from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
     from transformers.models.albert.modeling_tf_albert import (
         TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFAlbertForMaskedLM,
         TFAlbertForMultipleChoice,
         TFAlbertForPreTraining,
         TFAlbertForQuestionAnswering,
         TFAlbertForSequenceClassification,
         TFAlbertForTokenClassification,
         TFAlbertModel,
     )







class 			SCREAMING_SNAKE_CASE__ :

       def __init__(				self :			List[str]       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			Union[str, Any]=13       ,       _lowerCAmelCase :			Tuple=7       ,       _lowerCAmelCase :			Union[str, Any]=True       ,       _lowerCAmelCase :			Optional[Any]=True       ,       _lowerCAmelCase :			str=True       ,       _lowerCAmelCase :			Dict=True       ,       _lowerCAmelCase :			int=99       ,       _lowerCAmelCase :			Optional[int]=16       ,       _lowerCAmelCase :			Dict=32       ,       _lowerCAmelCase :			List[Any]=2       ,       _lowerCAmelCase :			Tuple=4       ,       _lowerCAmelCase :			Optional[int]=37       ,       _lowerCAmelCase :			int="gelu"       ,       _lowerCAmelCase :			Dict=0.1       ,       _lowerCAmelCase :			Optional[int]=0.1       ,       _lowerCAmelCase :			Union[str, Any]=5_12       ,       _lowerCAmelCase :			int=16       ,       _lowerCAmelCase :			Dict=2       ,       _lowerCAmelCase :			Tuple=0.02       ,       _lowerCAmelCase :			Optional[int]=3       ,       _lowerCAmelCase :			Optional[Any]=4       ,       _lowerCAmelCase :			Optional[Any]=None       ,       ):
           __snake_case    :							List[str]             =  parent
           __snake_case    :							str             =  13
           __snake_case    :							Optional[int]             =  7
           __snake_case    :							int             =  True
           __snake_case    :							str             =  True
           __snake_case    :							Optional[Any]             =  True
           __snake_case    :							List[str]             =  True
           __snake_case    :							int             =  99
           __snake_case    :							List[Any]             =  16
           __snake_case    :							Dict             =  32
           __snake_case    :							Dict             =  2
           __snake_case    :							Dict             =  4
           __snake_case    :							Optional[Any]             =  37
           __snake_case    :							List[str]             =  "gelu"
           __snake_case    :							Tuple             =  0.1
           __snake_case    :							str             =  0.1
           __snake_case    :							List[Any]             =  5_12
           __snake_case    :							Tuple             =  16
           __snake_case    :							Dict             =  2
           __snake_case    :							List[Any]             =  0.02
           __snake_case    :							Tuple             =  3
           __snake_case    :							List[str]             =  4
           __snake_case    :							Tuple             =  None

       def        snake_case__  (				self :			int       ):
           __snake_case    :							Union[str, Any]             =  ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size       )

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

           __snake_case    :							List[Any]             =  None
           if self.use_token_type_ids:
               __snake_case    :							List[Any]             =  ids_tensor([self.batch_size, self.seq_length]       ,       self.type_vocab_size       )

           __snake_case    :							Optional[Any]             =  None
           __snake_case    :							Optional[Any]             =  None
           __snake_case    :							Optional[int]             =  None
           if self.use_labels:
               __snake_case    :							int             =  ids_tensor([self.batch_size]       ,       self.type_sequence_label_size       )
               __snake_case    :							Optional[int]             =  ids_tensor([self.batch_size, self.seq_length]       ,       self.num_labels       )
               __snake_case    :							str             =  ids_tensor([self.batch_size]       ,       self.num_choices       )

           __snake_case    :							Any             =  AlbertConfig(
               vocab_size=self.vocab_size       ,       hidden_size=self.hidden_size       ,       embedding_size=self.embedding_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       intermediate_size=self.intermediate_size       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,       max_position_embeddings=self.max_position_embeddings       ,       type_vocab_size=self.type_vocab_size       ,       initializer_range=self.initializer_range       ,       )

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

       def        snake_case__  (				self :			Dict       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			Optional[int]       ):
           __snake_case    :							Tuple             =  TFAlbertModel(config=_snake_case       )
           # inputs = {'input_ids': input_ids,
           #           'attention_mask': input_mask,
           #           'token_type_ids': token_type_ids}
           # sequence_output, pooled_output = model(**inputs)
           __snake_case    :							str             =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
           __snake_case    :							List[str]             =  model(_snake_case       )

           __snake_case    :							Dict             =  [input_ids, input_mask]
           __snake_case    :							Dict             =  model(_snake_case       )

           __snake_case    :							List[str]             =  model(_snake_case       )

           self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)       )
           self.parent.assertEqual(result.pooler_output.shape       ,       (self.batch_size, self.hidden_size)       )

       def        snake_case__  (				self :			Dict       ,       _lowerCAmelCase :			Tuple       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			Optional[int]       ,       _lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			Optional[Any]       ):
           __snake_case    :							Any             =  self.num_labels
           __snake_case    :							Any             =  TFAlbertForPreTraining(config=_snake_case       )
           __snake_case    :							List[Any]             =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
           __snake_case    :							List[str]             =  model(_snake_case       )
           self.parent.assertEqual(result.prediction_logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)       )
           self.parent.assertEqual(result.sop_logits.shape       ,       (self.batch_size, self.num_labels)       )

       def        snake_case__  (				self :			str       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Tuple       ,       _lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			Optional[Any]       ):
           __snake_case    :							Dict             =  TFAlbertForMaskedLM(config=_snake_case       )
           __snake_case    :							Tuple             =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
           __snake_case    :							Dict             =  model(_snake_case       )
           self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)       )

       def        snake_case__  (				self :			Tuple       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Union[str, Any]       ):
           __snake_case    :							List[Any]             =  self.num_labels
           __snake_case    :							Union[str, Any]             =  TFAlbertForSequenceClassification(config=_snake_case       )
           __snake_case    :							str             =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
           __snake_case    :							List[Any]             =  model(_snake_case       )
           self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels)       )

       def        snake_case__  (				self :			Optional[int]       ,       _lowerCAmelCase :			Tuple       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			int       ):
           __snake_case    :							Union[str, Any]             =  TFAlbertForQuestionAnswering(config=_snake_case       )
           __snake_case    :							Union[str, Any]             =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
           __snake_case    :							Any             =  model(_snake_case       )
           self.parent.assertEqual(result.start_logits.shape       ,       (self.batch_size, self.seq_length)       )
           self.parent.assertEqual(result.end_logits.shape       ,       (self.batch_size, self.seq_length)       )

       def        snake_case__  (				self :			str       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Tuple       ):
           __snake_case    :							Tuple             =  self.num_choices
           __snake_case    :							List[str]             =  TFAlbertForMultipleChoice(config=_snake_case       )
           __snake_case    :							List[Any]             =  tf.tile(tf.expand_dims(_snake_case       ,       1       )       ,       (1, self.num_choices, 1)       )
           __snake_case    :							Union[str, Any]             =  tf.tile(tf.expand_dims(_snake_case       ,       1       )       ,       (1, self.num_choices, 1)       )
           __snake_case    :							Optional[int]             =  tf.tile(tf.expand_dims(_snake_case       ,       1       )       ,       (1, self.num_choices, 1)       )
           __snake_case    :							Dict             =  {
               "input_ids": multiple_choice_inputs_ids,
               "attention_mask": multiple_choice_input_mask,
               "token_type_ids": multiple_choice_token_type_ids,
           }
           __snake_case    :							Union[str, Any]             =  model(_snake_case       )
           self.parent.assertListEqual(list(result["""logits"""].shape       )       ,       [self.batch_size, self.num_choices]       )

       def        snake_case__  (				self :			Dict       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Tuple       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			Tuple       ):
           __snake_case    :							List[Any]             =  self.num_labels
           __snake_case    :							List[str]             =  TFAlbertForTokenClassification(config=_snake_case       )
           __snake_case    :							int             =  {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __snake_case    :							List[str]             =  model(_snake_case       )
           self.parent.assertListEqual(list(result["""logits"""].shape       )       ,       [self.batch_size, self.seq_length, self.num_labels]       )





       def        snake_case__  (				self :			Union[str, Any]       ):
           __snake_case    :							List[Any]             =  self.prepare_config_and_inputs()
           (
               __snake_case 
           )    :							Union[str, Any]             =  config_and_inputs
           __snake_case    :							Optional[int]             =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
           return config, inputs_dict







@require_tf
class 			SCREAMING_SNAKE_CASE__ (   __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				unittest.TestCase  ):
       A				:     Tuple					    =     (
           (
               TFAlbertModel,
               TFAlbertForPreTraining,
               TFAlbertForMaskedLM,
               TFAlbertForSequenceClassification,
               TFAlbertForQuestionAnswering,
               TFAlbertForTokenClassification,
               TFAlbertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       A				:     List[str]					    =     (
           {
               "feature-extraction": TFAlbertModel,
               "fill-mask": TFAlbertForMaskedLM,
               "question-answering": TFAlbertForQuestionAnswering,
               "text-classification": TFAlbertForSequenceClassification,
               "token-classification": TFAlbertForTokenClassification,
               "zero-shot": TFAlbertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       A				:     List[str]					    =     False
       A				:     Dict					    =     False

       def        snake_case__  (				self :			str       ,       _lowerCAmelCase :			Optional[int]       ,       _lowerCAmelCase :			Any       ,       _lowerCAmelCase :			Tuple=False       ):
           __snake_case    :							List[Any]             =  super()._prepare_for_class(_snake_case       ,       _snake_case       ,       return_labels=_snake_case       )

           if return_labels:
               if model_class in get_values(_snake_case       ):
                   __snake_case    :							List[str]             =  tf.zeros(self.model_tester.batch_size       ,       dtype=tf.intaa       )

           return inputs_dict

       def        snake_case__  (				self :			Optional[Any]       ):
           __snake_case    :							List[str]             =  TFAlbertModelTester(self       )
           __snake_case    :							Dict             =  ConfigTester(self       ,       config_class=_snake_case       ,       hidden_size=37       )

       def        snake_case__  (				self :			int       ):
           self.config_tester.run_common_tests()

       def        snake_case__  (				self :			Dict       ):
           __snake_case    :							Optional[Any]             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_model(*_snake_case       )

       def        snake_case__  (				self :			List[str]       ):
           __snake_case    :							Any             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_pretraining(*_snake_case       )

       def        snake_case__  (				self :			Tuple       ):
           __snake_case    :							Any             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_masked_lm(*_snake_case       )

       def        snake_case__  (				self :			Optional[Any]       ):
           __snake_case    :							int             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_multiple_choice(*_snake_case       )

       def        snake_case__  (				self :			List[str]       ):
           __snake_case    :							Union[str, Any]             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_sequence_classification(*_snake_case       )

       def        snake_case__  (				self :			List[str]       ):
           __snake_case    :							Optional[int]             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_question_answering(*_snake_case       )





       @slow
       def        snake_case__  (				self :			Optional[Any]       ):
           for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __snake_case    :							Tuple             =  TFAlbertModel.from_pretrained(_snake_case       )
               self.assertIsNotNone(_snake_case       )







@require_tf
class 			SCREAMING_SNAKE_CASE__ (   unittest.TestCase  ):

       @slow
       def        snake_case__  (				self :			Optional[int]       ):
           __snake_case    :							Optional[int]             =  TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""       )
           __snake_case    :							Tuple             =  tf.constant([[0, 1, 2, 3, 4, 5]]       )
           __snake_case    :							int             =  model(_snake_case       )[0]

           __snake_case    :							Union[str, Any]             =  [1, 6, 3_00_00]
           self.assertEqual(output.shape       ,       _snake_case       )

           __snake_case    :							List[Any]             =  tf.constant(
               [
                   [
                       [4.595668, 0.74462754, -1.818147],
                       [4.5954347, 0.7454184, -1.8188258],
                       [4.5954905, 0.7448235, -1.8182316],
                   ]
               ]       )
           tf.debugging.assert_near(output[:, :3, :3]       ,       _snake_case       ,       atol=1e-4       )






'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     List[str]          =RePaintPipeline
			__A   :     List[str]          =IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			__A   :     List[Any]          =PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			__A   :     str          =IMAGE_INPAINTING_BATCH_PARAMS
			__A   :     Union[str, Any]          =False






			def 						UpperCamelCase__				(   self			):
				torch.manual_seed(0			)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, Any]      =   UNetaDModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=3       ,out_channels=3       ,down_block_types=("DownBlock2D", "AttnDownBlock2D")       ,up_block_types=("AttnUpBlock2D", "UpBlock2D")       ,)
				UpperCAmelCase_			:	Optional[int]      =   RePaintScheduler()
				UpperCAmelCase_			:	List[Any]      =   {"unet": unet, "scheduler": scheduler}
				return components






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case=0			):
				if str(_snake_case			).startswith("mps"			):
					UpperCAmelCase_			:	Optional[Any]      =   torch.manual_seed(_snake_case			)
				else:
					UpperCAmelCase_			:	Union[str, Any]      =   torch.Generator(device=_snake_case			).manual_seed(_snake_case			)
				UpperCAmelCase_			:	List[str]      =   np.random.RandomState(_snake_case			).standard_normal((1, 3, 32, 32)			)
				UpperCAmelCase_			:	str      =   torch.from_numpy(_snake_case			).to(device=_snake_case       ,dtype=torch.floataa			)
				UpperCAmelCase_			:	str      =   (image > 0).to(device=_snake_case       ,dtype=torch.floataa			)
				UpperCAmelCase_			:	str      =   {
				    "image": image,
				    "mask_image": mask,
				    "generator": generator,
				    "num_inference_steps": 5,
				    "eta": 0.0,
				    "jump_length": 2,
				    "jump_n_sample": 2,
				    "output_type": "numpy",
				}
				return inputs






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_			:	List[Any]      =   self.get_dummy_components()
				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(**_snake_case			)
				UpperCAmelCase_			:	Dict      =   sd_pipe.to(_snake_case			)
				sd_pipe.set_progress_bar_config(disable=_snake_case			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   sd_pipe(**_snake_case			).images
				UpperCAmelCase_			:	Dict      =   image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_			:	List[Any]      =   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_local()






			@unittest.skip("non-deterministic pipeline"			)
			def 						UpperCamelCase__				(   self			):
				return super().test_inference_batch_single_identical()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_optional_components()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class  _snake_case						(unittest.TestCase):






			def 						UpperCamelCase__				(   self			):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
				    "repaint/celeba_hq_256.png"			)
				UpperCAmelCase_			:	Any      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"			)
				UpperCAmelCase_			:	str      =   load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
				    "repaint/celeba_hq_256_result.npy"			)

				UpperCAmelCase_			:	Optional[Any]      =   "google/ddpm-ema-celebahq-256"
				UpperCAmelCase_			:	Optional[int]      =   UNetaDModel.from_pretrained(_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   RePaintScheduler.from_pretrained(_snake_case			)

				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(unet=_snake_case       ,scheduler=_snake_case			).to(_snake_case			)
				repaint.set_progress_bar_config(disable=_snake_case			)
				repaint.enable_attention_slicing()

				UpperCAmelCase_			:	Any      =   torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, Any]      =   repaint(
				    _snake_case       ,_snake_case       ,num_inference_steps=2_50       ,eta=0.0       ,jump_length=10       ,jump_n_sample=10       ,generator=_snake_case       ,output_type="np"       ,)
				UpperCAmelCase_			:	Union[str, Any]      =   output.images[0]

				assert image.shape == (2_56, 2_56, 3)
				assert np.abs(expected_image - image			).mean() < 1E-2




