




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch


class 				__SCREAMING_SNAKE_CASE							:

					def __init__(				self ,       __lowercase ,       __lowercase=14 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=False ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=32 ,       __lowercase=4 ,       __lowercase=4 ,       __lowercase=[[["global", "local"], 2]] ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=7 ,       __lowercase=0.02 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Tuple							   =							seq_length
						lowerCamelCase_							:		Optional[Any]							   =							is_training
						lowerCamelCase_							:		List[Any]							   =							use_input_mask
						lowerCamelCase_							:		Dict							   =							use_token_type_ids
						lowerCamelCase_							:		int							   =							use_labels
						lowerCamelCase_							:		Dict							   =							vocab_size
						lowerCamelCase_							:		List[Any]							   =							hidden_size
						lowerCamelCase_							:		Union[str, Any]							   =							num_hidden_layers
						lowerCamelCase_							:		List[str]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							attention_types
						lowerCamelCase_							:		Tuple							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		List[str]							   =							hidden_dropout_prob
						lowerCamelCase_							:		Tuple							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		List[str]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							window_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		Tuple							   =							None
						lowerCamelCase_							:		Optional[Any]							   =							vocab_size - 1
						lowerCamelCase_							:		Any							   =							vocab_size - 1
						lowerCamelCase_							:		str							   =							vocab_size - 1

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		Optional[int]							   =							None
						if self.use_input_mask:
							lowerCamelCase_							:		Any							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							GPTNeoConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       num_layers=self.num_hidden_layers ,       num_heads=self.num_attention_heads ,       max_position_embeddings=self.max_position_embeddings ,       use_cache=__lowercase ,       bos_token_id=self.bos_token_id ,       eos_token_id=self.eos_token_id ,       pad_token_id=self.pad_token_id ,       window_size=self.window_size ,       attention_types=self.attention_types ,       )

						return (config, input_ids, input_mask)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							config_and_inputs
						lowerCamelCase_							:		Dict							   =							{'input_ids': input_ids, 'attention_mask': attention_mask}
						return config, inputs_dict

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							20
						lowerCamelCase_							:		Optional[Any]							   =							model_class_name(__lowercase					)

						lowerCamelCase_							:		List[Any]							   =							model.init_cache(input_ids.shape[0] ,       __lowercase					)
						lowerCamelCase_							:		Any							   =							jnp.ones((input_ids.shape[0], max_decoder_length) ,       dtype='i4'					)

						lowerCamelCase_							:		Union[str, Any]							   =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1					)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)					)
						lowerCamelCase_							:		List[Any]							   =							model(
						    input_ids[:, :-1] ,       attention_mask=__lowercase ,       past_key_values=__lowercase ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Any							   =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='i4'					)
						lowerCamelCase_							:		int							   =							model(
						    input_ids[:, -1:] ,       attention_mask=__lowercase ,       past_key_values=outputs_cache.past_key_values ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Optional[int]							   =							model(__lowercase					)

						lowerCamelCase_							:		Union[str, Any]							   =							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 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							20
						lowerCamelCase_							:		List[str]							   =							model_class_name(__lowercase					)

						lowerCamelCase_							:		Optional[int]							   =							jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)] ,       axis=-1 ,       )

						lowerCamelCase_							:		List[str]							   =							model.init_cache(input_ids.shape[0] ,       __lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1					)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)					)

						lowerCamelCase_							:		Tuple							   =							model(
						    input_ids[:, :-1] ,       attention_mask=__lowercase ,       past_key_values=__lowercase ,       position_ids=__lowercase ,       )
						lowerCamelCase_							:		Optional[int]							   =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='i4'					)
						lowerCamelCase_							:		Optional[Any]							   =							model(
						    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=__lowercase ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Optional[Any]							   =							model(__lowercase ,       attention_mask=__lowercase					)

						lowerCamelCase_							:		List[str]							   =							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}"					)


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					_lowerCamelCase		  = (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							FlaxGPTNeoModelTester(self					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		List[str]							   =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							GPTaTokenizer.from_pretrained('gpt2' ,       pad_token='<|endoftext|>' ,       padding_side='left'					)
						lowerCamelCase_							:		Dict							   =							tokenizer(['Hello this is a long string', 'Hey'] ,       return_tensors='np' ,       padding=__lowercase ,       truncation=__lowercase					)

						lowerCamelCase_							:		Optional[Any]							   =							FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'					)
						lowerCamelCase_							:		Union[str, Any]							   =							False
						lowerCamelCase_							:		Tuple							   =							model.config.eos_token_id

						lowerCamelCase_							:		str							   =							jax.jit(model.generate					)

						lowerCamelCase_							:		Union[str, Any]							   =							jit_generate(
						    inputs['input_ids'] ,       attention_mask=inputs['attention_mask'] ,       pad_token_id=tokenizer.pad_token_id					).sequences

						lowerCamelCase_							:		Tuple							   =							tokenizer.batch_decode(__lowercase ,       skip_special_tokens=__lowercase					)

						lowerCamelCase_							:		Dict							   =							[
						    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
						    'Hey, I\'m a little late to the party. I\'m going to',
						]

						self.assertListEqual(__lowercase ,       __lowercase					)

					@is_pt_flax_cross_test
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		List[Any]							   =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__					):
								# prepare inputs
								lowerCamelCase_							:		List[str]							   =							self._prepare_for_class(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		List[Any]							   =							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase_							:		Optional[Any]							   =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase_							:		Dict							   =							getattr(__lowercase ,       __lowercase					)

								lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							pt_inputs['input_ids'].shape
								lowerCamelCase_							:		int							   =							np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)					)
								for batch_idx, start_index in enumerate(__lowercase					):
									lowerCamelCase_							:		Optional[int]							   =							0
									lowerCamelCase_							:		List[Any]							   =							1
									lowerCamelCase_							:		Any							   =							0
									lowerCamelCase_							:		List[str]							   =							1
								lowerCamelCase_							:		Tuple							   =							pt_model_class(__lowercase					).eval()
								lowerCamelCase_							:		Tuple							   =							model_class(__lowercase ,       dtype=jnp.floataa					)

								lowerCamelCase_							:		Optional[int]							   =							convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,       __lowercase					)
								lowerCamelCase_							:		List[str]							   =							fx_state

								with torch.no_grad():
									lowerCamelCase_							:		Dict							   =							pt_model(**__lowercase					).to_tuple()

								lowerCamelCase_							:		Union[str, Any]							   =							fx_model(**__lowercase					).to_tuple()
								self.assertEqual(len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

								with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(__lowercase					)
									lowerCamelCase_							:		Dict							   =							model_class.from_pretrained(__lowercase ,       from_pt=__lowercase					)

								lowerCamelCase_							:		Optional[int]							   =							fx_model_loaded(**__lowercase					).to_tuple()
								self.assertEqual(
								    len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output_loaded, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output_loaded[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

					@is_pt_flax_cross_test
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__					):
								# prepare inputs
								lowerCamelCase_							:		Optional[int]							   =							self._prepare_for_class(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		Optional[int]							   =							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase_							:		Any							   =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase_							:		List[str]							   =							getattr(__lowercase ,       __lowercase					)

								lowerCamelCase_							:		int							   =							pt_model_class(__lowercase					).eval()
								lowerCamelCase_							:		Tuple							   =							model_class(__lowercase ,       dtype=jnp.floataa					)

								lowerCamelCase_							:		Any							   =							load_flax_weights_in_pytorch_model(__lowercase ,       fx_model.params					)
								lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							pt_inputs['input_ids'].shape
								lowerCamelCase_							:		Optional[Any]							   =							np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)					)
								for batch_idx, start_index in enumerate(__lowercase					):
									lowerCamelCase_							:		Optional[Any]							   =							0
									lowerCamelCase_							:		List[str]							   =							1
									lowerCamelCase_							:		Dict							   =							0
									lowerCamelCase_							:		str							   =							1

								# make sure weights are tied in PyTorch
								pt_model.tie_weights()

								with torch.no_grad():
									lowerCamelCase_							:		Union[str, Any]							   =							pt_model(**__lowercase					).to_tuple()

								lowerCamelCase_							:		List[Any]							   =							fx_model(**__lowercase					).to_tuple()
								self.assertEqual(len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

								with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(__lowercase					)
									lowerCamelCase_							:		str							   =							pt_model_class.from_pretrained(__lowercase ,       from_flax=__lowercase					)

								with torch.no_grad():
									lowerCamelCase_							:		Union[str, Any]							   =							pt_model_loaded(**__lowercase					).to_tuple()

								self.assertEqual(
								    len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_							:		int							   =							model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'					)
							lowerCamelCase_							:		Union[str, Any]							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


UpperCAmelCase			=    """
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = \"cuda\"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")
        ```
"""







class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       super().__init__()

       self.register_modules(
           unet=_UpperCAmelCase		,      scheduler=_UpperCAmelCase		,      )

       snake_case_       =      None



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=0					):
       if is_accelerate_available() and is_accelerate_version('''>='''		,      '''0.14.0'''					):
             from accelerate import cpu_offload
       else:
             raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''					)

       snake_case_       =      torch.device(F'''cuda:{gpu_id}'''					)

       if self.device.type != "cpu":
             self.to('''cpu'''		,      silence_dtype_warnings=_UpperCAmelCase					)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       for cpu_offloaded_model in [self.unet]:
             cpu_offload(_UpperCAmelCase		,      _UpperCAmelCase					)

       if self.safety_checker is not None:
             cpu_offload(self.safety_checker		,      execution_device=_UpperCAmelCase		,      offload_buffers=_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=0					):
       if is_accelerate_available() and is_accelerate_version('''>='''		,      '''0.17.0.dev0'''					):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''					)

       snake_case_       =      torch.device(F'''cuda:{gpu_id}'''					)

       if self.device.type != "cpu":
             self.to('''cpu'''		,      silence_dtype_warnings=_UpperCAmelCase					)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       snake_case_       =      None
       for cpu_offloaded_model in [self.unet]:
             snake_case_       ,    snake_case_       =      cpu_offload_with_hook(_UpperCAmelCase		,      _UpperCAmelCase		,      prev_module_hook=_UpperCAmelCase					)

       if self.safety_checker is not None:
             snake_case_       ,    snake_case_       =      cpu_offload_with_hook(self.safety_checker		,      _UpperCAmelCase		,      prev_module_hook=_UpperCAmelCase					)

       # We'll offload the last model manually.
       snake_case_       =      hook



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						UpperCamelCase__     (	self					):
       if not hasattr(self.unet		,      '''_hf_hook'''					):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase		,      '''_hf_hook'''					)
                 and hasattr(module._hf_hook		,      '''execution_device'''					)
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device					)
       return self.device



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       snake_case_       =      (batch_size, num_channels, height, width)
       if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) and len(_UpperCAmelCase					) != batch_size:
             raise ValueError(
                 F'''You have passed a list of generators of length {len(_UpperCAmelCase					)}, but requested an effective batch'''
                 F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

       if latents is None:
             snake_case_       =      randn_tensor(_UpperCAmelCase		,      generator=_UpperCAmelCase		,      device=_UpperCAmelCase		,      dtype=_UpperCAmelCase					)
       else:
             snake_case_       =      latents.to(device=_UpperCAmelCase		,      dtype=_UpperCAmelCase					)

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case_       =      latents * self.scheduler.init_noise_sigma
       return latents



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase = "pil"					):
       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(
                 F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''					)

       # Equivalent to diffusers.VaeImageProcessor.denormalize
       snake_case_       =      (sample / 2 + 0.5).clamp(0		,      1					)
       if output_type == "pt":
             return sample

       # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
       snake_case_       =      sample.cpu().permute(0		,      2		,      3		,      1					).numpy()
       if output_type == "np":
             return sample

       # Output_type must be 'pil'
       snake_case_       =      self.numpy_to_pil(_UpperCAmelCase					)
       return sample



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       if self.unet.config.num_class_embeds is not None:
             if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   snake_case_       =      torch.tensor(_UpperCAmelCase		,      dtype=torch.int					)
             elif isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   assert batch_size == 1, "Batch size must be 1 if classes is an int"
                   snake_case_       =      torch.tensor([class_labels]		,      dtype=torch.int					)
             elif class_labels is None:
                   # Randomly generate batch_size class labels
                   # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                   snake_case_       =      torch.randint(0		,      self.unet.config.num_class_embeds		,      size=(batch_size,)					)
             snake_case_       =      class_labels.to(_UpperCAmelCase					)
       else:
             snake_case_       =      None
       return class_labels



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       if num_inference_steps is None and timesteps is None:
             raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''					)

       if num_inference_steps is not None and timesteps is not None:
             logger.warning(
                 F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
                 ''' `timesteps` will be used over `num_inference_steps`.'''					)

       if latents is not None:
             snake_case_       =      (batch_size, 3, img_size, img_size)
             if latents.shape != expected_shape:
                   raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''					)

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





 @torch.no_grad()
 @replace_example_docstring(_UpperCAmelCase					)
 def __call__(	self		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = "pil"		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      ):
       snake_case_       =      self.unet.config.sample_size
       snake_case_       =      self._execution_device

       # 1. Check inputs
       self.check_inputs(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

       # 2. Prepare image latents
       # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
       snake_case_       =      self.prepare_latents(
           batch_size=_UpperCAmelCase		,      num_channels=self.unet.config.in_channels		,      height=_UpperCAmelCase		,      width=_UpperCAmelCase		,      dtype=self.unet.dtype		,      device=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      latents=_UpperCAmelCase		,      )

       # 3. Handle class_labels for class-conditional models
       snake_case_       =      self.prepare_class_labels(_UpperCAmelCase		,      _UpperCAmelCase		,      class_labels=_UpperCAmelCase					)

       # 4. Prepare timesteps
       if timesteps is not None:
             self.scheduler.set_timesteps(timesteps=_UpperCAmelCase		,      device=_UpperCAmelCase					)
             snake_case_       =      self.scheduler.timesteps
             snake_case_       =      len(_UpperCAmelCase					)
       else:
             self.scheduler.set_timesteps(_UpperCAmelCase					)
             snake_case_       =      self.scheduler.timesteps

       # 5. Denoising loop
       # Multistep sampling: implements Algorithm 1 in the paper
       with self.progress_bar(total=_UpperCAmelCase					) as progress_bar:
             for i, t in enumerate(_UpperCAmelCase					):
                   snake_case_       =      self.scheduler.scale_model_input(_UpperCAmelCase		,      _UpperCAmelCase					)
                   snake_case_       =      self.unet(_UpperCAmelCase		,      _UpperCAmelCase		,      class_labels=_UpperCAmelCase		,      return_dict=_UpperCAmelCase					)[0]

                   snake_case_       =      self.scheduler.step(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      generator=_UpperCAmelCase					)[0]

                   # call the callback, if provided
                   progress_bar.update()
                   if callback is not None and i % callback_steps == 0:
                         callback(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

        # 6. Post-process image sample
       snake_case_       =      self.postprocess_image(_UpperCAmelCase		,      output_type=_UpperCAmelCase					)

       # Offload last model to CPU
       if hasattr(self		,      '''final_offload_hook'''					) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase					)