




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFTransfoXLForSequenceClassification,
           TFTransfoXLLMHeadModel,
           TFTransfoXLModel,
       )


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     int		, A					:     Union[str, Any]		, )       ->str:
          lowerCamelCase__       :						Dict        =							parent
          lowerCamelCase__       :						Any        =							1_3
          lowerCamelCase__       :						List[Any]        =							7
          lowerCamelCase__       :						List[Any]        =							3_0
          lowerCamelCase__       :						Optional[int]        =							self.seq_length + self.mem_len
          lowerCamelCase__       :						List[str]        =							1_5
          lowerCamelCase__       :						Union[str, Any]        =							True
          lowerCamelCase__       :						List[str]        =							True
          lowerCamelCase__       :						str        =							9_9
          lowerCamelCase__       :						Any        =							[1_0, 5_0, 8_0]
          lowerCamelCase__       :						Tuple        =							3_2
          lowerCamelCase__       :						Optional[Any]        =							3_2
          lowerCamelCase__       :						List[Any]        =							4
          lowerCamelCase__       :						int        =							8
          lowerCamelCase__       :						List[str]        =							1_2_8
          lowerCamelCase__       :						str        =							2
          lowerCamelCase__       :						Dict        =							2
          lowerCamelCase__       :						Union[str, Any]        =							None
          lowerCamelCase__       :						Tuple        =							1
          lowerCamelCase__       :						Tuple        =							0
          lowerCamelCase__       :						List[str]        =							3
          lowerCamelCase__       :						List[Any]        =							self.vocab_size - 1
          lowerCamelCase__       :						int        =							0.01







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          lowerCamelCase__       :						str        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )
          lowerCamelCase__       :						List[str]        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

          lowerCamelCase__       :						str        =							None
          if self.use_labels:
             lowerCamelCase__       :						List[Any]        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

          lowerCamelCase__       :						Tuple        =							TransfoXLConfig(
              vocab_size=self.vocab_size		, mem_len=self.mem_len		, clamp_len=self.clamp_len		, cutoffs=self.cutoffs		, d_model=self.hidden_size		, d_embed=self.d_embed		, n_head=self.num_attention_heads		, d_head=self.d_head		, d_inner=self.d_inner		, div_val=self.div_val		, n_layer=self.num_hidden_layers		, eos_token_id=self.eos_token_id		, pad_token_id=self.vocab_size - 1		, init_range=self.init_range		, num_labels=self.num_labels		, )

          return (config, input_ids_a, input_ids_a, lm_labels)







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Dict:
          random.seed(self.seed      )
          tf.random.set_seed(self.seed      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     List[Any]		, A					:     int		, A					:     Any		, A					:     Any      )       ->Tuple:
          lowerCamelCase__       :						int        =							TFTransfoXLModel(A      )

          lowerCamelCase__       :						Any        =							model(A      ).to_tuple()

          lowerCamelCase__       :						Union[str, Any]        =							{'''input_ids''': input_ids_a, '''mems''': mems_a}

          lowerCamelCase__       :						Union[str, Any]        =							model(A      ).to_tuple()

          self.parent.assertEqual(hidden_states_a.shape		, (self.batch_size, self.seq_length, self.hidden_size)      )
          self.parent.assertEqual(hidden_states_a.shape		, (self.batch_size, self.seq_length, self.hidden_size)      )
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		, [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		, )
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		, [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		, )







       def 						__lowerCamelCase   (				self					:     int		, A					:     Optional[int]		, A					:     Union[str, Any]		, A					:     Tuple		, A					:     Optional[Any]      )       ->Tuple:
          lowerCamelCase__       :						Optional[int]        =							TFTransfoXLLMHeadModel(A      )

          lowerCamelCase__       :						Union[str, Any]        =							model(A      ).to_tuple()

          lowerCamelCase__       :						Union[str, Any]        =							{'''input_ids''': input_ids_a, '''labels''': lm_labels}
          lowerCamelCase__       :						Any        =							model(A      ).to_tuple()

          lowerCamelCase__       :						Any        =							model([input_ids_a, mems_a]      ).to_tuple()

          lowerCamelCase__       :						str        =							{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

          lowerCamelCase__       :						Tuple        =							model(A      ).to_tuple()

          self.parent.assertEqual(lm_logits_a.shape		, (self.batch_size, self.seq_length, self.vocab_size)      )
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		, [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		, )

          self.parent.assertEqual(lm_logits_a.shape		, (self.batch_size, self.seq_length, self.vocab_size)      )
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]		, [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		, )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     str		, A					:     List[str]		, A					:     int		, A					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							TFTransfoXLForSequenceClassification(A      )
          lowerCamelCase__       :						Optional[Any]        =							model(A      )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          lowerCamelCase__       :						Optional[int]        =							self.prepare_config_and_inputs()
          (lowerCamelCase__)       :						Dict        =							config_and_inputs
          lowerCamelCase__       :						str        =							{'''input_ids''': input_ids_a}
          return config, inputs_dict


@require_tf
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Union[str, Any]			   =   (
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       _UpperCAmelCase				:  Dict			   =   () if is_tf_available() else ()
       _UpperCAmelCase				:  List[Any]			   =   (
           {
               "feature-extraction": TFTransfoXLModel,
               "text-classification": TFTransfoXLForSequenceClassification,
               "text-generation": TFTransfoXLLMHeadModel,
               "zero-shot": TFTransfoXLForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
       _UpperCAmelCase				:  Optional[Any]			   =   False
       _UpperCAmelCase				:  Union[str, Any]			   =   False
       _UpperCAmelCase				:  Dict			   =   False
       _UpperCAmelCase				:  Tuple			   =   False







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Any		, A					:     Union[str, Any]		, A					:     str		, A					:     Optional[Any]		, A					:     Tuple      )       ->List[str]:
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

          return False







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
          lowerCamelCase__       :						Dict        =							TFTransfoXLModelTester(self      )
          lowerCamelCase__       :						Optional[int]        =							ConfigTester(self		, config_class=A		, d_embed=3_7      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
          self.model_tester.set_seed()
          lowerCamelCase__       :						str        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_model(*A      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[Any]:
          self.model_tester.set_seed()
          lowerCamelCase__       :						Dict        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_lm_head(*A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->List[Any]:
          lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__       :						List[str]        =							[TFTransfoXLForSequenceClassification]

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Union[str, Any]        =							model_class(A      )
             assert isinstance(model.get_input_embeddings()		, tf.keras.layers.Layer      )
             if model_class in list_other_models_with_output_ebd:
                lowerCamelCase__       :						Dict        =							model.get_output_embeddings()
                assert isinstance(A		, tf.keras.layers.Layer      )
                lowerCamelCase__       :						Tuple        =							model.get_bias()
                assert name is None
             else:
                lowerCamelCase__       :						str        =							model.get_output_embeddings()
                assert x is None
                lowerCamelCase__       :						Optional[int]        =							model.get_bias()
                assert name is None







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
          # TODO JP: Make TransfoXL XLA compliant
          pass







       @slow
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[int]:
          for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						int        =							TFTransfoXLModel.from_pretrained(A      )
             self.assertIsNotNone(A      )







       @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[Any]:
          pass


@require_tf
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @unittest.skip('''Skip test until #12651 is resolved.'''      )
       @slow
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          lowerCamelCase__       :						str        =							TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
          # fmt: off
          lowerCamelCase__       :						Any        =							tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]		, dtype=tf.intaa      )  # noqa: E231
          # fmt: on
          #  In 1991 , the remains of Russian Tsar Nicholas II and his family
          #  ( except for Alexei and Maria ) are discovered .
          #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
          #  remainder of the story . 1883 Western Siberia ,
          #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
          #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
          #  father initially slaps him for making such an accusation , Rasputin watches as the
          #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
          #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
          #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

          # fmt: off
          lowerCamelCase__       :						int        =							[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
          # fmt: on
          #  In 1991, the remains of Russian Tsar Nicholas II and his family (
          #  except for Alexei and Maria ) are discovered. The voice of young son,
          #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
          #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
          #  and a group of men to perform magic. Rasputin has a vision and
          #  denounces one of the men as a horse thief. Although his father initially
          #  slaps him for making such an accusation, Rasputin watches as the man
          #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
          #  of the Virgin Mary, prompting him to become a priest.
          #  Rasputin quickly becomes famous, with people, even a bishop, begging for
          #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
          # Nicholas II and his family were discovered. The voice of <unk> young son,
          # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

          lowerCamelCase__       :						Any        =							model.generate(A		, max_length=2_0_0		, do_sample=A      )
          self.assertListEqual(output_ids[0].numpy().tolist()		, A      )










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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_A :   Optional[Any]  			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







       def __init__(				self					:     Optional[Any]		, A					:     AutoencoderKL		, A					:     CLIPTextModel		, A					:     CLIPTokenizer		, A					:     UNetaDConditionModel		, A					:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		, A					:     StableDiffusionSafetyChecker		, A					:     CLIPImageProcessor		, )       ->List[Any]:
          super().__init__()
          self.register_modules(
              vae=A		, text_encoder=A		, tokenizer=A		, unet=A		, scheduler=A		, safety_checker=A		, feature_extractor=A		, )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     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__       :						List[Any]        =							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          self.enable_attention_slicing(A      )







       @torch.no_grad()
       def __call__(				self					:     Union[str, Any]		, A					:     Union[str, List[str]]		, A					:     int = 5_1_2		, A					:     int = 5_1_2		, A					:     int = 5_0		, A					:     float = 7.5		, A					:     Optional[Union[str, List[str]]] = None		, A					:     Optional[int] = 1		, A					:     float = 0.0		, A					:     Optional[torch.Generator] = None		, A					:     Optional[torch.FloatTensor] = None		, A					:     Optional[str] = "pil"		, A					:     bool = True		, A					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None		, A					:     int = 1		, A					:     Optional[torch.FloatTensor] = None		, **A					:     int		, )       ->Tuple:

          if isinstance(A		, A      ):
             lowerCamelCase__       :						str        =							1
          elif isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							len(A      )
          else:
             raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(A      )}"      )

          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(A		, A      ) or callback_steps <= 0)
          ):
             raise ValueError(
                 F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 F" {type(A      )}."      )

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

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase__       :						Optional[Any]        =							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}"      )
             lowerCamelCase__       :						Any        =							text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
             lowerCamelCase__       :						str        =							self.text_encoder(text_input_ids.to(self.device      )      )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						int        =							text_embeddings.shape
          lowerCamelCase__       :						int        =							text_embeddings.repeat(1		, A		, 1      )
          lowerCamelCase__       :						str        =							text_embeddings.view(bs_embed * num_images_per_prompt		, A		, -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.
          lowerCamelCase__       :						Optional[Any]        =							guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             lowerCamelCase__       :						List[str]
             if negative_prompt is None:
                lowerCamelCase__       :						Optional[int]        =							['''''']
             elif type(A      ) is not type(A      ):
                raise TypeError(
                    F"`negative_prompt` should be the same type to `prompt`, but got {type(A      )} !="
                    F" {type(A      )}."      )
             elif isinstance(A		, A      ):
                lowerCamelCase__       :						Optional[int]        =							[negative_prompt]
             elif batch_size != len(A      ):
                raise ValueError(
                    F"`negative_prompt`: {negative_prompt} has batch size {len(A      )}, but `prompt`:"
                    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    ''' the batch size of `prompt`.'''      )
             else:
                lowerCamelCase__       :						List[Any]        =							negative_prompt

             lowerCamelCase__       :						int        =							text_input_ids.shape[-1]
             lowerCamelCase__       :						Optional[int]        =							self.tokenizer(
                 A		, padding='''max_length'''		, max_length=A		, truncation=A		, return_tensors='''pt'''		, )
             lowerCamelCase__       :						Optional[Any]        =							self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowerCamelCase__       :						Any        =							uncond_embeddings.shape[1]
             lowerCamelCase__       :						Union[str, Any]        =							uncond_embeddings.repeat(A		, A		, 1      )
             lowerCamelCase__       :						str        =							uncond_embeddings.view(batch_size * num_images_per_prompt		, A		, -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
             lowerCamelCase__       :						str        =							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`.
          lowerCamelCase__       :						Dict        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          lowerCamelCase__       :						List[Any]        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
          lowerCamelCase__       :						List[Any]        =							text_embeddings.dtype
          if latents is None:
             if self.device.type == "mps":
                # randn does not exist on mps
                lowerCamelCase__       :						Dict        =							torch.randn(
                    A		, generator=A		, device='''cpu'''		, dtype=A      ).to(self.device      )
                lowerCamelCase__       :						Dict        =							torch.randn(A		, generator=A		, device='''cpu'''		, dtype=A      ).to(
                    self.device      )
             else:
                lowerCamelCase__       :						Optional[Any]        =							torch.randn(
                    A		, generator=A		, device=self.device		, dtype=A      )
                lowerCamelCase__       :						Union[str, Any]        =							torch.randn(A		, generator=A		, device=self.device		, dtype=A      )
          else:
             if latents_reference.shape != latents_shape:
                raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"      )
             lowerCamelCase__       :						Optional[Any]        =							latents_reference.to(self.device      )
             lowerCamelCase__       :						Optional[int]        =							latents.to(self.device      )

          # This is the key part of the pipeline where we
          # try to ensure that the generated images w/ the same seed
          # but different sizes actually result in similar images
          lowerCamelCase__       :						List[str]        =							(latents_shape[3] - latents_shape_reference[3]) // 2
          lowerCamelCase__       :						List[Any]        =							(latents_shape[2] - latents_shape_reference[2]) // 2
          lowerCamelCase__       :						Dict        =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          lowerCamelCase__       :						str        =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          lowerCamelCase__       :						int        =							0 if dx < 0 else dx
          lowerCamelCase__       :						Optional[int]        =							0 if dy < 0 else dy
          lowerCamelCase__       :						Dict        =							max(-dx		, 0      )
          lowerCamelCase__       :						int        =							max(-dy		, 0      )
          # import pdb
          # pdb.set_trace()
          lowerCamelCase__       :						str        =							latents_reference[:, :, dy : dy + h, dx : dx + w]

          # set timesteps
          self.scheduler.set_timesteps(A      )

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          lowerCamelCase__       :						Union[str, Any]        =							self.scheduler.timesteps.to(self.device      )

          # scale the initial noise by the standard deviation required by the scheduler
          lowerCamelCase__       :						List[Any]        =							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]
          lowerCamelCase__       :						Union[str, Any]        =							'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
          lowerCamelCase__       :						List[Any]        =							{}
          if accepts_eta:
             lowerCamelCase__       :						Any        =							eta

          for i, t in enumerate(self.progress_bar(A      )      ):
             # expand the latents if we are doing classifier free guidance
             lowerCamelCase__       :						Optional[int]        =							torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
             lowerCamelCase__       :						Any        =							self.scheduler.scale_model_input(A		, A      )

             # predict the noise residual
             lowerCamelCase__       :						Union[str, Any]        =							self.unet(A		, A		, encoder_hidden_states=A      ).sample

             # perform guidance
             if do_classifier_free_guidance:
                lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							noise_pred.chunk(2      )
                lowerCamelCase__       :						int        =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__       :						Dict        =							self.scheduler.step(A		, A		, A		, **A      ).prev_sample

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

          lowerCamelCase__       :						Optional[Any]        =							1 / 0.1_82_15 * latents
          lowerCamelCase__       :						int        =							self.vae.decode(A      ).sample

          lowerCamelCase__       :						Dict        =							(image / 2 + 0.5).clamp(0		, 1      )

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

          if self.safety_checker is not None:
             lowerCamelCase__       :						Tuple        =							self.feature_extractor(self.numpy_to_pil(A      )		, return_tensors='''pt'''      ).to(
                 self.device      )
             lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.safety_checker(
                 images=A		, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
          else:
             lowerCamelCase__       :						List[Any]        =							None

          if output_type == "pil":
             lowerCamelCase__       :						Optional[Any]        =							self.numpy_to_pil(A      )

          if not return_dict:
             return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=A		, nsfw_content_detected=A      )



