






import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
		from transformers import (
		    FlaxBertModel,
		    FlaxCLIPVisionModel,
		    FlaxVisionTextDualEncoderModel,
		    FlaxViTModel,
		    VisionTextDualEncoderConfig,
		    VisionTextDualEncoderProcessor,
		)
		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)


if is_torch_available():
		import torch

		from transformers import VisionTextDualEncoderModel

if is_vision_available():
		from PIL import Image




def 					lowercase__				(					__snake_case   :							Dict  ):


					'''simple docstring'''


					if isinstance(__snake_case					,	collections.abc.Iterable  ):
										return x
					return (x, x)



@require_flax
class   lowerCamelCase				:



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Dict:
							pass



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							pass



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							pass



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				Tuple						=						np.abs((a - b)       ).max()
							self.assertLessEqual(_UpperCamelCase    ,		_UpperCamelCase    ,		f"Difference between torch and flax is {diff} (>= {tol})."       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				Dict						=						VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				int						=						FlaxVisionTextDualEncoderModel(_UpperCamelCase       )

							UpperCAmelCase_   :				int						=						model(input_ids=_UpperCamelCase    ,		pixel_values=_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )

							self.assertEqual(output['text_embeds'].shape    ,		(input_ids.shape[0], config.projection_dim)       )
							self.assertEqual(output['image_embeds'].shape    ,		(pixel_values.shape[0], config.projection_dim)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				str						=						self.get_vision_text_model(_UpperCamelCase    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						{'vision_model': vision_model, 'text_model': text_model}
							UpperCAmelCase_   :				List[Any]						=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						model(input_ids=_UpperCamelCase    ,		pixel_values=_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )

							self.assertEqual(output['text_embeds'].shape    ,		(input_ids.shape[0], model.config.projection_dim)       )
							self.assertEqual(output['image_embeds'].shape    ,		(pixel_values.shape[0], model.config.projection_dim)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Tuple						=						self.get_vision_text_model(_UpperCamelCase    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						{'vision_model': vision_model, 'text_model': text_model}
							UpperCAmelCase_   :				Union[str, Any]						=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						model(input_ids=_UpperCamelCase    ,		pixel_values=_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						output[0]

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCamelCase       )

												UpperCAmelCase_   :				List[Any]						=						model(input_ids=_UpperCamelCase    ,		pixel_values=_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						after_output[0]
												UpperCAmelCase_   :				Optional[int]						=						np.amax(np.abs(out_a - out_a       )       )
												self.assertLessEqual(_UpperCamelCase    ,		1E-3       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Any						=						self.get_vision_text_model(_UpperCamelCase    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						{'vision_model': vision_model, 'text_model': text_model}
							UpperCAmelCase_   :				Dict						=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						model(
							    input_ids=_UpperCamelCase    ,		pixel_values=_UpperCamelCase    ,		attention_mask=_UpperCamelCase    ,		output_attentions=_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						output.vision_model_output.attentions
							self.assertEqual(len(_UpperCamelCase       )    ,		vision_config.num_hidden_layers       )

							# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
							UpperCAmelCase_   :				Optional[int]						=						to_atuple(vision_model.config.image_size       )
							UpperCAmelCase_   :				Dict						=						to_atuple(vision_model.config.patch_size       )
							UpperCAmelCase_   :				Union[str, Any]						=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							UpperCAmelCase_   :				List[str]						=						num_patches + 1
							self.assertEqual(vision_attentions[0].shape[-3:]    ,		(vision_config.num_attention_heads, seq_len, seq_len)       )

							UpperCAmelCase_   :				Any						=						output.text_model_output.attentions
							self.assertEqual(len(_UpperCamelCase       )    ,		text_config.num_hidden_layers       )

							self.assertEqual(
							    text_attentions[0].shape[-3:]    ,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							pt_model.to(_UpperCamelCase       )
							pt_model.eval()

							# prepare inputs
							UpperCAmelCase_   :				str						=						inputs_dict
							UpperCAmelCase_   :				Dict						=						{k: torch.tensor(v.tolist()       ) for k, v in flax_inputs.items()}

							with torch.no_grad():
												UpperCAmelCase_   :				Tuple						=						pt_model(**_UpperCamelCase       ).to_tuple()

							UpperCAmelCase_   :				int						=						fx_model(**_UpperCamelCase       ).to_tuple()
							self.assertEqual(len(_UpperCamelCase       )    ,		len(_UpperCamelCase       )    ,		'Output lengths differ between Flax and PyTorch'       )
							for fx_output, pt_output in zip(fx_outputs[:4]    ,		pt_outputs[:4]       ):
												self.assert_almost_equals(_UpperCamelCase    ,		pt_output.numpy()    ,		4E-2       )

							# PT -> Flax
							with tempfile.TemporaryDirectory() as tmpdirname:
												pt_model.save_pretrained(_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCamelCase    ,		from_pt=_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						fx_model_loaded(**_UpperCamelCase       ).to_tuple()
							self.assertEqual(len(_UpperCamelCase       )    ,		len(_UpperCamelCase       )    ,		'Output lengths differ between Flax and PyTorch'       )
							for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]    ,		pt_outputs[:4]       ):
												self.assert_almost_equals(_UpperCamelCase    ,		pt_output.numpy()    ,		4E-2       )

							# Flax -> PT
							with tempfile.TemporaryDirectory() as tmpdirname:
												fx_model.save_pretrained(_UpperCamelCase       )
												UpperCAmelCase_   :				int						=						VisionTextDualEncoderModel.from_pretrained(_UpperCamelCase    ,		from_flax=_UpperCamelCase       )

							pt_model_loaded.to(_UpperCamelCase       )
							pt_model_loaded.eval()

							with torch.no_grad():
												UpperCAmelCase_   :				Optional[int]						=						pt_model_loaded(**_UpperCamelCase       ).to_tuple()

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Dict						=						VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				int						=						VisionTextDualEncoderModel(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						FlaxVisionTextDualEncoderModel(_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						fx_state

							self.check_pt_flax_equivalence(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Any						=						VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						VisionTextDualEncoderModel(_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						FlaxVisionTextDualEncoderModel(_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						load_flax_weights_in_pytorch_model(_UpperCamelCase    ,		fx_model.params       )

							self.check_pt_flax_equivalence(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				List[Any]						=						self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Any						=						self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Optional[Any]						=						self.prepare_config_and_inputs()
							self.check_save_load(**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Optional[Any]						=						self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**_UpperCamelCase       )



		@is_pt_flax_cross_test
		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Tuple						=						self.prepare_config_and_inputs()
							UpperCAmelCase_   :				Optional[int]						=						config_inputs_dict.pop('vision_config'       )
							UpperCAmelCase_   :				int						=						config_inputs_dict.pop('text_config'       )

							UpperCAmelCase_   :				str						=						config_inputs_dict

							self.check_equivalence_pt_to_flax(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							self.check_equivalence_flax_to_pt(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Tuple						=						self.get_pretrained_model_and_inputs()

							UpperCAmelCase_   :				List[Any]						=						model_a(**_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						outputs[0]

							with tempfile.TemporaryDirectory() as tmp_dirname:
												model_a.save_pretrained(_UpperCamelCase       )
												UpperCAmelCase_   :				Tuple						=						FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCamelCase       )

												UpperCAmelCase_   :				Optional[Any]						=						model_a(**_UpperCamelCase       )
												UpperCAmelCase_   :				Any						=						after_outputs[0]
												UpperCAmelCase_   :				Tuple						=						np.amax(np.abs(out_a - out_a       )       )
												self.assertLessEqual(_UpperCamelCase    ,		1E-5       )





@require_flax
class   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Union[str, Any]						=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    'hf-internal-testing/tiny-random-vit'    ,		'hf-internal-testing/tiny-bert'    ,		vision_from_pt=_UpperCamelCase    ,		text_from_pt=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Tuple						=						1_3
							UpperCAmelCase_   :				Optional[int]						=						floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]       )
							UpperCAmelCase_   :				int						=						ids_tensor([batch_size, 4]    ,		model.config.text_config.vocab_size       )
							UpperCAmelCase_   :				Any						=						random_attention_mask([batch_size, 4]       )
							UpperCAmelCase_   :				Any						=						{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

							return model, inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						FlaxViTModel(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						FlaxBertModel(_UpperCamelCase       )
							return vision_model, text_model



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[str]						=						FlaxViTModelTester(self       )
							UpperCAmelCase_   :				Optional[int]						=						FlaxBertModelTester(self       )
							UpperCAmelCase_   :				Optional[int]						=						vit_model_tester.prepare_config_and_inputs()
							UpperCAmelCase_   :				Union[str, Any]						=						bert_model_tester.prepare_config_and_inputs()

							UpperCAmelCase_   :				Optional[Any]						=						vision_config_and_inputs

							UpperCAmelCase_   :				List[str]						=						text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}





@require_torch
class   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[Any]						=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    'hf-internal-testing/tiny-random-clip'    ,		'hf-internal-testing/tiny-bert'    ,		vision_from_pt=_UpperCamelCase    ,		text_from_pt=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Tuple						=						1_3
							UpperCAmelCase_   :				List[Any]						=						floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]       )
							UpperCAmelCase_   :				Dict						=						ids_tensor([batch_size, 4]    ,		model.config.text_config.vocab_size       )
							UpperCAmelCase_   :				Tuple						=						random_attention_mask([batch_size, 4]       )
							UpperCAmelCase_   :				Union[str, Any]						=						{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

							return model, inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				Dict						=						FlaxCLIPVisionModel(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						FlaxBertModel(_UpperCamelCase       )
							return vision_model, text_model



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				int						=						FlaxCLIPVisionModelTester(self       )
							UpperCAmelCase_   :				Any						=						FlaxBertModelTester(self       )
							UpperCAmelCase_   :				Dict						=						clip_model_tester.prepare_config_and_inputs()
							UpperCAmelCase_   :				str						=						bert_model_tester.prepare_config_and_inputs()

							UpperCAmelCase_   :				List[str]						=						vision_config_and_inputs

							UpperCAmelCase_   :				Union[str, Any]						=						text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}





@require_flax
@require_vision
class   lowerCamelCase				(unittest.TestCase      ):



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				int						=						FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'    ,		logit_scale_init_value=1.0       )
							UpperCAmelCase_   :				List[str]						=						VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'       )

							UpperCAmelCase_   :				Dict						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
							UpperCAmelCase_   :				str						=						processor(
							    text=['una foto di un gatto', 'una foto di un cane']    ,		images=_UpperCamelCase    ,		padding=_UpperCamelCase    ,		return_tensors='np'       )

							UpperCAmelCase_   :				List[str]						=						model(**_UpperCamelCase       )

							# verify the logits
							self.assertEqual(outputs.logits_per_image.shape    ,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])       )
							self.assertEqual(
							    outputs.logits_per_text.shape    ,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])    ,		)

							UpperCAmelCase_   :				Dict						=						np.array([[1.2_28_47_27, 0.3_10_41_22]]       )

							self.assertTrue(np.allclose(outputs.logits_per_image    ,		_UpperCamelCase    ,		atol=1E-3       )       )














import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase     							=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase     							=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir    ,		'schedulers/'       )       )
							UpperCAmelCase_   :				int						=						self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase    ,		'src/diffusers/schedulers/scheduling_ddpm.py'       )    ,		os.path.join(self.diffusers_dir    ,		'schedulers/scheduling_ddpm.py'       )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						'src/diffusers'
							shutil.rmtree(self.diffusers_dir       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
												UpperCAmelCase_   :				Optional[Any]						=						comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							UpperCAmelCase_   :				Any						=						black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=1_1_9       )
							UpperCAmelCase_   :				Optional[Any]						=						black.format_str(_UpperCamelCase    ,		mode=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						os.path.join(self.diffusers_dir    ,		'new_code.py'       )
							with open(_UpperCamelCase    ,		'w'    ,		newline='\n'       ) as f:
												f.write(_UpperCamelCase       )
							if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase       )       ) == 0       )
							else:
												check_copies.is_copy_consistent(f.name    ,		overwrite=_UpperCamelCase       )
												with open(_UpperCamelCase    ,		'r'       ) as f:
																	self.assertTrue(f.read()    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		REFERENCE_CODE + '\n'    ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		_UpperCamelCase    ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with a really long name
							UpperCAmelCase_   :				Optional[int]						=						'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"    ,		f"{long_class_name}SchedulerOutput"    ,		re.sub('Bert'    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		_UpperCamelCase    ,		overwrite_result=re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)






