




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					_lowerCamelCase		  =			(ScoreSdeVeScheduler,)
					_lowerCamelCase		  =			()

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							4
						lowerCamelCase_							:		List[str]							       =							3
						lowerCamelCase_							:		Any							       =							8
						lowerCamelCase_							:		str							       =							8

						lowerCamelCase_							:		int							       =							torch.rand((batch_size, num_channels, height, width) )

						return sample

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							4
						lowerCamelCase_							:		Tuple							       =							3
						lowerCamelCase_							:		str							       =							8
						lowerCamelCase_							:		int							       =							8

						lowerCamelCase_							:		Dict							       =							batch_size * num_channels * height * width
						lowerCamelCase_							:		Tuple							       =							torch.arange(__lowercase )
						lowerCamelCase_							:		int							       =							sample.reshape(__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							sample / num_elems
						lowerCamelCase_							:		Any							       =							sample.permute(3			,	0			,	1			,	2 )

						return sample

					def      lowercase_							(    self ):







						'''simple docstring'''




						def model(__lowercase			,	__lowercase			,	*__lowercase ):
							return sample * t / (t + 1)

						return model

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							{
						    'num_train_timesteps': 2_000,
						    'snr': 0.15,
						    'sigma_min': 0.01,
						    'sigma_max': 1_348,
						    'sampling_eps': 1E-5,
						}

						config.update(**__lowercase )
						return config

					def      lowercase_							(    self			,	__lowercase=0			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							dict(self.forward_default_kwargs )

						for scheduler_class in self.scheduler_classes:
							lowerCamelCase_							:		Any							       =							self.dummy_sample
							lowerCamelCase_							:		List[str]							       =							0.1 * sample

							lowerCamelCase_							:		int							       =							self.get_scheduler_config(**__lowercase )
							lowerCamelCase_							:		List[str]							       =							scheduler_class(**__lowercase )

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__lowercase )
								lowerCamelCase_							:		List[str]							       =							scheduler_class.from_pretrained(__lowercase )

							lowerCamelCase_							:		List[str]							       =							scheduler.step_pred(
							    __lowercase			,	__lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample
							lowerCamelCase_							:		Any							       =							new_scheduler.step_pred(
							    __lowercase			,	__lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

							lowerCamelCase_							:		int							       =							scheduler.step_correct(__lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample
							lowerCamelCase_							:		Union[str, Any]							       =							new_scheduler.step_correct(
							    __lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler correction are not identical"

					def      lowercase_							(    self			,	__lowercase=0			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							dict(self.forward_default_kwargs )
						kwargs.update(__lowercase )

						for scheduler_class in self.scheduler_classes:
							lowerCamelCase_							:		Dict							       =							self.dummy_sample
							lowerCamelCase_							:		Union[str, Any]							       =							0.1 * sample

							lowerCamelCase_							:		str							       =							self.get_scheduler_config()
							lowerCamelCase_							:		int							       =							scheduler_class(**__lowercase )

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__lowercase )
								lowerCamelCase_							:		Optional[Any]							       =							scheduler_class.from_pretrained(__lowercase )

							lowerCamelCase_							:		Optional[Any]							       =							scheduler.step_pred(
							    __lowercase			,	__lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample
							lowerCamelCase_							:		Optional[int]							       =							new_scheduler.step_pred(
							    __lowercase			,	__lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

							lowerCamelCase_							:		List[str]							       =							scheduler.step_correct(__lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample
							lowerCamelCase_							:		str							       =							new_scheduler.step_correct(
							    __lowercase			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler correction are not identical"

					def      lowercase_							(    self ):







						'''simple docstring'''




						for timesteps in [10, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]			,	[1, 100, 1_000] ):
							self.check_over_configs(sigma_min=__lowercase			,	sigma_max=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for t in [0.1, 0.5, 0.75]:
							self.check_over_forward(time_step=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							dict(self.forward_default_kwargs )

						lowerCamelCase_							:		Any							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		Optional[Any]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		List[Any]							       =							scheduler_class(**__lowercase )

						lowerCamelCase_							:		str							       =							3

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample_deter

						scheduler.set_sigmas(__lowercase )
						scheduler.set_timesteps(__lowercase )
						lowerCamelCase_							:		Dict							       =							torch.manual_seed(0 )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		int							       =							scheduler.sigmas[i]

							for _ in range(scheduler.config.correct_steps ):
								with torch.no_grad():
									lowerCamelCase_							:		Any							       =							model(__lowercase			,	__lowercase )
								lowerCamelCase_							:		Optional[Any]							       =							scheduler.step_correct(__lowercase			,	__lowercase			,	generator=__lowercase			,	**__lowercase ).prev_sample

							with torch.no_grad():
								lowerCamelCase_							:		Dict							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Union[str, Any]							       =							scheduler.step_pred(__lowercase			,	__lowercase			,	__lowercase			,	generator=__lowercase			,	**__lowercase )
							lowerCamelCase_       ,      lowerCamelCase_							:		Any							       =							output.prev_sample, output.prev_sample_mean

						lowerCamelCase_							:		Dict							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Tuple							       =							torch.mean(torch.abs(__lowercase ) )

						assert np.isclose(result_sum.item()			,	1_43_72_75_85_28.0 )
						assert np.isclose(result_mean.item()			,	18_71_45_30.0 )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							dict(self.forward_default_kwargs )

						lowerCamelCase_							:		List[str]							       =							kwargs.pop('num_inference_steps'			,	__lowercase )

						for scheduler_class in self.scheduler_classes:
							lowerCamelCase_							:		Optional[int]							       =							self.get_scheduler_config()
							lowerCamelCase_							:		Tuple							       =							scheduler_class(**__lowercase )

							lowerCamelCase_							:		List[str]							       =							self.dummy_sample
							lowerCamelCase_							:		Dict							       =							0.1 * sample

							if num_inference_steps is not None and hasattr(__lowercase			,	'set_timesteps' ):
								scheduler.set_timesteps(__lowercase )
							elif num_inference_steps is not None and not hasattr(__lowercase			,	'set_timesteps' ):
								lowerCamelCase_							:		str							       =							num_inference_steps

							lowerCamelCase_							:		Any							       =							scheduler.step_pred(__lowercase			,	0			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample
							lowerCamelCase_							:		List[str]							       =							scheduler.step_pred(__lowercase			,	1			,	__lowercase			,	generator=torch.manual_seed(0 )			,	**__lowercase ).prev_sample

							self.assertEqual(output_a.shape			,	sample.shape )
							self.assertEqual(output_a.shape			,	output_a.shape )






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			"""m2m_100"""
					_lowerCamelCase		  =			["""past_key_values"""]
					_lowerCamelCase		  =			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(    self			,	__lowercase=128_112			,	__lowercase=1_024			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=0.05			,	__lowercase=0.05			,	__lowercase=True			,	__lowercase=True			,	__lowercase="relu"			,	__lowercase=1_024			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=0.0			,	__lowercase=0.02			,	__lowercase=2			,	__lowercase=True			,	__lowercase=1			,	__lowercase=0			,	__lowercase=2			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							max_position_embeddings
						lowerCamelCase_							:		Tuple							       =							d_model
						lowerCamelCase_							:		Optional[int]							       =							encoder_ffn_dim
						lowerCamelCase_							:		Union[str, Any]							       =							encoder_layers
						lowerCamelCase_							:		Dict							       =							encoder_attention_heads
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_ffn_dim
						lowerCamelCase_							:		List[str]							       =							decoder_layers
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_attention_heads
						lowerCamelCase_							:		Optional[int]							       =							dropout
						lowerCamelCase_							:		str							       =							attention_dropout
						lowerCamelCase_							:		Optional[Any]							       =							activation_dropout
						lowerCamelCase_							:		str							       =							activation_function
						lowerCamelCase_							:		int							       =							init_std
						lowerCamelCase_							:		Optional[int]							       =							encoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							decoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							use_cache
						lowerCamelCase_							:		Any							       =							encoder_layers
						lowerCamelCase_							:		Any							       =							scale_embedding  # scale factor will be sqrt(d_model) if True

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


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ] )

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

						if self.use_past:
							self.fill_with_past_key_values_(__lowercase			,	direction='inputs' )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							compute_effective_axis_dimension(
						    __lowercase			,	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_							:		str							       =							tokenizer.num_special_tokens_to_add(__lowercase )
						lowerCamelCase_							:		Dict							       =							compute_effective_axis_dimension(
						    __lowercase			,	fixed_dimension=OnnxConfig.default_fixed_sequence			,	num_token_to_add=__lowercase )

						# Generate dummy inputs according to compute batch and sequence
						lowerCamelCase_							:		Optional[int]							       =							[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
						lowerCamelCase_							:		Optional[Any]							       =							dict(tokenizer(__lowercase			,	return_tensors=__lowercase ) )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )

						# Generate decoder inputs
						lowerCamelCase_							:		Dict							       =							seq_length if not self.use_past else 1
						lowerCamelCase_							:		List[str]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )
						lowerCamelCase_							:		Any							       =							{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						lowerCamelCase_							:		str							       =							dict(**__lowercase			,	**__lowercase )

						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_							:		int							       =							common_inputs['input_ids'].shape
							lowerCamelCase_							:		List[Any]							       =							common_inputs['decoder_input_ids'].shape[1]
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_attention_heads
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							lowerCamelCase_							:		Tuple							       =							decoder_seq_length + 3
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							lowerCamelCase_							:		List[Any]							       =							torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(__lowercase			,	__lowercase )]			,	dim=1 )

							lowerCamelCase_							:		Tuple							       =							[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_layers
							lowerCamelCase_							:		Optional[int]							       =							min(__lowercase			,	__lowercase )
							lowerCamelCase_							:		int							       =							max(__lowercase			,	__lowercase ) - min_num_layers
							lowerCamelCase_							:		Union[str, Any]							       =							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

							for _ in range(__lowercase ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								    ) )
							# TODO: test this.
							lowerCamelCase_							:		Optional[int]							       =							encoder_shape if remaining_side_name == 'encoder' else decoder_shape
							for _ in range(__lowercase			,	__lowercase ):
								common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
						return common_inputs





					_lowerCamelCase		  =			_generate_dummy_inputs_for_default_and_seqaseq_lm
