





'''simple docstring'''






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


__UpperCamelCase					    =	1E-4

if is_torch_available():
	import torch

	from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
	from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder




@require_torch
class  _A :

		def __init__(   self					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[Any]=16  ,      __magic_name__					:   Optional[Any]=13  ,      __magic_name__					:   List[str]=7  ,      __magic_name__					:   Union[str, Any]=14  ,      __magic_name__					:   Any=10  ,      __magic_name__					:   Tuple=19  ,      __magic_name__					:   int=5  ,      __magic_name__					:   Any=4  ,      __magic_name__					:   Tuple=True  ,      __magic_name__					:   int=16  ,      __magic_name__					:   Any=2  ,      __magic_name__					:   List[str]=4  ,      __magic_name__					:   str=4  ,      __magic_name__					:   Any="gelu"  ,      __magic_name__					:   int=0.1  ,      __magic_name__					:   Tuple=0.1  ,      __magic_name__					:   Optional[int]=[1, 2, 3, 4, 5]  ,      __magic_name__					:   Union[str, Any]=25  ,      __magic_name__					:   Union[str, Any]=5  ,      )					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     d_model
					__snake_case					:      int							      =     parent
					__snake_case					:      str							      =     batch_size
					__snake_case					:      Optional[Any]							      =     prediction_length
					__snake_case					:      str							      =     context_length
					__snake_case					:      str							      =     cardinality
					__snake_case					:      List[Any]							      =     num_time_features
					__snake_case					:      Union[str, Any]							      =     lags_sequence
					__snake_case					:      str							      =     embedding_dimension
					__snake_case					:      Optional[int]							      =     is_training
					__snake_case					:      Tuple							      =     hidden_size
					__snake_case					:      Any							      =     num_hidden_layers
					__snake_case					:      Dict							      =     num_attention_heads
					__snake_case					:      Union[str, Any]							      =     intermediate_size
					__snake_case					:      Any							      =     hidden_act
					__snake_case					:      str							      =     hidden_dropout_prob
					__snake_case					:      Tuple							      =     attention_probs_dropout_prob

					__snake_case					:      str							      =     context_length
					__snake_case					:      int							      =     prediction_length + label_length
					__snake_case					:      Optional[Any]							      =     label_length

					__snake_case					:      List[str]							      =     moving_average
					__snake_case					:      str							      =     autocorrelation_factor

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


					"""simple docstring"""

					return AutoformerConfig(
					    d_model=self.d_model  ,      encoder_layers=self.num_hidden_layers  ,      decoder_layers=self.num_hidden_layers  ,      encoder_attention_heads=self.num_attention_heads  ,      decoder_attention_heads=self.num_attention_heads  ,      encoder_ffn_dim=self.intermediate_size  ,      decoder_ffn_dim=self.intermediate_size  ,      dropout=self.hidden_dropout_prob  ,      attention_dropout=self.attention_probs_dropout_prob  ,      prediction_length=self.prediction_length  ,      context_length=self.context_length  ,      label_length=self.label_length  ,      lags_sequence=self.lags_sequence  ,      num_time_features=self.num_time_features  ,      num_static_categorical_features=1  ,      cardinality=[self.cardinality]  ,      embedding_dimension=[self.embedding_dimension]  ,      moving_average=self.moving_average  ,      )

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     config.context_length + max(config.lags_sequence			)

					__snake_case					:      Any							      =     ids_tensor([self.batch_size, 1]  ,      config.cardinality[0]			)
					__snake_case					:      int							      =     floats_tensor([self.batch_size, _past_length, config.num_time_features]			)
					__snake_case					:      List[str]							      =     floats_tensor([self.batch_size, _past_length]			)
					__snake_case					:      List[Any]							      =     floats_tensor([self.batch_size, _past_length]			) > 0.5

					# decoder inputs
					__snake_case					:      List[Any]							      =     floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]			)
					__snake_case					:      Tuple							      =     floats_tensor([self.batch_size, config.prediction_length]			)

					__snake_case					:      Union[str, Any]							      =     {
					    """past_values""": past_values,
					    """static_categorical_features""": static_categorical_features,
					    """past_time_features""": past_time_features,
					    """past_observed_mask""": past_observed_mask,
					    """future_time_features""": future_time_features,
					    """future_values""": future_values,
					}
					return inputs_dict

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


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.get_config()
					__snake_case					:      Any							      =     self.prepare_autoformer_inputs_dict(__magic_name__			)
					return config, inputs_dict

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


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.prepare_config_and_inputs()
					return config, inputs_dict

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   int  ,      __magic_name__					:   List[str]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     AutoformerModel(config=__magic_name__			).to(__magic_name__			).eval()
					__snake_case					:      Tuple							      =     model(**__magic_name__			)

					__snake_case					:      Optional[int]							      =     outputs.encoder_last_hidden_state
					__snake_case					:      Any							      =     outputs.last_hidden_state

					with tempfile.TemporaryDirectory() as tmpdirname:
								__snake_case					:      List[str]							      =     model.get_encoder()
								encoder.save_pretrained(__magic_name__			)
								__snake_case					:      List[str]							      =     AutoformerEncoder.from_pretrained(__magic_name__			).to(__magic_name__			)

					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      List[str]							      =     model.create_network_inputs(**__magic_name__			)
					__snake_case      ,     __snake_case					:      List[str]							      =     model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]			)

					__snake_case					:      Union[str, Any]							      =     torch.cat(
					    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])  ,      dim=-1  ,      )
					__snake_case					:      Optional[int]							      =     encoder(inputs_embeds=__magic_name__			)[0]
					self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3			)

					__snake_case					:      str							      =     (
					    torch.mean(transformer_inputs[:, : config.context_length, ...]  ,      dim=1			)
					    .unsqueeze(1			)
					    .repeat(1  ,      config.prediction_length  ,      1			)
					)
					__snake_case					:      Any							      =     torch.zeros(
					    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]  ,      device=enc_input.device  ,      )

					__snake_case					:      Optional[Any]							      =     torch.cat(
					    (
					        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)  ,      dim=1			),
					        feature[:, config.context_length - config.label_length :, ...],
					    )  ,      dim=-1  ,      )
					__snake_case					:      Optional[int]							      =     torch.cat(
					    (
					        torch.cat((trend_input[:, -config.label_length :, ...], mean)  ,      dim=1			),
					        feature[:, config.context_length - config.label_length :, ...],
					    )  ,      dim=-1  ,      )

					with tempfile.TemporaryDirectory() as tmpdirname:
								__snake_case					:      List[Any]							      =     model.get_decoder()
								decoder.save_pretrained(__magic_name__			)
								__snake_case					:      Dict							      =     AutoformerDecoder.from_pretrained(__magic_name__			).to(__magic_name__			)

					__snake_case					:      str							      =     decoder(
					    trend=__magic_name__  ,      inputs_embeds=__magic_name__  ,      encoder_hidden_states=__magic_name__  ,      )[0]

					self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3			)





@require_torch
class  _A (					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   Tuple				     =   (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
		lowercase__:   int				     =   (AutoformerForPrediction,) if is_torch_available() else ()
		lowercase__:   int				     =   {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
		lowercase__:   int				     =   False
		lowercase__:   Optional[int]				     =   False
		lowercase__:   Dict				     =   False
		lowercase__:   List[str]				     =   False
		lowercase__:   int				     =   False
		lowercase__:   List[str]				     =   False

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     AutoformerModelTester(self			)
					__snake_case					:      Dict							      =     ConfigTester(self  ,      config_class=__magic_name__  ,      has_text_modality=__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					self.config_tester.run_common_tests()

		def 	lowercase__     (   self					:   Optional[int]			)					->    int:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
								__snake_case					:      int							      =     model_class(__magic_name__			)

								with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(__magic_name__			)
											__snake_case      ,     __snake_case					:      str							      =     model_class.from_pretrained(__magic_name__  ,      output_loading_info=__magic_name__			)
								self.assertEqual(info["""missing_keys"""]  ,      []			)

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_encoder_decoder_model_standalone(*__magic_name__			)

		@unittest.skip(reason="""Model has no tokens embeddings"""			)
		def 	lowercase__     (   self					:   Optional[Any]			)					->    str:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     inspect.signature(getattr(__magic_name__  ,      """forward"""			)			)
					# The main input is the name of the argument after `self`
					__snake_case					:      List[Any]							      =     list(model_signature.parameters.keys()			)[1]
					self.assertEqual(AutoformerModel.main_input_name  ,      __magic_name__			)

		def 	lowercase__     (   self					:   int			)					->    Dict:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      int							      =     model_class(__magic_name__			)
								__snake_case					:      Tuple							      =     inspect.signature(model.forward			)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__snake_case					:      Tuple							      =     [*signature.parameters.keys()]

								__snake_case					:      Any							      =     [
								    """past_values""",
								    """past_time_features""",
								    """past_observed_mask""",
								    """static_categorical_features""",
								    """static_real_features""",
								    """future_values""",
								    """future_time_features""",
								]

								if model.__class__.__name__ in ["AutoformerForPrediction"]:
											expected_arg_names.append("""future_observed_mask"""			)

								expected_arg_names.extend(
								    [
								        """decoder_attention_mask""",
								        """head_mask""",
								        """decoder_head_mask""",
								        """cross_attn_head_mask""",
								        """encoder_outputs""",
								        """past_key_values""",
								        """output_hidden_states""",
								        """output_attentions""",
								        """use_cache""",
								        """return_dict""",
								    ]			)

								self.assertListEqual(arg_names[: len(__magic_name__			)]  ,      __magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case					:      Any							      =     True

					__snake_case					:      Union[str, Any]							      =     getattr(self.model_tester  ,      """seq_length"""  ,      __magic_name__			)
					__snake_case					:      str							      =     getattr(self.model_tester  ,      """decoder_seq_length"""  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     getattr(self.model_tester  ,      """encoder_seq_length"""  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     getattr(self.model_tester  ,      """d_model"""  ,      __magic_name__			)
					__snake_case					:      str							      =     getattr(self.model_tester  ,      """num_attention_heads"""  ,      __magic_name__			)
					__snake_case					:      Optional[int]							      =     d_model // num_attention_heads

					for model_class in self.all_model_classes:
								__snake_case					:      Optional[int]							      =     True
								__snake_case					:      List[Any]							      =     False
								__snake_case					:      Optional[Any]							      =     True
								__snake_case					:      List[str]							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								with torch.no_grad():
											__snake_case					:      Dict							      =     model(**self._prepare_for_class(__magic_name__  ,      __magic_name__			)			)
								__snake_case					:      List[str]							      =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(__magic_name__			)  ,      self.model_tester.num_hidden_layers			)

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								__snake_case					:      str							      =     True
								__snake_case					:      Optional[Any]							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								with torch.no_grad():
											__snake_case					:      str							      =     model(**self._prepare_for_class(__magic_name__  ,      __magic_name__			)			)
								__snake_case					:      List[Any]							      =     outputs.encoder_attentions
								self.assertEqual(len(__magic_name__			)  ,      self.model_tester.num_hidden_layers			)

								self.assertListEqual(
								    list(attentions[0].shape[-3:]			)  ,      [self.model_tester.num_attention_heads, encoder_seq_length, dim]  ,      )
								__snake_case					:      Union[str, Any]							      =     len(__magic_name__			)

								__snake_case					:      Dict							      =     7

								if "last_hidden_state" in outputs:
											correct_outlen += 1

								if "trend" in outputs:
											correct_outlen += 1

								if "past_key_values" in outputs:
											correct_outlen += 1  # past_key_values have been returned

								if "loss" in outputs:
											correct_outlen += 1

								if "params" in outputs:
											correct_outlen += 1

								self.assertEqual(__magic_name__  ,      __magic_name__			)

								# decoder attentions
								__snake_case					:      Union[str, Any]							      =     outputs.decoder_attentions
								self.assertIsInstance(__magic_name__  ,      (list, tuple)			)
								self.assertEqual(len(__magic_name__			)  ,      self.model_tester.num_hidden_layers			)
								self.assertListEqual(
								    list(decoder_attentions[0].shape[-3:]			)  ,      [self.model_tester.num_attention_heads, decoder_seq_length, dim]  ,      )

								# cross attentions
								__snake_case					:      str							      =     outputs.cross_attentions
								self.assertIsInstance(__magic_name__  ,      (list, tuple)			)
								self.assertEqual(len(__magic_name__			)  ,      self.model_tester.num_hidden_layers			)
								self.assertListEqual(
								    list(cross_attentions[0].shape[-3:]			)  ,      [self.model_tester.num_attention_heads, decoder_seq_length, dim]  ,      )

					# Check attention is always last and order is fine
					__snake_case					:      str							      =     True
					__snake_case					:      int							      =     True
					__snake_case					:      Any							      =     model_class(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()
					with torch.no_grad():
								__snake_case					:      Optional[int]							      =     model(**self._prepare_for_class(__magic_name__  ,      __magic_name__			)			)

					self.assertEqual(out_len + 2  ,      len(__magic_name__			)			)

					__snake_case					:      List[Any]							      =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

					self.assertEqual(len(__magic_name__			)  ,      self.model_tester.num_hidden_layers			)
					self.assertListEqual(
					    list(self_attentions[0].shape[-3:]			)  ,      [self.model_tester.num_attention_heads, encoder_seq_length, dim]  ,      )

		@is_flaky()
		def 	lowercase__     (   self					:   List[str]			)					->    int:


					"""simple docstring"""

					super().test_retain_grad_hidden_states_attentions()







def 					_a						(       _lowerCamelCase="train-batch.pt" ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Tuple							      =     hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""    ,	filename=_lowerCamelCase    ,	repo_type="""dataset""" )
			__snake_case					:      Union[str, Any]							      =     torch.load(_lowerCamelCase    ,	map_location=_lowerCamelCase )
			return batch




@require_torch
@slow
class  _A (					unittest.TestCase		):

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


					"""simple docstring"""

					__snake_case					:      int							      =     AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""			).to(__magic_name__			)
					__snake_case					:      str							      =     prepare_batch()

					with torch.no_grad():
								__snake_case					:      Any							      =     model(
								    past_values=batch["""past_values"""]  ,      past_time_features=batch["""past_time_features"""]  ,      past_observed_mask=batch["""past_observed_mask"""]  ,      static_categorical_features=batch["""static_categorical_features"""]  ,      future_values=batch["""future_values"""]  ,      future_time_features=batch["""future_time_features"""]  ,      )[0]

					__snake_case					:      List[str]							      =     torch.Size(
					    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)			)
					self.assertEqual(output.shape  ,      __magic_name__			)

					__snake_case					:      Any							      =     torch.tensor(
					    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]  ,      device=__magic_name__			)
					self.assertTrue(torch.allclose(output[0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

		def 	lowercase__     (   self					:   str			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""			).to(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     prepare_batch("""val-batch.pt"""			)
					with torch.no_grad():
								__snake_case					:      Optional[int]							      =     model(
								    past_values=batch["""past_values"""]  ,      past_time_features=batch["""past_time_features"""]  ,      past_observed_mask=batch["""past_observed_mask"""]  ,      static_categorical_features=batch["""static_categorical_features"""]  ,      ).encoder_last_hidden_state
					__snake_case					:      Optional[int]							      =     torch.Size((64, model.config.context_length, model.config.d_model)			)
					self.assertEqual(output.shape  ,      __magic_name__			)

					__snake_case					:      Tuple							      =     torch.tensor(
					    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]  ,      device=__magic_name__			)
					self.assertTrue(torch.allclose(output[0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

		def 	lowercase__     (   self					:   List[Any]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""			).to(__magic_name__			)
					__snake_case					:      Tuple							      =     prepare_batch("""val-batch.pt"""			)
					with torch.no_grad():
								__snake_case					:      Any							      =     model.generate(
								    static_categorical_features=batch["""static_categorical_features"""]  ,      past_time_features=batch["""past_time_features"""]  ,      past_values=batch["""past_values"""]  ,      future_time_features=batch["""future_time_features"""]  ,      past_observed_mask=batch["""past_observed_mask"""]  ,      )
					__snake_case					:      List[Any]							      =     torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)			)
					self.assertEqual(outputs.sequences.shape  ,      __magic_name__			)

					__snake_case					:      int							      =     torch.tensor([3130.6763, 4056.5293, 7053.0786]  ,      device=__magic_name__			)
					__snake_case					:      List[Any]							      =     outputs.sequences.mean(dim=1			)
					self.assertTrue(torch.allclose(mean_prediction[0, -3:]  ,      __magic_name__  ,      rtol=1E-1			)			)









'''simple docstring'''






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__UpperCamelCase					    =	logging.get_logger(__name__)  # pylint: disable=invalid-name




class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   WhisperForConditionalGeneration  ,      __magic_name__					:   WhisperProcessor  ,      __magic_name__					:   AutoencoderKL  ,      __magic_name__					:   CLIPTextModel  ,      __magic_name__					:   CLIPTokenizer  ,      __magic_name__					:   UNetaDConditionModel  ,      __magic_name__					:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,      __magic_name__					:   StableDiffusionSafetyChecker  ,      __magic_name__					:   CLIPImageProcessor  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					super().__init__()

					if safety_checker is None:
								logger.warning(
								    f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
								    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
								    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
								    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
								    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
								    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""			)

					self.register_modules(
					    speech_model=__magic_name__  ,      speech_processor=__magic_name__  ,      vae=__magic_name__  ,      text_encoder=__magic_name__  ,      tokenizer=__magic_name__  ,      unet=__magic_name__  ,      scheduler=__magic_name__  ,      feature_extractor=__magic_name__  ,      )

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Optional[Union[str, int]] = "auto"			)					->    Union[str, Any]:


					"""simple docstring"""

					if slice_size == "auto":
								__snake_case					:      str							      =     self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					self.enable_attention_slicing(__magic_name__			)

		@torch.no_grad()
		def __call__(   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict=1_60_00  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 50  ,      __magic_name__					:   float = 7.5  ,      __magic_name__					:   Optional[Union[str, List[str]]] = None  ,      __magic_name__					:   Optional[int] = 1  ,      __magic_name__					:   float = 0.0  ,      __magic_name__					:   Optional[torch.Generator] = None  ,      __magic_name__					:   Optional[torch.FloatTensor] = None  ,      __magic_name__					:   Optional[str] = "pil"  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      __magic_name__					:   int = 1  ,      **__magic_name__					:   List[str]  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.speech_processor.feature_extractor(
					    __magic_name__  ,      return_tensors="""pt"""  ,      sampling_rate=__magic_name__			).input_features.to(self.device			)
					__snake_case					:      List[str]							      =     self.speech_model.generate(__magic_name__  ,      max_length=48_00_00			)

					__snake_case					:      List[Any]							      =     self.speech_processor.tokenizer.batch_decode(__magic_name__  ,      skip_special_tokens=__magic_name__  ,      normalize=__magic_name__			)[
					    0
					]

					if isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Tuple							      =     1
					elif isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Optional[int]							      =     len(__magic_name__			)
					else:
								raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__magic_name__			)}'''			)

					if height % 8 != 0 or width % 8 != 0:
								raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''			)

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(__magic_name__  ,      __magic_name__			) or callback_steps <= 0)
					):
								raise ValueError(
								    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
								    f''' {type(__magic_name__			)}.'''			)

					# get prompt text embeddings
					__snake_case					:      Dict							      =     self.tokenizer(
					    __magic_name__  ,      padding="""max_length"""  ,      max_length=self.tokenizer.model_max_length  ,      return_tensors="""pt"""  ,      )
					__snake_case					:      Optional[Any]							      =     text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
								__snake_case					:      Tuple							      =     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
								logger.warning(
								    """The following part of your input was truncated because CLIP can only handle sequences up to"""
								    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''			)
								__snake_case					:      Any							      =     text_input_ids[:, : self.tokenizer.model_max_length]
					__snake_case					:      int							      =     self.text_encoder(text_input_ids.to(self.device			)			)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					__snake_case      ,     __snake_case      ,     __snake_case					:      Any							      =     text_embeddings.shape
					__snake_case					:      List[Any]							      =     text_embeddings.repeat(1  ,      __magic_name__  ,      1			)
					__snake_case					:      Dict							      =     text_embeddings.view(bs_embed * num_images_per_prompt  ,      __magic_name__  ,      -1			)

					# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
					# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
					# corresponds to doing no classifier free guidance.
					__snake_case					:      Optional[int]							      =     guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
								__snake_case					:      List[str]
								if negative_prompt is None:
											__snake_case					:      Optional[Any]							      =     [""""""] * batch_size
								elif type(__magic_name__			) is not type(__magic_name__			):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__			)} !='''
											    f''' {type(__magic_name__			)}.'''			)
								elif isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      Dict							      =     [negative_prompt]
								elif batch_size != len(__magic_name__			):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__			)}, but `prompt`:'''
											    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
											    """ the batch size of `prompt`."""			)
								else:
											__snake_case					:      int							      =     negative_prompt

								__snake_case					:      List[str]							      =     text_input_ids.shape[-1]
								__snake_case					:      Any							      =     self.tokenizer(
								    __magic_name__  ,      padding="""max_length"""  ,      max_length=__magic_name__  ,      truncation=__magic_name__  ,      return_tensors="""pt"""  ,      )
								__snake_case					:      Dict							      =     self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

								# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
								__snake_case					:      Optional[int]							      =     uncond_embeddings.shape[1]
								__snake_case					:      Union[str, Any]							      =     uncond_embeddings.repeat(1  ,      __magic_name__  ,      1			)
								__snake_case					:      Tuple							      =     uncond_embeddings.view(batch_size * num_images_per_prompt  ,      __magic_name__  ,      -1			)

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								__snake_case					:      Dict							      =     torch.cat([uncond_embeddings, text_embeddings]			)

					# get the initial random noise unless the user supplied it

					# Unlike in other pipelines, latents need to be generated in the target device
					# for 1-to-1 results reproducibility with the CompVis implementation.
					# However this currently doesn't work in `mps`.
					__snake_case					:      List[Any]							      =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					__snake_case					:      List[Any]							      =     text_embeddings.dtype
					if latents is None:
								if self.device.type == "mps":
											# randn does not exist on mps
											__snake_case					:      Optional[int]							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device="""cpu"""  ,      dtype=__magic_name__			).to(
											    self.device			)
								else:
											__snake_case					:      int							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device=self.device  ,      dtype=__magic_name__			)
					else:
								if latents.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
								__snake_case					:      List[str]							      =     latents.to(self.device			)

					# set timesteps
					self.scheduler.set_timesteps(__magic_name__			)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					__snake_case					:      Optional[int]							      =     self.scheduler.timesteps.to(self.device			)

					# scale the initial noise by the standard deviation required by the scheduler
					__snake_case					:      str							      =     latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__snake_case					:      Tuple							      =     """eta""" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
					__snake_case					:      List[str]							      =     {}
					if accepts_eta:
								__snake_case					:      str							      =     eta

					for i, t in enumerate(self.progress_bar(__magic_name__			)			):
								# expand the latents if we are doing classifier free guidance
								__snake_case					:      Union[str, Any]							      =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
								__snake_case					:      Dict							      =     self.scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								# predict the noise residual
								__snake_case					:      Tuple							      =     self.unet(__magic_name__  ,      __magic_name__  ,      encoder_hidden_states=__magic_name__			).sample

								# perform guidance
								if do_classifier_free_guidance:
											__snake_case      ,     __snake_case					:      str							      =     noise_pred.chunk(2			)
											__snake_case					:      Any							      =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								__snake_case					:      Optional[Any]							      =     self.scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      **__magic_name__			).prev_sample

								# call the callback, if provided
								if callback is not None and i % callback_steps == 0:
											callback(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					__snake_case					:      int							      =     1 / 0.18215 * latents
					__snake_case					:      Optional[Any]							      =     self.vae.decode(__magic_name__			).sample

					__snake_case					:      Any							      =     (image / 2 + 0.5).clamp(0  ,      1			)

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					__snake_case					:      Any							      =     image.cpu().permute(0  ,      2  ,      3  ,      1			).float().numpy()

					if output_type == "pil":
								__snake_case					:      Tuple							      =     self.numpy_to_pil(__magic_name__			)

					if not return_dict:
								return image

					return StableDiffusionPipelineOutput(images=__magic_name__  ,      nsfw_content_detected=__magic_name__			)


