





'''simple docstring'''






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
      import torch

      from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class 			UpperCAmelCase					:




      def __init__(      self      :			Any      ,      __snake_case      :			Any      ,      __snake_case      :			int=99      ,      __snake_case      :			Any=13      ,      __snake_case      :			Any=7      ,      __snake_case      :			Optional[int]=9      ,      __snake_case      :			Any=True      ,      __snake_case      :			Any=True      ,      __snake_case      :			Any=False      ,      __snake_case      :			Union[str, Any]=32      ,      __snake_case      :			int=5      ,      __snake_case      :			Union[str, Any]=4      ,      __snake_case      :			Union[str, Any]=37      ,      __snake_case      :			Any=8      ,      __snake_case      :			Union[str, Any]=0.1      ,      __snake_case      :			int=0.0_02      ,      __snake_case      :			Dict=1      ,      __snake_case      :			List[Any]=0      ,      __snake_case      :			str=0      ,      __snake_case      :			Any=None      ,      __snake_case      :			Dict=None      ,      )    ->				List[str]:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							encoder_seq_length
            _lowerCAmelCase												=							decoder_seq_length
            # For common tests
            _lowerCAmelCase												=							self.decoder_seq_length
            _lowerCAmelCase												=							is_training
            _lowerCAmelCase												=							use_attention_mask
            _lowerCAmelCase												=							use_labels
            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							d_ff
            _lowerCAmelCase												=							relative_attention_num_buckets
            _lowerCAmelCase												=							dropout_rate
            _lowerCAmelCase												=							initializer_factor
            _lowerCAmelCase												=							eos_token_id
            _lowerCAmelCase												=							pad_token_id
            _lowerCAmelCase												=							decoder_start_token_id
            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							decoder_layers




      def     lowercase__   (      self      :			List[Any]  )    ->				Optional[Any]:
            return TaConfig.from_pretrained("""google/umt5-base"""  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Dict      ,      __snake_case      :			Dict      ,      __snake_case      :			Dict=None      ,      __snake_case      :			int=None      ,      __snake_case      :			Optional[int]=None      ,      __snake_case      :			Union[str, Any]=None      ,      __snake_case      :			List[str]=None      ,      )    ->				int:
            if attention_mask is None:
                  _lowerCAmelCase												=							input_ids.ne(config.pad_token_id  )
            if decoder_attention_mask is None:
                  _lowerCAmelCase												=							decoder_input_ids.ne(config.pad_token_id  )
            if head_mask is None:
                  _lowerCAmelCase												=							torch.ones(config.num_hidden_layers      ,      config.num_attention_heads      ,      device=__snake_case  )
            if decoder_head_mask is None:
                  _lowerCAmelCase												=							torch.ones(config.num_decoder_layers      ,      config.num_attention_heads      ,      device=__snake_case  )
            if cross_attn_head_mask is None:
                  _lowerCAmelCase												=							torch.ones(
                      config.num_decoder_layers      ,      config.num_attention_heads      ,      device=__snake_case  )
            return {
                "input_ids": input_ids,
                "decoder_input_ids": decoder_input_ids,
                "attention_mask": attention_mask,
                "decoder_attention_mask": decoder_attention_mask,
                "head_mask": head_mask,
                "decoder_head_mask": decoder_head_mask,
                "cross_attn_head_mask": cross_attn_head_mask,
            }




      def     lowercase__   (      self      :			Dict  )    ->				Optional[Any]:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.encoder_seq_length]      ,      self.vocab_size  )
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.decoder_seq_length]      ,      self.vocab_size  )

            # we need to clamp the input ids here to avoid having pad token in between
            # this is because for NllbMoe the position_ids are prepared such that
            # all pad tokens have pos id = 2 and rest are between 2..seq_length
            # and the seq_length here is seq_length - num_pad_tokens
            # but when using past, there is no way of knowing if the past input ids had
            # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
            # position_ids being off by num_pad_tokens in past input
            _lowerCAmelCase												=							input_ids.clamp(self.pad_token_id + 1  )
            _lowerCAmelCase												=							decoder_input_ids.clamp(self.pad_token_id + 1  )

            _lowerCAmelCase												=							self.get_config()
            _lowerCAmelCase												=							config.num_attention_heads
            _lowerCAmelCase												=							self.prepare_inputs_dict(__snake_case      ,      __snake_case      ,      __snake_case  )
            return config, input_dict




      def     lowercase__   (      self      :			Optional[Any]  )    ->				int:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.prepare_config_and_inputs()
            return config, inputs_dict




      def     lowercase__   (      self      :			Tuple  )    ->				int:
            return TaConfig(
                vocab_size=1_66      ,      d_model=self.hidden_size      ,      d_ff=self.d_ff      ,      d_kv=self.hidden_size // self.num_attention_heads      ,      num_layers=self.num_hidden_layers      ,      num_decoder_layers=self.decoder_layers      ,      num_heads=self.num_attention_heads      ,      relative_attention_num_buckets=self.relative_attention_num_buckets      ,      dropout_rate=self.dropout_rate      ,      initializer_factor=self.initializer_factor      ,      eos_token_id=self.eos_token_id      ,      bos_token_id=self.pad_token_id      ,      pad_token_id=self.pad_token_id      ,      decoder_start_token_id=self.decoder_start_token_id      ,      )




      def     lowercase__   (      self      :			Optional[Any]  )    ->				Union[str, Any]:
            return TaConfig(
                vocab_size=self.vocab_size      ,      d_model=self.hidden_size      ,      d_ff=self.d_ff      ,      d_kv=self.hidden_size // self.num_attention_heads      ,      num_layers=self.num_hidden_layers      ,      num_decoder_layers=self.decoder_layers      ,      num_heads=self.num_attention_heads      ,      relative_attention_num_buckets=self.relative_attention_num_buckets      ,      dropout_rate=self.dropout_rate      ,      initializer_factor=self.initializer_factor      ,      eos_token_id=self.eos_token_id      ,      bos_token_id=self.pad_token_id      ,      pad_token_id=self.pad_token_id      ,      decoder_start_token_id=self.decoder_start_token_id      ,      )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Dict      ,      __snake_case      :			Tuple      ,      __snake_case      :			List[str]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			str      ,      )    ->				List[Any]:
            _lowerCAmelCase												=							UMTaModel(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(
                input_ids=__snake_case      ,      decoder_input_ids=__snake_case      ,      attention_mask=__snake_case      ,      decoder_attention_mask=__snake_case      ,      )
            _lowerCAmelCase												=							model(input_ids=__snake_case      ,      decoder_input_ids=__snake_case  )
            _lowerCAmelCase												=							result.last_hidden_state
            _lowerCAmelCase												=							result.past_key_values
            _lowerCAmelCase												=							result.encoder_last_hidden_state

            self.parent.assertEqual(encoder_output.size()      ,      (self.batch_size, self.encoder_seq_length, self.hidden_size)  )
            self.parent.assertEqual(decoder_output.size()      ,      (self.batch_size, self.decoder_seq_length, self.hidden_size)  )
            # There should be `num_layers` key value embeddings stored in decoder_past
            self.parent.assertEqual(len(__snake_case  )      ,      config.num_layers  )
            # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
            self.parent.assertEqual(len(decoder_past[0]  )      ,      4  )




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Any      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Tuple      ,      )    ->				Optional[int]:
            _lowerCAmelCase												=							UMTaModel(config=__snake_case  ).get_decoder().to(__snake_case  ).eval()
            # first forward pass
            _lowerCAmelCase												=							model(__snake_case      ,      use_cache=__snake_case  )
            _lowerCAmelCase												=							model(__snake_case  )
            _lowerCAmelCase												=							model(__snake_case      ,      use_cache=__snake_case  )

            self.parent.assertTrue(len(__snake_case  ) == len(__snake_case  )  )
            self.parent.assertTrue(len(__snake_case  ) == len(__snake_case  ) + 1  )

            _lowerCAmelCase					,							_lowerCAmelCase												=							outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            _lowerCAmelCase												=							ids_tensor((self.batch_size, 1)      ,      config.vocab_size  )

            # append to next input_ids and
            _lowerCAmelCase												=							torch.cat([input_ids, next_tokens]      ,      dim=-1  )

            _lowerCAmelCase												=							model(__snake_case  )["""last_hidden_state"""]
            _lowerCAmelCase												=							model(__snake_case      ,      past_key_values=__snake_case  )["""last_hidden_state"""]

            # select random slice
            _lowerCAmelCase												=							ids_tensor((1,)      ,      output_from_past.shape[-1]  ).item()
            _lowerCAmelCase												=							output_from_no_past[:, -1, random_slice_idx].detach()
            _lowerCAmelCase												=							output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__snake_case      ,      __snake_case      ,      atol=1E-3  )  )




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Any      ,      )    ->				Union[str, Any]:
            _lowerCAmelCase												=							UMTaModel(config=__snake_case  ).to(__snake_case  ).half().eval()
            _lowerCAmelCase												=							model(**__snake_case  )["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(__snake_case  ).any().item()  )




@require_torch
class 			UpperCAmelCase					(      snake_case_							,		snake_case_							,		snake_case_							,		unittest.TestCase       ):
      _lowercase:      Optional[int]											=       (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      _lowercase:      List[Any]											=       (UMTaForConditionalGeneration,) if is_torch_available() else ()
      _lowercase:      Any											=       (
          {
              '''conversational''': UMTaForConditionalGeneration,
              '''feature-extraction''': UMTaModel,
              '''summarization''': UMTaForConditionalGeneration,
              '''text2text-generation''': UMTaForConditionalGeneration,
              '''translation''': UMTaForConditionalGeneration,
              '''question-answering''': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      _lowercase:      int											=       True
      _lowercase:      Union[str, Any]											=       False
      _lowercase:      List[Any]											=       False
      _lowercase:      Union[str, Any]											=       True
      _lowercase:      Any											=       True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      _lowercase:      Any											=       [0.8, 0.9]




      def     lowercase__   (      self      :			int  )    ->				str:
            _lowerCAmelCase												=							UMTaModelTester(self  )




      @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""  )
      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            _lowerCAmelCase												=							UMTaModel(config_and_inputs[0]  ).to(__snake_case  )
            with tempfile.TemporaryDirectory() as tmpdirname:
                  torch.onnx.export(
                      __snake_case      ,      (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,      f"{tmpdirname}/t5_test.onnx"      ,      export_params=__snake_case      ,      opset_version=9      ,      input_names=["""input_ids""", """decoder_input_ids"""]      ,      )




      @unittest.skipIf(torch_device == """cpu"""      ,      """Cant do half precision"""  )
      def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*__snake_case  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Any:
            _lowerCAmelCase												=							["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            _lowerCAmelCase												=							config_and_inputs[0]
            _lowerCAmelCase												=							UMTaForConditionalGeneration(__snake_case  ).eval()
            model.to(__snake_case  )

            _lowerCAmelCase												=							{
                """head_mask""": torch.zeros(config.num_layers      ,      config.num_heads      ,      device=__snake_case  ),
                """decoder_head_mask""": torch.zeros(config.num_decoder_layers      ,      config.num_heads      ,      device=__snake_case  ),
                """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers      ,      config.num_heads      ,      device=__snake_case  ),
            }

            for attn_name, (name, mask) in zip(__snake_case      ,      head_masking.items()  ):
                  _lowerCAmelCase												=							{name: mask}
                  # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                  if name == "head_mask":
                        _lowerCAmelCase												=							torch.ones(
                            config.num_decoder_layers      ,      config.num_heads      ,      device=__snake_case  )

                  _lowerCAmelCase												=							model.generate(
                      config_and_inputs[1]["""input_ids"""]      ,      num_beams=1      ,      max_length=3      ,      output_attentions=__snake_case      ,      return_dict_in_generate=__snake_case      ,      **__snake_case      ,      )
                  # We check the state of decoder_attentions and cross_attentions just from the last step
                  _lowerCAmelCase												=							out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                  self.assertEqual(sum([w.sum().item() for w in attn_weights]  )      ,      0.0  )




      @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""  )
      def     lowercase__   (      self      :			List[Any]  )    ->				List[Any]:
            pass




@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @slow
      @unittest.skip(
          """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""  )
      def     lowercase__   (      self      :			List[Any]  )    ->				Any:
            _lowerCAmelCase												=							UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""      ,      return_dict=__snake_case  ).to(__snake_case  )
            _lowerCAmelCase												=							AutoTokenizer.from_pretrained("""google/umt5-small"""      ,      use_fast=__snake_case      ,      legacy=__snake_case  )
            _lowerCAmelCase												=							[
                """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
                """No se como puedo <extra_id_0>.""",
                """This is the reason why we <extra_id_0> them.""",
                """The <extra_id_0> walks in <extra_id_1>, seats""",
                """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
            ]
            _lowerCAmelCase												=							tokenizer(__snake_case      ,      return_tensors="""pt"""      ,      padding=__snake_case  ).input_ids
            # fmt: off
            _lowerCAmelCase												=							torch.tensor(
                [
                    [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
                ]  )
            # fmt: on
            torch.testing.assert_allclose(__snake_case      ,      __snake_case  )

            _lowerCAmelCase												=							model.generate(input_ids.to(__snake_case  )  )
            _lowerCAmelCase												=							[
                """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
                """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            ]
            _lowerCAmelCase												=							tokenizer.batch_decode(__snake_case  )
            self.assertEqual(__snake_case      ,      __snake_case  )













'''simple docstring'''






from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

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


A__    :       Optional[int]              ='''CompVis/stable-diffusion-v1-1'''
A__    :       Optional[int]              ='''CompVis/stable-diffusion-v1-2'''
A__    :       List[str]              ='''CompVis/stable-diffusion-v1-3'''
A__    :       Optional[int]              ='''CompVis/stable-diffusion-v1-4'''




class 			UpperCAmelCase					(      snake_case_       ):




      def __init__(      self      :			Any      ,      __snake_case      :			AutoencoderKL      ,      __snake_case      :			CLIPTextModel      ,      __snake_case      :			CLIPTokenizer      ,      __snake_case      :			UNetaDConditionModel      ,      __snake_case      :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,      __snake_case      :			StableDiffusionSafetyChecker      ,      __snake_case      :			CLIPImageProcessor      ,      __snake_case      :			bool = True      ,      )    ->				Any:
            super()._init_()

            _lowerCAmelCase												=							StableDiffusionPipeline.from_pretrained(__snake_case  )
            _lowerCAmelCase												=							StableDiffusionPipeline.from_pretrained(__snake_case  )
            _lowerCAmelCase												=							StableDiffusionPipeline.from_pretrained(__snake_case  )
            _lowerCAmelCase												=							StableDiffusionPipeline(
                vae=__snake_case      ,      text_encoder=__snake_case      ,      tokenizer=__snake_case      ,      unet=__snake_case      ,      scheduler=__snake_case      ,      safety_checker=__snake_case      ,      feature_extractor=__snake_case      ,      requires_safety_checker=__snake_case      ,      )

            self.register_modules(pipelinea=self.pipea      ,      pipelinea=self.pipea      ,      pipelinea=self.pipea      ,      pipelinea=self.pipea  )




      @property
      def     lowercase__   (      self      :			List[Any]  )    ->				Dict[str, Any]:
            return {k: getattr(self      ,      __snake_case  ) for k in self.config.keys() if not k.startswith("""_"""  )}




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			Optional[Union[str, int]] = "auto"  )    ->				Tuple:
            if slice_size == "auto":
                  # half the attention head size is usually a good trade-off between
                  # speed and memory
                  _lowerCAmelCase												=							self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				int:
            self.enable_attention_slicing(__snake_case  )




      @torch.no_grad()
      def     lowercase__   (      self      :			Any      ,      __snake_case      :			Union[str, List[str]]      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 50      ,      __snake_case      :			float = 7.5      ,      __snake_case      :			Optional[Union[str, List[str]]] = None      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[torch.FloatTensor] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      __snake_case      :			int = 1      ,      **__snake_case      :			Tuple      ,      )    ->				Tuple:
            return self.pipea(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )




      @torch.no_grad()
      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			Union[str, List[str]]      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 50      ,      __snake_case      :			float = 7.5      ,      __snake_case      :			Optional[Union[str, List[str]]] = None      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[torch.FloatTensor] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      __snake_case      :			int = 1      ,      **__snake_case      :			int      ,      )    ->				List[Any]:
            return self.pipea(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )




      @torch.no_grad()
      def     lowercase__   (      self      :			Any      ,      __snake_case      :			Union[str, List[str]]      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 50      ,      __snake_case      :			float = 7.5      ,      __snake_case      :			Optional[Union[str, List[str]]] = None      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[torch.FloatTensor] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      __snake_case      :			int = 1      ,      **__snake_case      :			Tuple      ,      )    ->				Dict:
            return self.pipea(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )




      @torch.no_grad()
      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			Union[str, List[str]]      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 50      ,      __snake_case      :			float = 7.5      ,      __snake_case      :			Optional[Union[str, List[str]]] = None      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[torch.FloatTensor] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      __snake_case      :			int = 1      ,      **__snake_case      :			str      ,      )    ->				List[Any]:
            return self.pipea(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )




      @torch.no_grad()
      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Union[str, List[str]]      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 50      ,      __snake_case      :			float = 7.5      ,      __snake_case      :			Optional[Union[str, List[str]]] = None      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[torch.FloatTensor] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      __snake_case      :			int = 1      ,      **__snake_case      :			Tuple      ,      )    ->				Optional[Any]:
            _lowerCAmelCase												=							"""cuda""" if torch.cuda.is_available() else """cpu"""
            self.to(__snake_case  )

            # Checks if the height and width are divisible by 8 or not
            if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}."  )

            # Get first result from Stable Diffusion Checkpoint v1.1
            _lowerCAmelCase												=							self.textaimg_sda_a(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )

            # Get first result from Stable Diffusion Checkpoint v1.2
            _lowerCAmelCase												=							self.textaimg_sda_a(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )

            # Get first result from Stable Diffusion Checkpoint v1.3
            _lowerCAmelCase												=							self.textaimg_sda_a(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )

            # Get first result from Stable Diffusion Checkpoint v1.4
            _lowerCAmelCase												=							self.textaimg_sda_a(
                prompt=__snake_case      ,      height=__snake_case      ,      width=__snake_case      ,      num_inference_steps=__snake_case      ,      guidance_scale=__snake_case      ,      negative_prompt=__snake_case      ,      num_images_per_prompt=__snake_case      ,      eta=__snake_case      ,      generator=__snake_case      ,      latents=__snake_case      ,      output_type=__snake_case      ,      return_dict=__snake_case      ,      callback=__snake_case      ,      callback_steps=__snake_case      ,      **__snake_case      ,      )

            # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
            return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]  )






