCamelCase__			:		Tuple=0.0  ,     lowerCamelCase__			:		str=True  ,     lowerCamelCase__			:		Union[str, Any]=0  ,     lowerCamelCase__			:		Optional[int]=2  ,     lowerCamelCase__			:		Optional[int]=1  ,     lowerCamelCase__			:		Any="block_sparse"  ,     lowerCamelCase__			:		List[str]=6_4  ,     lowerCamelCase__			:		Any=3  ,     lowerCamelCase__			:		Union[str, Any]=False  ,     **lowerCamelCase__			:		Union[str, Any]  ,     ):
							lowerCAmelCase						:    Any     		=   vocab_size
							lowerCAmelCase						:    Tuple     		=   max_position_embeddings
							lowerCAmelCase						:    Tuple     		=   d_model
							lowerCAmelCase						:    Optional[int]     		=   encoder_ffn_dim
							lowerCAmelCase						:    Union[str, Any]     		=   encoder_layers
							lowerCAmelCase						:    Tuple     		=   encoder_attention_heads
							lowerCAmelCase						:    str     		=   decoder_ffn_dim
							lowerCAmelCase						:    List[Any]     		=   decoder_layers
							lowerCAmelCase						:    Union[str, Any]     		=   decoder_attention_heads
							lowerCAmelCase						:    Tuple     		=   dropout
							lowerCAmelCase						:    Tuple     		=   attention_dropout
							lowerCAmelCase						:    Optional[Any]     		=   activation_dropout
							lowerCAmelCase						:    Optional[int]     		=   activation_function
							lowerCAmelCase						:    Tuple     		=   init_std
							lowerCAmelCase						:    Dict     		=   encoder_layerdrop
							lowerCAmelCase						:    Union[str, Any]     		=   decoder_layerdrop
							lowerCAmelCase						:    int     		=   classifier_dropout
							lowerCAmelCase						:    List[str]     		=   use_cache
							lowerCAmelCase						:    Tuple     		=   encoder_layers
							lowerCAmelCase						:    str     		=   scale_embedding  # scale factor will be sqrt(d_model) if True

							# extra config
							lowerCAmelCase						:    List[str]     		=   attention_type
							lowerCAmelCase						:    Dict     		=   block_size
							lowerCAmelCase						:    Tuple     		=   num_random_blocks
							lowerCAmelCase						:    List[Any]     		=   use_bias

							super().__init__(
							    pad_token_id=_snake_case  ,     bos_token_id=_snake_case  ,     eos_token_id=_snake_case  ,     is_encoder_decoder=_snake_case  ,     decoder_start_token_id=_snake_case  ,     **_snake_case  ,     )



class 						__magic_name__      (  __SCREAMING_SNAKE_CASE			):





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

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





		def 					_A							(							self			:		Tuple  ,     lowerCamelCase__			:		Dict  ,     lowerCamelCase__			:		Optional[Any] = -1  ,     lowerCamelCase__			:		Tuple = -1  ,     lowerCamelCase__			:		List[Any] = False  ,     lowerCamelCase__			:		List[Any] = None  ,     ):
							lowerCAmelCase						:    Union[str, Any]     		=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _snake_case  ,     _snake_case  ,     _snake_case  ,     _snake_case  ,     _snake_case )

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

												lowerCAmelCase						:    Any     		=   torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,     _snake_case )]  ,     dim=1 )

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

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





		def 					_A							(							self			:		List[str]  ,     lowerCamelCase__			:		Tuple  ,     lowerCamelCase__			:		List[Any] = -1  ,     lowerCamelCase__			:		int = -1  ,     lowerCamelCase__			:		int = False  ,     lowerCamelCase__			:		Any = 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
							lowerCAmelCase						:    Dict     		=   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
							lowerCAmelCase						:    Any     		=   tokenizer.num_special_tokens_to_add(_snake_case )
							lowerCAmelCase						:    List[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
							lowerCAmelCase						:    int     		=   [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
							lowerCAmelCase						:    int     		=   dict(tokenizer(_snake_case  ,     return_tensors=_snake_case ) )
							return common_inputs





		def 					_A							(							self			:		List[str]  ,     lowerCamelCase__			:		Dict  ,     lowerCamelCase__			:		List[str] = -1  ,     lowerCamelCase__			:		List[str] = -1  ,     lowerCamelCase__			:		int = False  ,     lowerCamelCase__			:		List[str] = None  ,     ):
							if self.task in ["default", "seq2seq-lm"]:
												lowerCAmelCase						:    int     		=   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":
												lowerCAmelCase						:    Optional[int]     		=   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:
												lowerCAmelCase						:    str     		=   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 					_A							(							self			:		Optional[Any]  ,     lowerCamelCase__			:		Union[str, Any]  ,     lowerCamelCase__			:		Optional[int]  ,     lowerCamelCase__			:		Union[str, Any]  ,     lowerCamelCase__			:		List[Any] ):
							if self.task in ["default", "seq2seq-lm"]:
												lowerCAmelCase						:    int     		=   super()._flatten_past_key_values_(_snake_case  ,     _snake_case  ,     _snake_case  ,     _snake_case )
							else:
												lowerCAmelCase						:    Tuple     		=   super(_snake_case  ,     self )._flatten_past_key_values_(
												    _snake_case  ,     _snake_case  ,     _snake_case  ,     _snake_case )



'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






			def 						UpperCamelCase__				(   self			):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




