





'''simple docstring'''

import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   lowercase__		:     Dict			=							'platform'
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 							__lowerCAmelCase	:






       """simple docstring"""
       _snake_case      :	Tuple									=       PegasusConfig
       _snake_case      :	Any									=       {}
       _snake_case      :	Dict									=       'gelu'





       def __init__(				self  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Dict=13			,					lowerCAmelCase__  :						Optional[Any]=7			,					lowerCAmelCase__  :						int=True			,					lowerCAmelCase__  :						int=False			,					lowerCAmelCase__  :						Tuple=99			,					lowerCAmelCase__  :						Optional[Any]=32			,					lowerCAmelCase__  :						Dict=5			,					lowerCAmelCase__  :						Dict=4			,					lowerCAmelCase__  :						Union[str, Any]=37			,					lowerCAmelCase__  :						str=0.1			,					lowerCAmelCase__  :						Any=0.1			,					lowerCAmelCase__  :						List[str]=20			,					lowerCAmelCase__  :						Optional[Any]=2			,					lowerCAmelCase__  :						str=1			,					lowerCAmelCase__  :						Tuple=0			,					)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			seq_length
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size

            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			max_position_embeddings
            _UpperCamelCase							       =			eos_token_id
            _UpperCamelCase							       =			pad_token_id
            _UpperCamelCase							       =			bos_token_id





       def snake_case__		(				self  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			ids_tensor([self.batch_size, self.seq_length - 1]			,					self.vocab_size	).clip(3			,					self.vocab_size	)
            _UpperCamelCase							       =			np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)			,					1	)
            _UpperCamelCase							       =			np.concatenate([input_ids, eos_tensor]			,					axis=1	)

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

            _UpperCamelCase							       =			self.config_cls(
                vocab_size=self.vocab_size			,					d_model=self.hidden_size			,					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			,					max_position_embeddings=self.max_position_embeddings			,					eos_token_ids=[2]			,					bos_token_id=self.bos_token_id			,					pad_token_id=self.pad_token_id			,					decoder_start_token_id=self.pad_token_id			,					**self.config_updates			,					)
            _UpperCamelCase							       =			prepare_pegasus_inputs_dict(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            return config, inputs_dict





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			20
            _UpperCamelCase							       =			model_class_name(lowerCAmelCase__	)

            _UpperCamelCase							       =			model.encode(inputs_dict['''input_ids''']	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            _UpperCamelCase							       =			model.init_cache(decoder_input_ids.shape[0]			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,					dtype='''i4'''	)

            _UpperCamelCase							       =			jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]			,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,					)
            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, :-1]			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					past_key_values=lowerCAmelCase__			,					decoder_position_ids=lowerCAmelCase__			,					)

            _UpperCamelCase							       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='''i4'''	)
            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, -1:]			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					past_key_values=outputs_cache.past_key_values			,					decoder_position_ids=lowerCAmelCase__			,					)

            _UpperCamelCase							       =			model.decode(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
            self.parent.assertTrue(diff < 1e-3			,					msg=f"""Max diff is {diff}"""	)







       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			20
            _UpperCamelCase							       =			model_class_name(lowerCAmelCase__	)

            _UpperCamelCase							       =			model.encode(inputs_dict['''input_ids''']	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            _UpperCamelCase							       =			jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
                ]			,					axis=-1			,					)

            _UpperCamelCase							       =			model.init_cache(decoder_input_ids.shape[0]			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]			,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,					)

            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, :-1]			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					past_key_values=lowerCAmelCase__			,					decoder_position_ids=lowerCAmelCase__			,					)
            _UpperCamelCase							       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='''i4'''	)
            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, -1:]			,					lowerCAmelCase__			,					past_key_values=outputs_cache.past_key_values			,					decoder_attention_mask=lowerCAmelCase__			,					decoder_position_ids=lowerCAmelCase__			,					)

            _UpperCamelCase							       =			model.decode(lowerCAmelCase__			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__	)

            _UpperCamelCase							       =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
            self.parent.assertTrue(diff < 1e-3			,					msg=f"""Max diff is {diff}"""	)







def    a__							(							lowercase   :	Union[str, Any],   lowercase   :	Tuple,   lowercase   :	Optional[Any],   lowercase   :	Union[str, Any]=None,   lowercase   :	Optional[Any]=None,   )						->						Any:





     """simple docstring"""

     if attention_mask is None:
          _UpperCamelCase							       =			np.not_equal(lowercase,   config.pad_token_id ).astype(np.inta )
     if decoder_attention_mask is None:
          _UpperCamelCase							       =			np.concatenate(
              [
                  np.ones(decoder_input_ids[:, :1].shape,   dtype=np.inta ),
                  np.not_equal(decoder_input_ids[:, 1:],   config.pad_token_id ).astype(np.inta ),
              ],   axis=-1,   )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
     }




@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       (
           (
               FlaxPegasusForConditionalGeneration,
               FlaxPegasusModel,
           )
           if is_flax_available()
           else ()
       )
       _snake_case      :	Optional[Any]									=       (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Optional[int]									=       False
       _snake_case      :	Any									=       False
       _snake_case      :	List[Any]									=       False





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

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxPegasusModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__	)





       def snake_case__		(				self  :						Dict	)       ->	Any:

            '''simple docstring'''




            self.config_tester.run_common_tests()





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                      @jax.jit
                      def encode_jitted(lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Dict=None			,					**lowerCAmelCase__  :						Union[str, Any]	):
                           return model.encode(input_ids=lowerCAmelCase__			,					attention_mask=lowerCAmelCase__	)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			encode_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			encode_jitted(**lowerCAmelCase__	).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__	)			,					len(lowerCAmelCase__	)	)
                      for jitted_output, output in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                           self.assertEqual(jitted_output.shape			,					output.shape	)





       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                      _UpperCamelCase							       =			model.encode(inputs_dict['''input_ids''']			,					inputs_dict['''attention_mask''']	)

                      _UpperCamelCase							       =			{
                          '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                          '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                          '''encoder_outputs''': encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Dict	):
                           return model.decode(
                               decoder_input_ids=lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					encoder_outputs=lowerCAmelCase__			,					)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			decode_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			decode_jitted(**lowerCAmelCase__	).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__	)			,					len(lowerCAmelCase__	)	)
                      for jitted_output, output in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                           self.assertEqual(jitted_output.shape			,					output.shape	)





       @slow
       def snake_case__		(				self  :						Any	)       ->	int:

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase							       =			model_class_name.from_pretrained('''google/pegasus-large'''			,					from_pt=lowerCAmelCase__	)
                 _UpperCamelCase							       =			np.ones((1, 1)	)
                 _UpperCamelCase							       =			model(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)







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

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''	)
            _UpperCamelCase							       =			PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''	)

            _UpperCamelCase							       =			[
                ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
                ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
            ]

            _UpperCamelCase							       =			[
                '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
                '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
            ]

            _UpperCamelCase							       =			tokenizer(lowerCAmelCase__			,					return_tensors='''np'''			,					truncation=lowerCAmelCase__			,					max_length=512			,					padding=lowerCAmelCase__	)
            _UpperCamelCase							       =			model.generate(**lowerCAmelCase__			,					num_beams=2	).sequences
            _UpperCamelCase							       =			tokenizer.batch_decode(lowerCAmelCase__			,					skip_special_tokens=lowerCAmelCase__	)
            assert tgt_text == decoded










'''simple docstring'''

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


lowercase__		:     List[str]			=							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						AutoencoderKL			,					lowerCAmelCase__  :						CLIPTextModel			,					lowerCAmelCase__  :						CLIPTokenizer			,					lowerCAmelCase__  :						UNetaDConditionModel			,					lowerCAmelCase__  :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,					lowerCAmelCase__  :						StableDiffusionSafetyChecker			,					lowerCAmelCase__  :						CLIPImageProcessor			,					)       ->	Optional[Any]:

            '''simple docstring'''




            super().__init__()
            self.register_modules(
                vae=lowerCAmelCase__			,					text_encoder=lowerCAmelCase__			,					tokenizer=lowerCAmelCase__			,					unet=lowerCAmelCase__			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					)





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

            '''simple docstring'''




            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 _UpperCamelCase							       =			self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCAmelCase__	)





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

            '''simple docstring'''




            self.enable_attention_slicing(lowerCAmelCase__	)







       @torch.no_grad()
       def __call__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Union[str, List[str]]			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 50			,					lowerCAmelCase__  :						float = 7.5			,					lowerCAmelCase__  :						Optional[Union[str, List[str]]] = None			,					lowerCAmelCase__  :						Optional[int] = 1			,					lowerCAmelCase__  :						float = 0.0			,					lowerCAmelCase__  :						Optional[torch.Generator] = None			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					lowerCAmelCase__  :						Optional[str] = "pil"			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,					lowerCAmelCase__  :						int = 1			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					**lowerCAmelCase__  :						Optional[int]			,					)       ->	Any:

            '''simple docstring'''




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

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

            # get prompt text embeddings
            _UpperCamelCase							       =			self.tokenizer(
                lowerCAmelCase__			,					padding='''max_length'''			,					max_length=self.tokenizer.model_max_length			,					return_tensors='''pt'''			,					)
            _UpperCamelCase							       =			text_inputs.input_ids

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

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

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			text_embeddings.shape
            _UpperCamelCase							       =			text_embeddings.repeat(1			,					lowerCAmelCase__			,					1	)
            _UpperCamelCase							       =			text_embeddings.view(bs_embed * num_images_per_prompt			,					lowerCAmelCase__			,					-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.
            _UpperCamelCase							       =			guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 _UpperCamelCase							       =			42
                 if negative_prompt is None:
                      _UpperCamelCase							       =			['''''']
                 elif type(lowerCAmelCase__	) is not type(lowerCAmelCase__	):
                      raise TypeError(
                          f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__	)} !="""
                          f""" {type(lowerCAmelCase__	)}."""	)
                 elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                      _UpperCamelCase							       =			[negative_prompt]
                 elif batch_size != len(lowerCAmelCase__	):
                      raise ValueError(
                          f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__	)}, but `prompt`:"""
                          f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                          ''' the batch size of `prompt`.'''	)
                 else:
                      _UpperCamelCase							       =			negative_prompt

                 _UpperCamelCase							       =			text_input_ids.shape[-1]
                 _UpperCamelCase							       =			self.tokenizer(
                     lowerCAmelCase__			,					padding='''max_length'''			,					max_length=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					return_tensors='''pt'''			,					)
                 _UpperCamelCase							       =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _UpperCamelCase							       =			uncond_embeddings.shape[1]
                 _UpperCamelCase							       =			uncond_embeddings.repeat(lowerCAmelCase__			,					lowerCAmelCase__			,					1	)
                 _UpperCamelCase							       =			uncond_embeddings.view(batch_size * num_images_per_prompt			,					lowerCAmelCase__			,					-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
                 _UpperCamelCase							       =			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`.
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
            _UpperCamelCase							       =			text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(self.device	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(
                          self.device	)
                 else:
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
            else:
                 if latents_reference.shape != latents_shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
                 _UpperCamelCase							       =			latents_reference.to(self.device	)
                 _UpperCamelCase							       =			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
            _UpperCamelCase							       =			(latents_shape[3] - latents_shape_reference[3]) // 2
            _UpperCamelCase							       =			(latents_shape[2] - latents_shape_reference[2]) // 2
            _UpperCamelCase							       =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
            _UpperCamelCase							       =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
            _UpperCamelCase							       =			0 if dx < 0 else dx
            _UpperCamelCase							       =			0 if dy < 0 else dy
            _UpperCamelCase							       =			max(-dx			,					0	)
            _UpperCamelCase							       =			max(-dy			,					0	)
            # import pdb
            # pdb.set_trace()
            _UpperCamelCase							       =			latents_reference[:, :, dy : dy + h, dx : dx + w]

            # set timesteps
            self.scheduler.set_timesteps(lowerCAmelCase__	)

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            _UpperCamelCase							       =			self.scheduler.timesteps.to(self.device	)

            # scale the initial noise by the standard deviation required by the scheduler
            _UpperCamelCase							       =			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]
            _UpperCamelCase							       =			'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
            _UpperCamelCase							       =			{}
            if accepts_eta:
                 _UpperCamelCase							       =			eta

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

                 # predict the noise residual
                 _UpperCamelCase							       =			self.unet(lowerCAmelCase__			,					lowerCAmelCase__			,					encoder_hidden_states=lowerCAmelCase__	).sample

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

                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCamelCase							       =			self.scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					**lowerCAmelCase__	).prev_sample

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

            _UpperCamelCase							       =			1 / 0.18215 * latents
            _UpperCamelCase							       =			self.vae.decode(lowerCAmelCase__	).sample

            _UpperCamelCase							       =			(image / 2 + 0.5).clamp(0			,					1	)

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

            if self.safety_checker is not None:
                 _UpperCamelCase							       =			self.feature_extractor(self.numpy_to_pil(lowerCAmelCase__	)			,					return_tensors='''pt'''	).to(
                     self.device	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			self.safety_checker(
                     images=lowerCAmelCase__			,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
            else:
                 _UpperCamelCase							       =			None

            if output_type == "pil":
                 _UpperCamelCase							       =			self.numpy_to_pil(lowerCAmelCase__	)

            if not return_dict:
                 return (image, has_nsfw_concept)

            return StableDiffusionPipelineOutput(images=lowerCAmelCase__			,					nsfw_content_detected=lowerCAmelCase__	)



