import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMAEForPreTraining, ViTMAEModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class _lowercase       :





							"""simple docstring"""




							def __init__(     self  , UpperCAmelCase  , UpperCAmelCase=13  , UpperCAmelCase=30  , UpperCAmelCase=2  , UpperCAmelCase=3  , UpperCAmelCase=True  , UpperCAmelCase=True  , UpperCAmelCase=32  , UpperCAmelCase=5  , UpperCAmelCase=4  , UpperCAmelCase=37  , UpperCAmelCase="gelu"  , UpperCAmelCase=0.1  , UpperCAmelCase=0.1  , UpperCAmelCase=10  , UpperCAmelCase=0.02  , UpperCAmelCase=3  , UpperCAmelCase=0.6  , UpperCAmelCase=None  , ):







														'''simple docstring'''






														_lowercase					=						parent
														_lowercase					=						batch_size
														_lowercase					=						image_size
														_lowercase					=						patch_size
														_lowercase					=						num_channels
														_lowercase					=						is_training
														_lowercase					=						use_labels
														_lowercase					=						hidden_size
														_lowercase					=						num_hidden_layers
														_lowercase					=						num_attention_heads
														_lowercase					=						intermediate_size
														_lowercase					=						hidden_act
														_lowercase					=						hidden_dropout_prob
														_lowercase					=						attention_probs_dropout_prob
														_lowercase					=						type_sequence_label_size
														_lowercase					=						initializer_range
														_lowercase					=						mask_ratio
														_lowercase					=						scope

														# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
														# (we add 1 for the [CLS] token)
														_lowercase					=						(image_size // patch_size) ** 2
														_lowercase					=						int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

														_lowercase					=						None
														if self.use_labels:
																					_lowercase					=						ids_tensor([self.batch_size]  , self.type_sequence_label_size	)

														_lowercase					=						self.get_config()

														return config, pixel_values, labels



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return ViTMAEConfig(
														    image_size=self.image_size  , patch_size=self.patch_size  , num_channels=self.num_channels  , hidden_size=self.hidden_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  , is_decoder=lowercase_  , initializer_range=self.initializer_range  , mask_ratio=self.mask_ratio  , )



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						ViTMAEModel(config=lowercase_	)
														model.to(lowercase_	)
														model.eval()
														_lowercase					=						model(lowercase_	)
														self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size)	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						ViTMAEForPreTraining(lowercase_	)
														model.to(lowercase_	)
														model.eval()
														_lowercase					=						model(lowercase_	)
														_lowercase					=						(self.image_size // self.patch_size) ** 2
														_lowercase					=						self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape  , (self.batch_size, num_patches, expected_num_channels)	)

														# test greyscale images
														_lowercase					=						1
														_lowercase					=						ViTMAEForPreTraining(lowercase_	)
														model.to(lowercase_	)
														model.eval()
														_lowercase					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
														_lowercase					=						model(lowercase_	)
														_lowercase					=						self.patch_size**2
														self.parent.assertEqual(result.logits.shape  , (self.batch_size, num_patches, expected_num_channels)	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.prepare_config_and_inputs()
														_lowercase					,							_lowercase					,							_lowercase					=						config_and_inputs
														_lowercase					=						{"""pixel_values""": pixel_values}
														return config, inputs_dict






@require_torch
class _lowercase       (							_lowerCamelCase ,					_lowerCamelCase ,					unittest.TestCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							lowerCAmelCase__							=       {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

							lowerCAmelCase__							=       False
							lowerCAmelCase__							=       False
							lowerCAmelCase__							=       False
							lowerCAmelCase__							=       False



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						ViTMAEModelTester(self	)
														_lowercase					=						ConfigTester(self  , config_class=lowercase_  , has_text_modality=lowercase_  , hidden_size=37	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														self.config_tester.run_common_tests()



							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""	)
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					,							_lowercase					=						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowercase					=						model_class(lowercase_	)
																					self.assertIsInstance(model.get_input_embeddings()  , (nn.Module)	)
																					_lowercase					=						model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowercase_  , nn.Linear	)	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					,							_lowercase					=						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowercase					=						model_class(lowercase_	)
																					_lowercase					=						inspect.signature(model.forward	)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					_lowercase					=						[*signature.parameters.keys()]

																					_lowercase					=						["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]  , lowercase_	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowercase_	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*lowercase_	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase	):







														'''simple docstring'''






														np.random.seed(2	)

														_lowercase					=						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
														_lowercase					=						np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
														_lowercase					=						torch.from_numpy(lowercase_	)

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														_lowercase					=						pt_noise

														super().check_pt_tf_models(lowercase_  , lowercase_  , lowercase_	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					,							_lowercase					=						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowercase					=						model_class(lowercase_	)
																					model.to(lowercase_	)
																					model.eval()
																					# make random mask reproducible
																					torch.manual_seed(2	)
																					with torch.no_grad():
																												_lowercase					=						model(**self._prepare_for_class(lowercase_  , lowercase_	)	)

																					_lowercase					=						outputs[0].cpu().numpy()
																					_lowercase					=						0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(lowercase_	)
																												_lowercase					=						model_class.from_pretrained(lowercase_	)
																												model.to(lowercase_	)
																												# make random mask reproducible
																												torch.manual_seed(2	)
																												with torch.no_grad():
																																			_lowercase					=						model(**self._prepare_for_class(lowercase_  , lowercase_	)	)

																												# Make sure we don't have nans
																												_lowercase					=						after_outputs[0].cpu().numpy()
																												_lowercase					=						0
																												_lowercase					=						np.amax(np.abs(out_a - out_a	)	)
																												self.assertLessEqual(lowercase_  , 1e-5	)



							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""	)
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass



							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""	)
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass



							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""	)
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass



							@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""	)
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass



							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														pass



							@slow
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowercase					=						ViTMAEModel.from_pretrained(lowercase_	)
																					self.assertIsNotNone(lowercase_	)






def        __lowerCAmelCase      (	):



							"""simple docstring"""


							_lowercase					=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image







@require_torch
@require_vision
class _lowercase       (							unittest.TestCase    ):





							"""simple docstring"""




							@cached_property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""	) if is_vision_available() else None



							@slow
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														np.random.seed(2	)

														_lowercase					=						ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""	).to(lowercase_	)

														_lowercase					=						self.default_image_processor
														_lowercase					=						prepare_img()
														_lowercase					=						image_processor(images=lowercase_  , return_tensors="""pt"""	).to(lowercase_	)

														# prepare a noise vector that will be also used for testing the TF model
														# (this way we can ensure that the PT and TF models operate on the same inputs)
														_lowercase					=						ViTMAEConfig()
														_lowercase					=						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
														_lowercase					=						np.random.uniform(size=(1, num_patches)	)

														# forward pass
														with torch.no_grad():
																					_lowercase					=						model(**lowercase_  , noise=torch.from_numpy(lowercase_	).to(device=lowercase_	)	)

														# verify the logits
														_lowercase					=						torch.Size((1, 196, 768)	)
														self.assertEqual(outputs.logits.shape  , lowercase_	)

														_lowercase					=						torch.tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]	)

														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  , expected_slice.to(lowercase_	)  , atol=1e-4	)	)








'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
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 numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)






class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   int			,							lowercase_       :   str=13			,							lowercase_       :   Union[str, Any]=7			,							lowercase_       :   Any=True			,							lowercase_       :   List[str]=True			,							lowercase_       :   Tuple=True			,							lowercase_       :   List[Any]=True			,							lowercase_       :   str=99			,							lowercase_       :   int=32			,							lowercase_       :   int=2			,							lowercase_       :   Any=4			,							lowercase_       :   Union[str, Any]=37			,							lowercase_       :   Optional[int]="gelu"			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Optional[Any]=0.1			,							lowercase_       :   Union[str, Any]=512			,							lowercase_       :   Dict=16			,							lowercase_       :   int=2			,							lowercase_       :   Any=0.02			,							lowercase_       :   int=3			,							lowercase_       :   Any=4			,							lowercase_       :   List[Any]=None			,							lowercase_       :   List[str]=0			,							):
									snake_case_		     =							parent
									snake_case_		     =							batch_size
									snake_case_		     =							seq_length
									snake_case_		     =							is_training
									snake_case_		     =							use_input_mask
									snake_case_		     =							use_token_type_ids
									snake_case_		     =							use_labels
									snake_case_		     =							vocab_size
									snake_case_		     =							hidden_size
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							num_attention_heads
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							hidden_dropout_prob
									snake_case_		     =							attention_probs_dropout_prob
									snake_case_		     =							max_position_embeddings
									snake_case_		     =							type_vocab_size
									snake_case_		     =							type_sequence_label_size
									snake_case_		     =							initializer_range
									snake_case_		     =							num_labels
									snake_case_		     =							num_choices
									snake_case_		     =							scope
									snake_case_		     =							projection_dim


				def     A_					(						self       :   int   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )

									snake_case_		     =							None
									if self.use_input_mask:
														# follow test_modeling_tf_ctrl.py
														snake_case_		     =							random_attention_mask([self.batch_size, self.seq_length]   )

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

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

									snake_case_		     =							BertConfig(
									    vocab_size=self.vocab_size			,							hidden_size=self.hidden_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			,							is_decoder=lowercase_			,							initializer_range=self.initializer_range			,							)
									snake_case_		     =							DPRConfig(projection_dim=self.projection_dim			,							**config.to_dict()   )

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


				def     A_					(						self       :   Dict			,							lowercase_       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   str   ):
									snake_case_		     =							TFDPRContextEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Any   ):
									snake_case_		     =							TFDPRQuestionEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									snake_case_		     =							TFDPRReader(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_   )

									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)   )
									self.parent.assertEqual(result.relevance_logits.shape			,							(self.batch_size,)   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,
									)		     =							config_and_inputs
									snake_case_		     =							{'''input_ids''': input_ids}
									return config, inputs_dict







@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        TFDPRContextEncoder,
				        TFDPRQuestionEncoder,
				        TFDPRReader,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_           =						{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							TFDPRModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   Any   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_context_encoder(*lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_question_encoder(*lowercase_   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_reader(*lowercase_   )


				@slow
				def     A_					(						self       :   int   ):
									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRQuestionEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRReader.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







@require_tf
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   Dict   ):
									snake_case_		     =							TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''   )

									snake_case_		     =							tf.constant(
									    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]   )  # [CLS] hello, is my dog cute? [SEP]
									snake_case_		     =							model(lowercase_   )[0]  # embedding shape = (1, 768)
									# compare the actual values for a slice.
									snake_case_		     =							tf.constant(
									    [
									        [
									            0.0323_6253,
									            0.1275_3335,
									            0.1681_8509,
									            0.0027_9786,
									            0.389_6933,
									            0.2426_4945,
									            0.217_8971,
									            -0.0233_5227,
									            -0.0848_1959,
									            -0.1432_4117,
									        ]
									    ]   )
									self.assertTrue(numpy.allclose(output[:, :10].numpy()			,							expected_slice.numpy()			,							atol=1e-4   )   )

