


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__()

										# make sure scheduler can always be converted to DDIM
										SCREAMING_SNAKE_CASE_		:     Tuple					=					DDIMScheduler.from_config(scheduler.config       )

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






							@torch.no_grad()
							def __call__(      self			,						lowerCAmelCase__ = 1			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 0.0			,						lowerCAmelCase__ = 5_0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = "pil"			,						lowerCAmelCase__ = True			,						):





										"""simple docstring"""




										if isinstance(self.unet.config.sample_size			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Any					=					(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
										else:
													SCREAMING_SNAKE_CASE_		:     Dict					=					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) and len(lowerCAmelCase__       ) != batch_size:
													raise ValueError(
													    F'''You have passed a list of generators of length {len(lowerCAmelCase__       )}, but requested an effective batch'''
													    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					randn_tensor(lowerCAmelCase__			,						generator=lowerCAmelCase__			,						device=self.device			,						dtype=self.unet.dtype       )

										# set step values
										self.scheduler.set_timesteps(lowerCAmelCase__       )

										for t in self.progress_bar(self.scheduler.timesteps       ):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.unet(lowerCAmelCase__			,						lowerCAmelCase__       ).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.scheduler.step(
													    lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						eta=lowerCAmelCase__			,						use_clipped_model_output=lowerCAmelCase__			,						generator=lowerCAmelCase__       ).prev_sample

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(image / 2 + 0.5).clamp(0			,						1       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					image.cpu().permute(0			,						2			,						3			,						1       ).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:     str					=					self.numpy_to_pil(lowerCAmelCase__       )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__       )










'''simple docstring'''
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 				lowercase__      (				unittest.TestCase  ):





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Optional[int]												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Dict												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Any												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Any												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						List[Any]												=  FlaxBertModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[int]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	@slow
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Tuple												=  FlaxRobertaModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[Any]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'bert-base is not a local folder and is not a valid model identifier'					):
													_UpperCamelCase   :						int												=  FlaxAutoModel.from_pretrained('bert-base'					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					,revision='aaaaaa'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'					,):
													_UpperCamelCase   :						List[Any]												=  FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(lowerCamelCase__					,'Use `from_pt=True` to load this model'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'					)


