






import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    XLMForMultipleChoice,
	    XLMForQuestionAnswering,
	    XLMForQuestionAnsweringSimple,
	    XLMForSequenceClassification,
	    XLMForTokenClassification,
	    XLMModel,
	    XLMWithLMHeadModel,
	)
	from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST


class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=7		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase=False		,    __UpperCAmelCase=False		,    __UpperCAmelCase=False		,    __UpperCAmelCase=2		,    __UpperCAmelCase=99		,    __UpperCAmelCase=0		,    __UpperCAmelCase=32		,    __UpperCAmelCase=5		,    __UpperCAmelCase=4		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=512		,    __UpperCAmelCase=2		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=2		,    __UpperCAmelCase=4		,    __UpperCAmelCase="last"		,    __UpperCAmelCase=True		,    __UpperCAmelCase=None		,    __UpperCAmelCase=0		,    )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						seq_length
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_input_lengths
							lowerCAmelCase__       =						use_token_type_ids
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						gelu_activation
							lowerCAmelCase__       =						sinusoidal_embeddings
							lowerCAmelCase__       =						causal
							lowerCAmelCase__       =						asm
							lowerCAmelCase__       =						n_langs
							lowerCAmelCase__       =						vocab_size
							lowerCAmelCase__       =						n_special
							lowerCAmelCase__       =						hidden_size
							lowerCAmelCase__       =						num_hidden_layers
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						max_position_embeddings
							lowerCAmelCase__       =						type_sequence_label_size
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						num_choices
							lowerCAmelCase__       =						summary_type
							lowerCAmelCase__       =						use_proj
							lowerCAmelCase__       =						scope
							lowerCAmelCase__       =						bos_token_id

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size       )
							lowerCAmelCase__       =						random_attention_mask([self.batch_size, self.seq_length]       )

							lowerCAmelCase__       =						None
							if self.use_input_lengths:
													lowerCAmelCase__       =						(
													    ids_tensor([self.batch_size]		,    vocab_size=2       ) + self.seq_length - 2
													)  # small variation of seq_length

							lowerCAmelCase__       =						None
							if self.use_token_type_ids:
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length]		,    self.n_langs       )

							lowerCAmelCase__       =						None
							lowerCAmelCase__       =						None
							lowerCAmelCase__       =						None
							if self.use_labels:
													lowerCAmelCase__       =						ids_tensor([self.batch_size]		,    self.type_sequence_label_size       )
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels       )
													lowerCAmelCase__       =						ids_tensor([self.batch_size]		,    2       ).float()
													lowerCAmelCase__       =						ids_tensor([self.batch_size]		,    self.num_choices       )

							lowerCAmelCase__       =						self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							return XLMConfig(
							    vocab_size=self.vocab_size		,    n_special=self.n_special		,    emb_dim=self.hidden_size		,    n_layers=self.num_hidden_layers		,    n_heads=self.num_attention_heads		,    dropout=self.hidden_dropout_prob		,    attention_dropout=self.attention_probs_dropout_prob		,    gelu_activation=self.gelu_activation		,    sinusoidal_embeddings=self.sinusoidal_embeddings		,    asm=self.asm		,    causal=self.causal		,    n_langs=self.n_langs		,    max_position_embeddings=self.max_position_embeddings		,    initializer_range=self.initializer_range		,    summary_type=self.summary_type		,    use_proj=self.use_proj		,    num_labels=self.num_labels		,    bos_token_id=self.bos_token_id		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMModel(config=__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__lowerCamelCase		,    lengths=__lowerCamelCase		,    langs=__lowerCamelCase       )
							lowerCAmelCase__       =						model(__lowerCamelCase		,    langs=__lowerCamelCase       )
							lowerCAmelCase__       =						model(__lowerCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMWithLMHeadModel(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							lowerCAmelCase__       =						model(__lowerCamelCase		,    token_type_ids=__lowerCamelCase		,    labels=__lowerCamelCase       )
							self.parent.assertEqual(result.loss.shape		,    ()       )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMForQuestionAnsweringSimple(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							lowerCAmelCase__       =						model(__lowerCamelCase       )

							lowerCAmelCase__       =						model(__lowerCamelCase		,    start_positions=__lowerCamelCase		,    end_positions=__lowerCamelCase       )
							lowerCAmelCase__       =						outputs
							self.parent.assertEqual(result.start_logits.shape		,    (self.batch_size, self.seq_length)       )
							self.parent.assertEqual(result.end_logits.shape		,    (self.batch_size, self.seq_length)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMForQuestionAnswering(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							lowerCAmelCase__       =						model(__lowerCamelCase       )

							lowerCAmelCase__       =						model(
							    __lowerCamelCase		,    start_positions=__lowerCamelCase		,    end_positions=__lowerCamelCase		,    cls_index=__lowerCamelCase		,    is_impossible=__lowerCamelCase		,    p_mask=__lowerCamelCase		,    )

							lowerCAmelCase__       =						model(
							    __lowerCamelCase		,    start_positions=__lowerCamelCase		,    end_positions=__lowerCamelCase		,    cls_index=__lowerCamelCase		,    is_impossible=__lowerCamelCase		,    )

							(lowerCAmelCase__	)       =						result_with_labels.to_tuple()

							lowerCAmelCase__       =						model(__lowerCamelCase		,    start_positions=__lowerCamelCase		,    end_positions=__lowerCamelCase       )

							(lowerCAmelCase__	)       =						result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape		,    ()       )
							self.parent.assertEqual(result.start_top_log_probs.shape		,    (self.batch_size, model.config.start_n_top)       )
							self.parent.assertEqual(result.start_top_index.shape		,    (self.batch_size, model.config.start_n_top)       )
							self.parent.assertEqual(
							    result.end_top_log_probs.shape		,    (self.batch_size, model.config.start_n_top * model.config.end_n_top)       )
							self.parent.assertEqual(
							    result.end_top_index.shape		,    (self.batch_size, model.config.start_n_top * model.config.end_n_top)       )
							self.parent.assertEqual(result.cls_logits.shape		,    (self.batch_size,)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMForSequenceClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							lowerCAmelCase__       =						model(__lowerCamelCase       )
							lowerCAmelCase__       =						model(__lowerCamelCase		,    labels=__lowerCamelCase       )
							self.parent.assertEqual(result.loss.shape		,    ()       )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						XLMForTokenClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							lowerCAmelCase__       =						model(__lowerCamelCase		,    attention_mask=__lowerCamelCase		,    labels=__lowerCamelCase       )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_choices
							lowerCAmelCase__       =						XLMForMultipleChoice(config=__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							lowerCAmelCase__       =						input_ids.unsqueeze(1       ).expand(-1		,    self.num_choices		,    -1       ).contiguous()
							lowerCAmelCase__       =						token_type_ids.unsqueeze(1       ).expand(-1		,    self.num_choices		,    -1       ).contiguous()
							lowerCAmelCase__       =						input_mask.unsqueeze(1       ).expand(-1		,    self.num_choices		,    -1       ).contiguous()
							lowerCAmelCase__       =						model(
							    __lowerCamelCase		,    attention_mask=__lowerCamelCase		,    token_type_ids=__lowerCamelCase		,    labels=__lowerCamelCase		,    )
							self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)       )


	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							(
							    lowerCAmelCase__	
							)       =						config_and_inputs
							lowerCAmelCase__       =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
							return config, inputs_dict





@require_torch
class      lowercase__     (			lowerCamelCase__,					lowerCamelCase__,					lowerCamelCase__,					unittest.TestCase	):
	a_         =(
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	a_         =(
	    {
	        """feature-extraction""": XLMModel,
	        """fill-mask""": XLMWithLMHeadModel,
	        """question-answering""": XLMForQuestionAnsweringSimple,
	        """text-classification""": XLMForSequenceClassification,
	        """text-generation""": XLMWithLMHeadModel,
	        """token-classification""": XLMForTokenClassification,
	        """zero-shot""": XLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("Fast"       )
							):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

							return False

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=False       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						super()._prepare_for_class(__lowerCamelCase		,    __lowerCamelCase		,    return_labels=__lowerCamelCase       )

							if return_labels:
													if model_class.__name__ == "XLMForQuestionAnswering":
																			lowerCAmelCase__       =						torch.zeros(
																			    self.model_tester.batch_size		,    dtype=torch.long		,    device=__lowerCamelCase       )
																			lowerCAmelCase__       =						torch.zeros(
																			    self.model_tester.batch_size		,    dtype=torch.long		,    device=__lowerCamelCase       )

							return inputs_dict

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMModelTester(self       )
							lowerCAmelCase__       =						ConfigTester(self		,    config_class=__lowerCamelCase		,    emb_dim=37       )

	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_simple_qa(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_token_classif(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*__lowerCamelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=False		,    __UpperCAmelCase=1       )->					Dict:







							'''simple docstring'''





							self.assertIsInstance(__lowerCamelCase		,    __lowerCamelCase       )
							self.assertListEqual(
							    [isinstance(__lowerCamelCase		,    __lowerCamelCase       ) for iter_attentions in attentions]		,    [True] * len(__lowerCamelCase       )       )
							self.assertEqual(len(__lowerCamelCase       )		,    (max_length - min_length) * num_beam_groups       )

							for idx, iter_attentions in enumerate(__lowerCamelCase       ):
													# adds PAD dummy token
													lowerCAmelCase__       =						min_length + idx + 1
													lowerCAmelCase__       =						min_length + idx + 1

													lowerCAmelCase__       =						(
													    batch_size * num_beam_groups,
													    config.num_attention_heads,
													    tgt_len,
													    src_len,
													)
													# check attn size
													self.assertListEqual(
													    [layer_attention.shape for layer_attention in iter_attentions]		,    [expected_shape] * len(__lowerCamelCase       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=False		,    __UpperCAmelCase=1       )->					Union[str, Any]:







							'''simple docstring'''





							self.assertIsInstance(__lowerCamelCase		,    __lowerCamelCase       )
							self.assertListEqual(
							    [isinstance(__lowerCamelCase		,    __lowerCamelCase       ) for iter_hidden_states in hidden_states]		,    [True] * len(__lowerCamelCase       )		,    )
							self.assertEqual(len(__lowerCamelCase       )		,    (max_length - min_length) * num_beam_groups       )

							for idx, iter_hidden_states in enumerate(__lowerCamelCase       ):
													# adds PAD dummy token
													lowerCAmelCase__       =						min_length + idx + 1
													lowerCAmelCase__       =						(batch_size * num_beam_groups, seq_len, config.hidden_size)
													# check hidden size
													self.assertListEqual(
													    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]		,    [expected_shape] * len(__lowerCamelCase       )		,    )
							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						XLMModel.from_pretrained(__lowerCamelCase       )
													self.assertIsNotNone(__lowerCamelCase       )





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"       )
							model.to(__lowerCamelCase       )
							lowerCAmelCase__       =						torch.tensor([[14, 447]]		,    dtype=torch.long		,    device=__lowerCamelCase       )  # the president
							lowerCAmelCase__       =						[
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							lowerCAmelCase__       =						model.generate(__lowerCamelCase		,    do_sample=__lowerCamelCase       )
							self.assertListEqual(output_ids[0].cpu().numpy().tolist()		,    __lowerCamelCase       )














import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Tuple=0.999    ,   UpperCamelCase_					:				Union[str, Any]="cosine"    ,   ) ->   str:
						"""simple docstring"""







						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCamelCase_					:				List[Any]   ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_					:				int   ):
																		return math.exp(t * -12.0   )

						else:
												raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

						lowerCAmelCase__       =						[]
						for i in range(UpperCamelCase_   ):
												lowerCAmelCase__       =						i / num_diffusion_timesteps
												lowerCAmelCase__       =						(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCamelCase_   ) / alpha_bar_fn(UpperCamelCase_   )    ,   UpperCamelCase_   )   )
						return torch.tensor(UpperCamelCase_    ,   dtype=torch.floataa   )





class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):
	a_         =[e.name for e in KarrasDiffusionSchedulers]
	a_         =2

	@register_to_config
	def __init__(							self		,    __UpperCAmelCase = 1000		,    __UpperCAmelCase = 0.00_085		,    __UpperCAmelCase = 0.012		,    __UpperCAmelCase = "linear"		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "epsilon"		,    __UpperCAmelCase = "linspace"		,    __UpperCAmelCase = 0		,    )->					int:







							'''simple docstring'''





							if trained_betas is not None:
													lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    dtype=torch.floataa       )
							elif beta_schedule == "linear":
													lowerCAmelCase__       =						torch.linspace(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    dtype=torch.floataa       )
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowerCAmelCase__       =						(
													    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    __UpperCAmelCase		,    dtype=torch.floataa       ) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowerCAmelCase__       =						betas_for_alpha_bar(__UpperCAmelCase       )
							else:
													raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"       )

							lowerCAmelCase__       =						1.0 - self.betas
							lowerCAmelCase__       =						torch.cumprod(self.alphas		,    dim=0       )

							#  set all values
							self.set_timesteps(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					str:







							'''simple docstring'''





							if schedule_timesteps is None:
													lowerCAmelCase__       =						self.timesteps

							lowerCAmelCase__       =						(schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter       ) == 0:
													lowerCAmelCase__       =						1 if len(__UpperCAmelCase       ) > 1 else 0
							else:
													lowerCAmelCase__       =						timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase       ) else timestep
													lowerCAmelCase__       =						self._index_counter[timestep_int]

							return indices[pos].item()

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					torch.FloatTensor:







							'''simple docstring'''





							lowerCAmelCase__       =						self.index_for_timestep(__UpperCAmelCase       )

							if self.state_in_first_order:
													lowerCAmelCase__       =						self.sigmas[step_index]
							else:
													lowerCAmelCase__       =						self.sigmas_interpol[step_index]

							lowerCAmelCase__       =						sample / ((sigma**2 + 1) ** 0.5)
							return sample

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						num_inference_steps

							lowerCAmelCase__       =						num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													lowerCAmelCase__       =						np.linspace(0		,    num_train_timesteps - 1		,    __UpperCAmelCase		,    dtype=__UpperCAmelCase       )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													lowerCAmelCase__       =						num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													lowerCAmelCase__       =						(np.arange(0		,    __UpperCAmelCase       ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase       )
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													lowerCAmelCase__       =						num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													lowerCAmelCase__       =						(np.arange(__UpperCAmelCase		,    0		,    -step_ratio       )).round().copy().astype(__UpperCAmelCase       )
													timesteps -= 1
							else:
													raise ValueError(
													    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."       )

							lowerCAmelCase__       =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
							lowerCAmelCase__       =						torch.from_numpy(np.log(__UpperCAmelCase       )       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						np.interp(__UpperCAmelCase		,    np.arange(0		,    len(__UpperCAmelCase       )       )		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
							lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(device=__UpperCAmelCase       )

							# interpolate sigmas
							lowerCAmelCase__       =						sigmas.log().lerp(sigmas.roll(1       ).log()		,    0.5       ).exp()

							lowerCAmelCase__       =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
							lowerCAmelCase__       =						torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

							if str(__UpperCAmelCase       ).startswith("mps"       ):
													# mps does not support float64
													lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(__UpperCAmelCase		,    dtype=torch.floataa       )
							else:
													lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(__UpperCAmelCase       )

							# interpolate timesteps
							lowerCAmelCase__       =						self.sigma_to_t(__UpperCAmelCase       ).to(__UpperCAmelCase		,    dtype=timesteps.dtype       )
							lowerCAmelCase__       =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,    dim=-1       ).flatten()

							lowerCAmelCase__       =						torch.cat([timesteps[:1], interleaved_timesteps]       )

							lowerCAmelCase__       =						None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							lowerCAmelCase__       =						defaultdict(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						sigma.log()

							# get distribution
							lowerCAmelCase__       =						log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							lowerCAmelCase__       =						dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
							lowerCAmelCase__       =						low_idx + 1

							lowerCAmelCase__       =						self.log_sigmas[low_idx]
							lowerCAmelCase__       =						self.log_sigmas[high_idx]

							# interpolate sigmas
							lowerCAmelCase__       =						(low - log_sigma) / (low - high)
							lowerCAmelCase__       =						w.clamp(0		,    1       )

							# transform interpolation to time range
							lowerCAmelCase__       =						(1 - w) * low_idx + w * high_idx
							lowerCAmelCase__       =						t.view(sigma.shape       )
							return t

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							return self.sample is None

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = True		,    )->					Union[SchedulerOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.index_for_timestep(__UpperCAmelCase       )

							# advance index counter by 1
							lowerCAmelCase__       =						timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase       ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													lowerCAmelCase__       =						self.sigmas[step_index]
													lowerCAmelCase__       =						self.sigmas_interpol[step_index + 1]
													lowerCAmelCase__       =						self.sigmas[step_index + 1]
							else:
													# 2nd order / KDPM2's method
													lowerCAmelCase__       =						self.sigmas[step_index - 1]
													lowerCAmelCase__       =						self.sigmas_interpol[step_index]
													lowerCAmelCase__       =						self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							lowerCAmelCase__       =						0
							lowerCAmelCase__       =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													lowerCAmelCase__       =						sigma_hat if self.state_in_first_order else sigma_interpol
													lowerCAmelCase__       =						sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													lowerCAmelCase__       =						sigma_hat if self.state_in_first_order else sigma_interpol
													lowerCAmelCase__       =						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
							elif self.config.prediction_type == "sample":
													raise NotImplementedError("prediction_type not implemented yet: sample"       )
							else:
													raise ValueError(
													    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"       )

							if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													lowerCAmelCase__       =						(sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													lowerCAmelCase__       =						sigma_interpol - sigma_hat

													# store for 2nd order step
													lowerCAmelCase__       =						sample
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													lowerCAmelCase__       =						(sample - pred_original_sample) / sigma_interpol

													# 3. delta timestep
													lowerCAmelCase__       =						sigma_next - sigma_hat

													lowerCAmelCase__       =						self.sample
													lowerCAmelCase__       =						None

							lowerCAmelCase__       =						sample + derivative * dt

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					torch.FloatTensor:







							'''simple docstring'''





							lowerCAmelCase__       =						self.sigmas.to(device=original_samples.device		,    dtype=original_samples.dtype       )
							if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase       ):
													# mps does not support float64
													lowerCAmelCase__       =						self.timesteps.to(original_samples.device		,    dtype=torch.floataa       )
													lowerCAmelCase__       =						timesteps.to(original_samples.device		,    dtype=torch.floataa       )
							else:
													lowerCAmelCase__       =						self.timesteps.to(original_samples.device       )
													lowerCAmelCase__       =						timesteps.to(original_samples.device       )

							lowerCAmelCase__       =						[self.index_for_timestep(__UpperCAmelCase		,    __UpperCAmelCase       ) for t in timesteps]

							lowerCAmelCase__       =						sigmas[step_indices].flatten()
							while len(sigma.shape       ) < len(original_samples.shape       ):
													lowerCAmelCase__       =						sigma.unsqueeze(-1       )

							lowerCAmelCase__       =						original_samples + noise * sigma
							return noisy_samples


	def __len__(							self       )->					List[Any]:







							'''simple docstring'''





							return self.config.num_train_timesteps






