



'''simple docstring'''







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
		from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor

class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

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




								'''simple docstring'''





								lowerCAmelCase__  =   self.config_class(**self.inputs_dict					)
								self.parent.assertTrue(hasattr(__A   ,		"""hidden_sizes"""					)					)
								self.parent.assertTrue(hasattr(__A   ,		"""num_attention_heads"""					)					)
								self.parent.assertTrue(hasattr(__A   ,		"""num_encoder_blocks"""					)					)





class      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Dict   ,		__A		:  Any=13   ,		__A		:  str=64   ,		__A		:  Any=3   ,		__A		:  Any=4   ,		__A		:  str=[2, 2, 2, 2]   ,		__A		:  str=[8, 4, 2, 1]   ,		__A		:  Union[str, Any]=[16, 32, 64, 128]   ,		__A		:  List[str]=[1, 4, 8, 16]   ,		__A		:  Tuple=[1, 2, 4, 8]   ,		__A		:  Tuple=True   ,		__A		:  List[str]=True   ,		__A		:  Optional[int]="gelu"   ,		__A		:  List[str]=0.1   ,		__A		:  List[str]=0.1   ,		__A		:  int=0.0_2   ,		__A		:  Optional[Any]=16   ,		__A		:  int=3   ,		__A		:  str=None   ,		)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   num_encoder_blocks
								lowerCAmelCase__  =   sr_ratios
								lowerCAmelCase__  =   depths
								lowerCAmelCase__  =   hidden_sizes
								lowerCAmelCase__  =   downsampling_rates
								lowerCAmelCase__  =   num_attention_heads
								lowerCAmelCase__  =   is_training
								lowerCAmelCase__  =   use_labels
								lowerCAmelCase__  =   hidden_act
								lowerCAmelCase__  =   hidden_dropout_prob
								lowerCAmelCase__  =   attention_probs_dropout_prob
								lowerCAmelCase__  =   initializer_range
								lowerCAmelCase__  =   decoder_hidden_size
								lowerCAmelCase__  =   num_labels
								lowerCAmelCase__  =   scope




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




								'''simple docstring'''





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

								lowerCAmelCase__  =   None
								if self.use_labels:
															lowerCAmelCase__  =   ids_tensor([self.batch_size, self.image_size, self.image_size]   ,		self.num_labels					)

								lowerCAmelCase__  =   self.get_config()
								return config, pixel_values, labels




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




								'''simple docstring'''





								return GLPNConfig(
								    image_size=self.image_size   ,		num_channels=self.num_channels   ,		num_encoder_blocks=self.num_encoder_blocks   ,		depths=self.depths   ,		hidden_sizes=self.hidden_sizes   ,		num_attention_heads=self.num_attention_heads   ,		hidden_act=self.hidden_act   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		initializer_range=self.initializer_range   ,		decoder_hidden_size=self.decoder_hidden_size   ,		)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  Optional[Any]   ,		__A		:  Union[str, Any]   ,		__A		:  Dict					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   GLPNModel(config=__A					)
								model.to(__A					)
								model.eval()
								lowerCAmelCase__  =   model(__A					)
								lowerCAmelCase__  =   lowerCAmelCase__  =   self.image_size // (self.downsampling_rates[-1] * 2)
								self.parent.assertEqual(
								    result.last_hidden_state.shape   ,		(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)




	def      lowercase__							(  self		:  List[Any]   ,		__A		:  List[str]   ,		__A		:  Any   ,		__A		:  Optional[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   GLPNForDepthEstimation(__A					)
								model.to(__A					)
								model.eval()
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertEqual(result.predicted_depth.shape   ,		(self.batch_size, self.image_size, self.image_size)					)
								lowerCAmelCase__  =   model(__A   ,		labels=__A					)
								self.parent.assertEqual(result.predicted_depth.shape   ,		(self.batch_size, self.image_size, self.image_size)					)




	def      lowercase__							(  self		:  int					)     ->			List[str]:




								'''simple docstring'''





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





@require_torch
class      lowerCamelCase__     (     _A,     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
	A__		=	(
	    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
	)

	A__		=	False
	A__		=	False
	A__		=	False




	def      lowercase__							(  self		:  List[str]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   GLPNModelTester(self					)
								lowerCAmelCase__  =   GLPNConfigTester(self   ,		config_class=__A					)




	def      lowercase__							(  self		:  str					)     ->			Optional[int]:




								'''simple docstring'''





								self.config_tester.run_common_tests()




	def      lowercase__							(  self		:  Optional[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__A					)




	def      lowercase__							(  self		:  List[Any]					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*__A					)




	@unittest.skip("""GLPN does not use inputs_embeds"""					)
	def      lowercase__							(  self		:  Optional[int]					)     ->			List[Any]:




								'''simple docstring'''





								pass




	@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""					)
	def      lowercase__							(  self		:  List[Any]					)     ->			List[str]:




								'''simple docstring'''





								pass




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




								'''simple docstring'''





								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCAmelCase__  =   ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]   ,		__A					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__  =   True

								for model_class in self.all_model_classes:
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   False
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()
															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)
															lowerCAmelCase__  =   outputs.attentions

															lowerCAmelCase__  =   sum(self.model_tester.depths					)
															self.assertEqual(len(__A					)   ,		__A					)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()
															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)
															lowerCAmelCase__  =   outputs.attentions

															self.assertEqual(len(__A					)   ,		__A					)

															# verify the first attentions (first block, first layer)
															lowerCAmelCase__  =   (self.model_tester.image_size // 4) ** 2
															lowerCAmelCase__  =   (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
															self.assertListEqual(
															    list(attentions[0].shape[-3:]					)   ,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,		)

															# verify the last attentions (last block, last layer)
															lowerCAmelCase__  =   (self.model_tester.image_size // 32) ** 2
															lowerCAmelCase__  =   (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
															self.assertListEqual(
															    list(attentions[-1].shape[-3:]					)   ,		[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,		)
															lowerCAmelCase__  =   len(__A					)

															# Check attention is always last and order is fine
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()
															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)

															self.assertEqual(out_len + 1   ,		len(__A					)					)

															lowerCAmelCase__  =   outputs.attentions

															self.assertEqual(len(__A					)   ,		__A					)
															# verify the first attentions (first block, first layer)
															lowerCAmelCase__  =   (self.model_tester.image_size // 4) ** 2
															lowerCAmelCase__  =   (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]					)   ,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,		)




	def      lowercase__							(  self		:  Optional[int]					)     ->			str:




								'''simple docstring'''





								def check_hidden_states_output(__A		:  Dict   ,		__A		:  str   ,		__A		:  Optional[Any]					):
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()

															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)

															lowerCAmelCase__  =   outputs.hidden_states

															lowerCAmelCase__  =   self.model_tester.num_encoder_blocks
															self.assertEqual(len(__A					)   ,		__A					)

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:]					)   ,		[
															        self.model_tester.hidden_sizes[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]   ,		)

								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase__  =   True
															check_hidden_states_output(__A   ,		__A   ,		__A					)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCAmelCase__  =   True

															check_hidden_states_output(__A   ,		__A   ,		__A					)




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								if not self.model_tester.is_training:
															return

								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__  =   True

								for model_class in self.all_model_classes:
															if model_class in get_values(__A					):
																						continue
															# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
															# this can then be incorporated into _prepare_for_class in test_modeling_common.py
															if model_class.__name__ == "GLPNForDepthEstimation":
																						lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  =   inputs_dict["""pixel_values"""].shape
																						lowerCAmelCase__  =   torch.zeros(
																						    [self.model_tester.batch_size, height, width]   ,		device=__A					).long()
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.train()
															lowerCAmelCase__  =   self._prepare_for_class(__A   ,		__A   ,		return_labels=__A					)
															lowerCAmelCase__  =   model(**__A					).loss
															loss.backward()




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




								'''simple docstring'''





								for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase__  =   GLPNModel.from_pretrained(__A					)
															self.assertIsNotNone(__A					)





def 			_lowerCAmelCase(      ) ->							str:
							lowerCAmelCase__  =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
							return image






@require_torch
@require_vision
@slow
class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

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




								'''simple docstring'''





								lowerCAmelCase__  =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								lowerCAmelCase__  =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(__A					)

								lowerCAmelCase__  =   prepare_img()
								lowerCAmelCase__  =   image_processor(images=__A   ,		return_tensors="""pt"""					).to(__A					)

								# forward pass
								with torch.no_grad():
															lowerCAmelCase__  =   model(**__A					)

								# verify the predicted depth
								lowerCAmelCase__  =   torch.Size([1, 480, 640]					)
								self.assertEqual(outputs.predicted_depth.shape   ,		__A					)

								lowerCAmelCase__  =   torch.tensor(
								    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]					).to(__A					)

								self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,		__A   ,		atol=1E-4					)					)










'''simple docstring'''







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      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Dict   ,		__A		:  Optional[Any]=13   ,		__A		:  Dict=7   ,		__A		:  Dict=True   ,		__A		:  Any=True   ,		__A		:  Any=True   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=99   ,		__A		:  Optional[int]=16   ,		__A		:  Tuple=32   ,		__A		:  Optional[int]=2   ,		__A		:  Dict=4   ,		__A		:  Dict=37   ,		__A		:  Any="gelu"   ,		__A		:  Union[str, Any]=0.1   ,		__A		:  List[str]=0.1   ,		__A		:  Any=512   ,		__A		:  Optional[Any]=16   ,		__A		:  List[str]=2   ,		__A		:  List[str]=0.0_2   ,		__A		:  List[str]=3   ,		__A		:  Dict=4   ,		__A		:  List[str]=None   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   13
								lowerCAmelCase__  =   7
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   99
								lowerCAmelCase__  =   16
								lowerCAmelCase__  =   32
								lowerCAmelCase__  =   2
								lowerCAmelCase__  =   4
								lowerCAmelCase__  =   37
								lowerCAmelCase__  =   """gelu"""
								lowerCAmelCase__  =   0.1
								lowerCAmelCase__  =   0.1
								lowerCAmelCase__  =   512
								lowerCAmelCase__  =   16
								lowerCAmelCase__  =   2
								lowerCAmelCase__  =   0.0_2
								lowerCAmelCase__  =   3
								lowerCAmelCase__  =   4
								lowerCAmelCase__  =   None




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




								'''simple docstring'''





								lowerCAmelCase__  =   ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size					)

								lowerCAmelCase__  =   None
								if self.use_input_mask:
															lowerCAmelCase__  =   random_attention_mask([self.batch_size, self.seq_length]					)

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

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

								lowerCAmelCase__  =   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      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Optional[Any]   ,		__A		:  Tuple   ,		__A		:  List[Any]   ,		__A		:  Tuple   ,		__A		:  Dict   ,		__A		:  Optional[int]   ,		__A		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertModel(config=__A					)
								# inputs = {'input_ids': input_ids,
								#           'attention_mask': input_mask,
								#           'token_type_ids': token_type_ids}
								# sequence_output, pooled_output = model(**inputs)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)

								lowerCAmelCase__  =   [input_ids, input_mask]
								lowerCAmelCase__  =   model(__A					)

								lowerCAmelCase__  =   model(__A					)

								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      lowercase__							(  self		:  List[str]   ,		__A		:  Optional[int]   ,		__A		:  Any   ,		__A		:  List[str]   ,		__A		:  Optional[int]   ,		__A		:  List[Any]   ,		__A		:  Optional[int]   ,		__A		:  Union[str, Any]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   TFAlbertForPreTraining(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								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      lowercase__							(  self		:  str   ,		__A		:  Dict   ,		__A		:  Any   ,		__A		:  List[Any]   ,		__A		:  Tuple   ,		__A		:  Union[str, Any]   ,		__A		:  int   ,		__A		:  str					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertForMaskedLM(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.vocab_size)					)




	def      lowercase__							(  self		:  Dict   ,		__A		:  List[Any]   ,		__A		:  Union[str, Any]   ,		__A		:  Union[str, Any]   ,		__A		:  Optional[Any]   ,		__A		:  Optional[Any]   ,		__A		:  str   ,		__A		:  List[str]					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   TFAlbertForSequenceClassification(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels)					)




	def      lowercase__							(  self		:  Dict   ,		__A		:  Any   ,		__A		:  Tuple   ,		__A		:  Union[str, Any]   ,		__A		:  List[Any]   ,		__A		:  List[Any]   ,		__A		:  Any   ,		__A		:  Any					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertForQuestionAnswering(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								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      lowercase__							(  self		:  List[Any]   ,		__A		:  Union[str, Any]   ,		__A		:  List[Any]   ,		__A		:  Tuple   ,		__A		:  Dict   ,		__A		:  Tuple   ,		__A		:  List[str]   ,		__A		:  Dict					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_choices
								lowerCAmelCase__  =   TFAlbertForMultipleChoice(config=__A					)
								lowerCAmelCase__  =   tf.tile(tf.expand_dims(__A   ,		1					)   ,		(1, self.num_choices, 1)					)
								lowerCAmelCase__  =   tf.tile(tf.expand_dims(__A   ,		1					)   ,		(1, self.num_choices, 1)					)
								lowerCAmelCase__  =   tf.tile(tf.expand_dims(__A   ,		1					)   ,		(1, self.num_choices, 1)					)
								lowerCAmelCase__  =   {
								    """input_ids""": multiple_choice_inputs_ids,
								    """attention_mask""": multiple_choice_input_mask,
								    """token_type_ids""": multiple_choice_token_type_ids,
								}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertListEqual(list(result["""logits"""].shape					)   ,		[self.batch_size, self.num_choices]					)




	def      lowercase__							(  self		:  int   ,		__A		:  Tuple   ,		__A		:  Optional[Any]   ,		__A		:  Any   ,		__A		:  Union[str, Any]   ,		__A		:  Tuple   ,		__A		:  str   ,		__A		:  Optional[Any]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   TFAlbertForTokenClassification(config=__A					)
								lowerCAmelCase__  =   {
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertListEqual(list(result["""logits"""].shape					)   ,		[self.batch_size, self.seq_length, self.num_labels]					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   self.prepare_config_and_inputs()
								(
								    (
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,
								)  =   config_and_inputs
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict





@require_tf
class      lowerCamelCase__     (     _A,     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	(
	    (
	        TFAlbertModel,
	        TFAlbertForPreTraining,
	        TFAlbertForMaskedLM,
	        TFAlbertForSequenceClassification,
	        TFAlbertForQuestionAnswering,
	        TFAlbertForTokenClassification,
	        TFAlbertForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	A__		=	(
	    {
	        '''feature-extraction''': TFAlbertModel,
	        '''fill-mask''': TFAlbertForMaskedLM,
	        '''question-answering''': TFAlbertForQuestionAnswering,
	        '''text-classification''': TFAlbertForSequenceClassification,
	        '''token-classification''': TFAlbertForTokenClassification,
	        '''zero-shot''': TFAlbertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	A__		=	False
	A__		=	False




	def      lowercase__							(  self		:  Any   ,		__A		:  List[str]   ,		__A		:  int   ,		__A		:  Union[str, Any]=False					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   super()._prepare_for_class(__A   ,		__A   ,		return_labels=__A					)

								if return_labels:
															if model_class in get_values(__A					):
																						lowerCAmelCase__  =   tf.zeros(self.model_tester.batch_size   ,		dtype=tf.intaa					)

								return inputs_dict




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




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertModelTester(self					)
								lowerCAmelCase__  =   ConfigTester(self   ,		config_class=__A   ,		hidden_size=37					)




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




								'''simple docstring'''





								self.config_tester.run_common_tests()




	def      lowercase__							(  self		:  Any					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_model(*__A					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_pretraining(*__A					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_masked_lm(*__A					)




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_multiple_choice(*__A					)




	def      lowercase__							(  self		:  str					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_sequence_classification(*__A					)




	def      lowercase__							(  self		:  Any					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_question_answering(*__A					)




	@slow
	def      lowercase__							(  self		:  Tuple					)     ->			Dict:




								'''simple docstring'''





								for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase__  =   TFAlbertModel.from_pretrained(__A					)
															self.assertIsNotNone(__A					)





@require_tf
class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

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




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""					)
								lowerCAmelCase__  =   tf.constant([[0, 1, 2, 3, 4, 5]]					)
								lowerCAmelCase__  =   model(__A					)[0]

								lowerCAmelCase__  =   [1, 6, 3_0000]
								self.assertEqual(output.shape   ,		__A					)

								lowerCAmelCase__  =   tf.constant(
								    [
								        [
								            [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
								            [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
								            [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
								        ]
								    ]					)
								tf.debugging.assert_near(output[:, :3, :3]   ,		__A   ,		atol=1E-4					)





