from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class    lowercase_    :
							_lowerCamelCase						=		42

							# setable values
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None

							# running values
							_lowerCamelCase						=		None



							@classmethod
							def        UpperCamelCase       (						cls      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													return cls(common=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				sigmas=lowercase_				)




@dataclass
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		42




class    lowercase_    (     __snake_case						, __snake_case		):
							_lowerCamelCase						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase						=		42



							@property
							def        UpperCamelCase       (						self				):
													return True



							@register_to_config
							def __init__(						self      ,				lowercase_ = 1_000      ,				lowercase_ = 0.0_001      ,				lowercase_ = 0.02      ,				lowercase_ = "linear"      ,				lowercase_ = None      ,				lowercase_ = "epsilon"      ,				lowercase_ = jnp.floataa      ,				):
													_snake_case						:     List[Any]				=    dtype



							def        UpperCamelCase       (						self      ,				lowercase_ = None				):
													if common is None:
																			_snake_case						:     List[str]				=    CommonSchedulerState.create(self				)

													_snake_case						:     int				=    jnp.arange(0      ,				self.config.num_train_timesteps				).round()[::-1]
													_snake_case						:     Union[str, Any]				=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													_snake_case						:     int				=    sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				sigmas=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													((_snake_case)				,)						:     str				=    jnp.where(state.timesteps == timestep      ,				size=1				)
													_snake_case						:     Optional[Any]				=    step_index[0]

													_snake_case						:     Tuple				=    state.sigmas[step_index]
													_snake_case						:     Union[str, Any]				=    sample / ((sigma**2 + 1) ** 0.5)
													return sample



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):

													def lms_derivative(lowercase_				):
																			_snake_case						:     Union[str, Any]				=    1.0
																			for k in range(lowercase_				):
																									if current_order == k:
																															continue
																									prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																			return prod

													_snake_case						:     Tuple				=    integrate.quad(lowercase_      ,				state.sigmas[t]      ,				state.sigmas[t + 1]      ,				epsrel=1e-4				)[0]

													return integrated_coeff



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = ()				):
													_snake_case						:     Tuple				=    jnp.linspace(self.config.num_train_timesteps - 1      ,				0      ,				lowercase_      ,				dtype=self.dtype				)

													_snake_case						:     str				=    jnp.floor(lowercase_				).astype(jnp.intaa				)
													_snake_case						:     Any				=    jnp.ceil(lowercase_				).astype(jnp.intaa				)

													_snake_case						:     str				=    jnp.mod(lowercase_      ,				1.0				)

													_snake_case						:     int				=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													_snake_case						:     List[str]				=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													_snake_case						:     Union[str, Any]				=    jnp.concatenate([sigmas, jnp.array([0.0]      ,				dtype=self.dtype				)]				)

													_snake_case						:     str				=    timesteps.astype(jnp.intaa				)

													# initial running values
													_snake_case						:     List[str]				=    jnp.zeros((0,) + shape      ,				dtype=self.dtype				)

													return state.replace(
													    timesteps=lowercase_      ,				sigmas=lowercase_      ,				num_inference_steps=lowercase_      ,				derivatives=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_ = 4      ,				lowercase_ = True      ,				):
													if state.num_inference_steps is None:
																			raise ValueError(
																			    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"				)

													_snake_case						:     List[str]				=    state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			_snake_case						:     Any				=    sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			_snake_case						:     Dict				=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

													# 2. Convert to an ODE derivative
													_snake_case						:     Tuple				=    (sample - pred_original_sample) / sigma
													_snake_case						:     List[str]				=    state.replace(derivatives=jnp.append(state.derivatives      ,				lowercase_				)				)
													if len(state.derivatives				) > order:
																			_snake_case						:     Optional[Any]				=    state.replace(derivatives=jnp.delete(state.derivatives      ,				0				)				)

													# 3. Compute linear multistep coefficients
													_snake_case						:     Any				=    min(timestep + 1      ,				lowercase_				)
													_snake_case						:     Optional[Any]				=    [self.get_lms_coefficient(lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				) for curr_order in range(lowercase_				)]

													# 4. Compute previous sample based on the derivatives path
													_snake_case						:     Optional[int]				=    sample + sum(
													    coeff * derivative for coeff, derivative in zip(lowercase_      ,				reversed(state.derivatives				)				)				)

													if not return_dict:
																			return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=lowercase_      ,				state=lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Optional[int]				=    state.sigmas[timesteps].flatten()
													_snake_case						:     int				=    broadcast_to_shape_from_left(lowercase_      ,				noise.shape				)

													_snake_case						:     List[str]				=    original_samples + noise * sigma

													return noisy_samples



							def __len__(						self				):
													return self.config.num_train_timesteps




'''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


UpperCAmelCase			=    logging.get_logger(__name__)

UpperCAmelCase			=    {
    '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 __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Union[str, Any]           =		"bigbird_pegasus"
						UpperCAmelCase							:    Optional[Any]           =		["past_key_values"]
						UpperCAmelCase							:    Optional[Any]           =		{
						    "num_attention_heads": "encoder_attention_heads",
						    "hidden_size": "d_model",
						    "attention_probs_dropout_prob": "attention_dropout",
						}


						def __init__(					self	,       A_=9_6103	,       A_=4096	,       A_=16	,       A_=4096	,       A_=16	,       A_=16	,       A_=4096	,       A_=16	,       A_=0.0	,       A_=0.0	,       A_=True	,       A_=True	,       A_="gelu_new"	,       A_=1024	,       A_=0.1	,       A_=0.0	,       A_=0.0	,       A_=0.0_2	,       A_=2	,       A_=0.0	,       A_=True	,       A_=0	,       A_=2	,       A_=1	,       A_="block_sparse"	,       A_=64	,       A_=3	,       A_=False	,       **A_	,       )	->	List[str]:
							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

							# extra config
							lowerCAmelCase    =	attention_type
							lowerCAmelCase    =	block_size
							lowerCAmelCase    =	num_random_blocks
							lowerCAmelCase    =	use_bias

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







class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						@property
						def 				__snake_case    (					self      )	->	Mapping[str, Mapping[int, str]]:
							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 				__snake_case    (					self      )	->	Mapping[str, Mapping[int, str]]:
							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 				__snake_case    (					self	,       A_	,       A_ = -1	,       A_ = -1	,       A_ = False	,       A_ = None	,       )	->	Mapping[str, Any]:
							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 				__snake_case    (					self	,       A_	,       A_ = -1	,       A_ = -1	,       A_ = False	,       A_ = None	,       )	->	Mapping[str, Any]:
							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 				__snake_case    (					self	,       A_	,       A_ = -1	,       A_ = -1	,       A_ = False	,       A_ = 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
							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 				__snake_case    (					self	,       A_	,       A_ = -1	,       A_ = -1	,       A_ = False	,       A_ = None	,       )	->	Mapping[str, Any]:
							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 				__snake_case    (					self	,       A_	,       A_	,       A_	,       A_      )	->	Tuple:
							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_      )