


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   __lowercase						(lowerCAmelCase_     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				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   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__=1_3 ,	lowerCAmelCase__=6_4 ,	lowerCAmelCase__=3 ,	lowerCAmelCase__=4 ,	lowerCAmelCase__=[2, 2, 2, 2] ,	lowerCAmelCase__=[8, 4, 2, 1] ,	lowerCAmelCase__=[1_6, 3_2, 6_4, 1_2_8] ,	lowerCAmelCase__=[1, 4, 8, 1_6] ,	lowerCAmelCase__=[1, 2, 4, 8] ,	lowerCAmelCase__=True ,	lowerCAmelCase__=True ,	lowerCAmelCase__="gelu" ,	lowerCAmelCase__=0.1 ,	lowerCAmelCase__=0.1 ,	lowerCAmelCase__=0.02 ,	lowerCAmelCase__=1_6 ,	lowerCAmelCase__=3 ,	lowerCAmelCase__=None ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				parent
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				batch_size
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				image_size
										SCREAMING_SNAKE_CASE_		:    Any 		=				num_channels
										SCREAMING_SNAKE_CASE_		:    str 		=				num_encoder_blocks
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				sr_ratios
										SCREAMING_SNAKE_CASE_		:    Any 		=				depths
										SCREAMING_SNAKE_CASE_		:    Dict 		=				hidden_sizes
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				downsampling_rates
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				num_attention_heads
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				is_training
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				use_labels
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				hidden_act
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				initializer_range
										SCREAMING_SNAKE_CASE_		:    Any 		=				decoder_hidden_size
										SCREAMING_SNAKE_CASE_		:    Dict 		=				num_labels
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				scope






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										SCREAMING_SNAKE_CASE_		:    int 		=				self.get_config()
										return config, pixel_values, labels






							def 				UpperCamelCase__    (			self					):





										"""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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				GLPNModel(config=__a					)
										model.to(__a					)
										model.eval()
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				model(__a					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.num_labels
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				GLPNForDepthEstimation(__a					)
										model.to(__a					)
										model.eval()
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				model(__a					)
										self.parent.assertEqual(result.predicted_depth.shape ,	(self.batch_size, self.image_size, self.image_size)					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model(__a ,	labels=__a					)
										self.parent.assertEqual(result.predicted_depth.shape ,	(self.batch_size, self.image_size, self.image_size)					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				config_and_inputs
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				{'pixel_values': pixel_values}
										return config, inputs_dict







@require_torch
class   __lowercase						(lowerCAmelCase_  ,					lowerCAmelCase_  ,					unittest.TestCase     ):





							"""simple docstring"""







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

							_UpperCAmelCase						     =	False
							_UpperCAmelCase						     =	False
							_UpperCAmelCase						     =	False






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				GLPNModelTester(self					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				GLPNConfigTester(self ,	config_class=__a					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				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 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										pass






							@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'					)
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										pass






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    str 		=				self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE_		:    Tuple 		=				['pixel_values']
													self.assertListEqual(arg_names[:1] ,	__a					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_		:    Dict 		=				True

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:    Dict 		=				True
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				False
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				True
													SCREAMING_SNAKE_CASE_		:    Dict 		=				model_class(__a					)
													model.to(__a					)
													model.eval()
													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:    str 		=				model(**self._prepare_for_class(__a ,	__a					)					)
													SCREAMING_SNAKE_CASE_		:    Dict 		=				outputs.attentions

													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				sum(self.model_tester.depths					)
													self.assertEqual(len(__a					) ,	__a					)

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

													self.assertEqual(len(__a					) ,	__a					)

													# verify the first attentions (first block, first layer)
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				(self.model_tester.image_size // 4) ** 2
													SCREAMING_SNAKE_CASE_		:    int 		=				(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)
													SCREAMING_SNAKE_CASE_		:    int 		=				(self.model_tester.image_size // 3_2) ** 2
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				(self.model_tester.image_size // (3_2 * 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] ,	)
													SCREAMING_SNAKE_CASE_		:    Dict 		=				len(__a					)

													# Check attention is always last and order is fine
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				True
													SCREAMING_SNAKE_CASE_		:    Dict 		=				True
													SCREAMING_SNAKE_CASE_		:    int 		=				model_class(__a					)
													model.to(__a					)
													model.eval()
													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:    int 		=				model(**self._prepare_for_class(__a ,	__a					)					)

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

													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				outputs.attentions

													self.assertEqual(len(__a					) ,	__a					)
													# verify the first attentions (first block, first layer)
													SCREAMING_SNAKE_CASE_		:    int 		=				(self.model_tester.image_size // 4) ** 2
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				(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 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				model_class(__a					)
													model.to(__a					)
													model.eval()

													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:    int 		=				model(**self._prepare_for_class(__a ,	__a					)					)

													SCREAMING_SNAKE_CASE_		:    Dict 		=				outputs.hidden_states

													SCREAMING_SNAKE_CASE_		:    Any 		=				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,
													    ] ,	)

										SCREAMING_SNAKE_CASE_		:    Dict 		=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				True
													check_hidden_states_output(__a ,	__a ,	__a					)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				True

													check_hidden_states_output(__a ,	__a ,	__a					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										if not self.model_tester.is_training:
													return

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_		:    str 		=				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":
																SCREAMING_SNAKE_CASE_		:    Any 		=				inputs_dict['pixel_values'].shape
																SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.zeros(
																    [self.model_tester.batch_size, height, width] ,	device=__a					).long()
													SCREAMING_SNAKE_CASE_		:    int 		=				model_class(__a					)
													model.to(__a					)
													model.train()
													SCREAMING_SNAKE_CASE_		:    Any 		=				self._prepare_for_class(__a ,	__a ,	return_labels=__a					)
													SCREAMING_SNAKE_CASE_		:    str 		=				model(**__a					).loss
													loss.backward()






							@slow
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_		:    str 		=				GLPNModel.from_pretrained(__a					)
													self.assertIsNotNone(__a					)







def 	a__ (							):
			SCREAMING_SNAKE_CASE_		:    str 		=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
			return image







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





							"""simple docstring"""







							@slow
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(__a					)

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				prepare_img()
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				image_processor(images=__a ,	return_tensors='pt'					).to(__a					)

										# forward pass
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				model(**__a					)

										# verify the predicted depth
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				torch.Size([1, 4_8_0, 6_4_0]					)
										self.assertEqual(outputs.predicted_depth.shape ,	__a					)

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				torch.tensor(
										    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]					).to(__a					)

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









from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										return True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				'Hugging Face大法好，谁用谁知道。'
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				'Hugging Face大法好,谁用谁知道。<unk>'
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tokenizer.tokenize(lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer.decode(lowerCAmelCase__					)
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)





