



from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
							from transformers import (
							    AutoConfig,
							    BertConfig,
							    GPTaConfig,
							    TaConfig,
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
							from transformers import (
							    AutoModel,
							    AutoModelForCausalLM,
							    AutoModelForMaskedLM,
							    AutoModelForPreTraining,
							    AutoModelForQuestionAnswering,
							    AutoModelForSeqaSeqLM,
							    AutoModelForSequenceClassification,
							    AutoModelWithLMHead,
							    BertForMaskedLM,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    BertModel,
							    GPTaLMHeadModel,
							    RobertaForMaskedLM,
							    TaForConditionalGeneration,
							)







@is_pt_tf_cross_test
class     __lowerCAmelCase					(    unittest.TestCase		):



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



								"""simple docstring"""






								for model_name in ["bert-base-uncased"]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModel.from_pretrained(__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModel.from_pretrained(__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



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



								"""simple docstring"""






								for model_name in ["bert-base-uncased"]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelForPreTraining.from_pretrained(__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelForPreTraining.from_pretrained(__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



					@slow
					def 					_lowerCamelCase (		self     :						Optional[int])     ->					Dict:



								"""simple docstring"""






								for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelForCausalLM.from_pretrained(__a				,	from_pt=__a)
											_UpperCAmelCase	 =     TFAutoModelForCausalLM.from_pretrained(
											    __a				,	output_loading_info=__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelForCausalLM.from_pretrained(__a				,	from_tf=__a)
											_UpperCAmelCase	 =     AutoModelForCausalLM.from_pretrained(
											    __a				,	output_loading_info=__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



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



								"""simple docstring"""






								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelWithLMHead.from_pretrained(__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelWithLMHead.from_pretrained(__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



					@slow
					def 					_lowerCamelCase (		self     :						Optional[Any])     ->					str:



								"""simple docstring"""






								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelForMaskedLM.from_pretrained(__a				,	from_pt=__a)
											_UpperCAmelCase	 =     TFAutoModelForMaskedLM.from_pretrained(
											    __a				,	output_loading_info=__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelForMaskedLM.from_pretrained(__a				,	from_tf=__a)
											_UpperCAmelCase	 =     AutoModelForMaskedLM.from_pretrained(
											    __a				,	output_loading_info=__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



					@slow
					def 					_lowerCamelCase (		self     :						Optional[int])     ->					str:



								"""simple docstring"""






								for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelForSeqaSeqLM.from_pretrained(__a				,	from_pt=__a)
											_UpperCAmelCase	 =     TFAutoModelForSeqaSeqLM.from_pretrained(
											    __a				,	output_loading_info=__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelForSeqaSeqLM.from_pretrained(__a				,	from_tf=__a)
											_UpperCAmelCase	 =     AutoModelForSeqaSeqLM.from_pretrained(
											    __a				,	output_loading_info=__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



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



								"""simple docstring"""






								for model_name in ["bert-base-uncased"]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelForSequenceClassification.from_pretrained(__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelForSequenceClassification.from_pretrained(__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



					@slow
					def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



								"""simple docstring"""






								for model_name in ["bert-base-uncased"]:
											_UpperCAmelCase	 =     AutoConfig.from_pretrained(__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     TFAutoModelForQuestionAnswering.from_pretrained(__a				,	from_pt=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)

											_UpperCAmelCase	 =     AutoModelForQuestionAnswering.from_pretrained(__a				,	from_tf=__a)
											self.assertIsNotNone(__a)
											self.assertIsInstance(__a				,	__a)



					def 					_lowerCamelCase (		self     :						Dict)     ->					str:



								"""simple docstring"""






								_UpperCAmelCase	 =     TFAutoModelWithLMHead.from_pretrained(__a				,	from_pt=__a)
								self.assertIsInstance(__a				,	__a)
								self.assertEqual(model.num_parameters()				,	1_44_10)
								self.assertEqual(model.num_parameters(only_trainable=__a)				,	1_44_10)

								_UpperCAmelCase	 =     AutoModelWithLMHead.from_pretrained(__a				,	from_tf=__a)
								self.assertIsInstance(__a				,	__a)
								self.assertEqual(model.num_parameters()				,	1_44_10)
								self.assertEqual(model.num_parameters(only_trainable=__a)				,	1_44_10)



					def 					_lowerCamelCase (		self     :						Tuple)     ->					List[Any]:



								"""simple docstring"""






								_UpperCAmelCase	 =     TFAutoModelWithLMHead.from_pretrained(__a				,	from_pt=__a)
								self.assertIsInstance(__a				,	__a)
								self.assertEqual(model.num_parameters()				,	1_44_10)
								self.assertEqual(model.num_parameters(only_trainable=__a)				,	1_44_10)

								_UpperCAmelCase	 =     AutoModelWithLMHead.from_pretrained(__a				,	from_tf=__a)
								self.assertIsInstance(__a				,	__a)
								self.assertEqual(model.num_parameters()				,	1_44_10)
								self.assertEqual(model.num_parameters(only_trainable=__a)				,	1_44_10)





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  IFInpaintingPipeline
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase           =  PipelineTesterMixin.required_optional_params - {'''latents'''}



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(A)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=A).manual_seed(A)

						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)
						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)

						_UpperCAmelCase	 =     {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs



			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()				,	reason='XFormers attention is only available with CUDA and `xformers` installed'				,	)
			def 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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



						"""simple docstring"""






						self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != 'cuda'				,	reason='float16 requires CUDA')
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1E-1)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2				,	)
