





'''simple docstring'''







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     __a					(lowerCamelCase   ):

							def __init__(							self							:   List[Any]				,   __magic_name__							:   List[Any]				,   __magic_name__							:   List[str]      )    ->				Optional[int]:







													"""simple docstring"""
													super().__init__()

													# make sure scheduler can always be converted to DDIM
													UpperCAmelCase_	:					List[str]     =     DDIMScheduler.from_config(scheduler.config      )

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



							@torch.no_grad()
							def __call__(							self							:   Optional[Any]				,   __magic_name__							:   List[str] = 1				,   __magic_name__							:   str = None				,   __magic_name__							:   List[str] = 0.0				,   __magic_name__							:   str = 50				,   __magic_name__							:   Optional[int] = None				,   __magic_name__							:   Union[str, Any] = "pil"				,   __magic_name__							:   Optional[int] = True				,   )    ->				Union[ImagePipelineOutput, Tuple]:







													"""simple docstring"""




													if isinstance(self.unet.config.sample_size				,   _snake_case      ):
																			UpperCAmelCase_	:					Optional[int]     =     (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			UpperCAmelCase_	:					Optional[int]     =     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

													UpperCAmelCase_	:					Optional[int]     =     randn_tensor(_snake_case				,   generator=_snake_case				,   device=self.device				,   dtype=self.unet.dtype      )

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

													for t in self.progress_bar(self.scheduler.timesteps      ):
																			# 1. predict noise model_output
																			UpperCAmelCase_	:					Union[str, Any]     =     self.unet(_snake_case				,   _snake_case      ).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
																			UpperCAmelCase_	:					int     =     self.scheduler.step(
																			    _snake_case				,   _snake_case				,   _snake_case				,   eta=_snake_case				,   use_clipped_model_output=_snake_case				,   generator=_snake_case      ).prev_sample

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

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=_snake_case      )







import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
					import os

					from flax.core.frozen_dict import unfreeze
					from flax.traverse_util import flatten_dict

					from transformers import FlaxBertModel

					A     :	int    = '0.12'  # assumed parallelism: 8







@require_flax
@is_staging_test
class 			__A( unittest.TestCase			):




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    TOKEN
								HfFolder.save_token(_snake_case				)




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Union[str, Any]:


								'''simple docstring'''




								try:
												delete_repo(token=cls._token		,     repo_id='''test-model-flax'''				)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token		,     repo_id='''valid_org/test-model-flax-org'''				)
								except HTTPError:
												pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    BertConfig(
								    vocab_size=99		,     hidden_size=32		,     num_hidden_layers=5		,     num_attention_heads=4		,     intermediate_size=37				)
								__a										=    FlaxBertModel(_snake_case				)
								model.push_to_hub('''test-model-flax'''		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)

								# Reset repo
								delete_repo(token=self._token		,     repo_id='''test-model-flax'''				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(_snake_case		,     repo_id='''test-model-flax'''		,     push_to_hub=_snake_case		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)




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


								'''simple docstring'''




								__a										=    BertConfig(
								    vocab_size=99		,     hidden_size=32		,     num_hidden_layers=5		,     num_attention_heads=4		,     intermediate_size=37				)
								__a										=    FlaxBertModel(_snake_case				)
								model.push_to_hub('''valid_org/test-model-flax-org'''		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)

								# Reset repo
								delete_repo(token=self._token		,     repo_id='''valid_org/test-model-flax-org'''				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(
												    _snake_case		,     repo_id='''valid_org/test-model-flax-org'''		,     push_to_hub=_snake_case		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)



def     __lowerCAmelCase			(			a__						,    a__  )       ->  str:
				__a										=    True
				__a										=    flatten_dict(modela.params  )
				__a										=    flatten_dict(modela.params  )
				for key in flat_params_a.keys():
								if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]  )  ) > 1e-4:
												__a										=    False

				return models_are_equal







@require_flax
class 			__A( unittest.TestCase			):




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


								'''simple docstring'''




								__a										=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)
								__a										=    FlaxBertModel(_snake_case				)

								__a										=    '''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(_snake_case		,     _snake_case				)				)

												with self.assertRaises(_snake_case				):
																__a										=    FlaxBertModel.from_pretrained(_snake_case				)

												__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertTrue(check_models_equal(_snake_case		,     _snake_case				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)
								__a										=    FlaxBertModel(_snake_case				)

								__a										=    '''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(_snake_case		,     _snake_case				)		,     max_shard_size='''10KB'''				)

												with self.assertRaises(_snake_case				):
																__a										=    FlaxBertModel.from_pretrained(_snake_case				)

												__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertTrue(check_models_equal(_snake_case		,     _snake_case				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    '''bert'''
								__a										=    '''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(_snake_case				):
												__a										=    FlaxBertModel.from_pretrained(_snake_case				)

								__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertIsNotNone(_snake_case				)




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


								'''simple docstring'''




								__a										=    '''bert'''
								__a										=    '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
								with self.assertRaises(_snake_case				):
												__a										=    FlaxBertModel.from_pretrained(_snake_case				)

								__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertIsNotNone(_snake_case				)