

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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						__magic_name__				=     self.config_class(**self.inputs_dict   )
						self.parent.assertTrue(hasattr(__UpperCamelCase	,			'hidden_sizes'   )   )
						self.parent.assertTrue(hasattr(__UpperCamelCase	,			'num_attention_heads'   )   )
						self.parent.assertTrue(hasattr(__UpperCamelCase	,			'num_encoder_blocks'   )   )




class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Union[str, Any]	,			__UpperCamelCase:			List[str]=13	,			__UpperCamelCase:			Optional[int]=64	,			__UpperCamelCase:			str=3	,			__UpperCamelCase:			List[str]=4	,			__UpperCamelCase:			Any=[2, 2, 2, 2]	,			__UpperCamelCase:			str=[8, 4, 2, 1]	,			__UpperCamelCase:			str=[16, 32, 64, 1_28]	,			__UpperCamelCase:			Any=[1, 4, 8, 16]	,			__UpperCamelCase:			Optional[Any]=[1, 2, 4, 8]	,			__UpperCamelCase:			Tuple=True	,			__UpperCamelCase:			int=True	,			__UpperCamelCase:			Optional[int]="gelu"	,			__UpperCamelCase:			Dict=0.1	,			__UpperCamelCase:			List[Any]=0.1	,			__UpperCamelCase:			Tuple=0.02	,			__UpperCamelCase:			List[str]=16	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Optional[Any]=None	,			):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





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

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''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 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			Union[str, Any]	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     GLPNModel(config=__UpperCamelCase   )
						model.to(__UpperCamelCase   )
						model.eval()
						__magic_name__				=     model(__UpperCamelCase   )
						__magic_name__				=     __magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[Any]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			Optional[int]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     self.prepare_config_and_inputs()
						__magic_name__,						__magic_name__,						__magic_name__				=     config_and_inputs
						__magic_name__				=     {'pixel_values': pixel_values}
						return config, inputs_dict




@require_torch
class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase      ):
	_lowercase	: Dict												=	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
	_lowercase	: str												=	(
	    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
	)

	_lowercase	: Dict												=	False
	_lowercase	: Optional[int]												=	False
	_lowercase	: Optional[Any]												=	False







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     GLPNModelTester(self   )
						__magic_name__				=     GLPNConfigTester(self	,			config_class=__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						self.config_tester.run_common_tests()







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase   )







	@unittest.skip('GLPN does not use inputs_embeds'   )
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						pass







	@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'   )
	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						pass







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.model_tester.prepare_config_and_inputs_for_common()

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

											__magic_name__				=     ['pixel_values']
											self.assertListEqual(arg_names[:1]	,			__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





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

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

											__magic_name__				=     sum(self.model_tester.depths   )
											self.assertEqual(len(__UpperCamelCase   )	,			__UpperCamelCase   )

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

											self.assertEqual(len(__UpperCamelCase   )	,			__UpperCamelCase   )

											# verify the first attentions (first block, first layer)
											__magic_name__				=     (self.model_tester.image_size // 4) ** 2
											__magic_name__				=     (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)
											__magic_name__				=     (self.model_tester.image_size // 32) ** 2
											__magic_name__				=     (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]	,			)
											__magic_name__				=     len(__UpperCamelCase   )

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

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

											__magic_name__				=     outputs.attentions

											self.assertEqual(len(__UpperCamelCase   )	,			__UpperCamelCase   )
											# verify the first attentions (first block, first layer)
											__magic_name__				=     (self.model_tester.image_size // 4) ** 2
											__magic_name__				=     (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 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						def check_hidden_states_output(__UpperCamelCase:			List[str]	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			List[str]   ):
											__magic_name__				=     model_class(__UpperCamelCase   )
											model.to(__UpperCamelCase   )
											model.eval()

											with torch.no_grad():
																__magic_name__				=     model(**self._prepare_for_class(__UpperCamelCase	,			__UpperCamelCase   )   )

											__magic_name__				=     outputs.hidden_states

											__magic_name__				=     self.model_tester.num_encoder_blocks
											self.assertEqual(len(__UpperCamelCase   )	,			__UpperCamelCase   )

											# 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,
											    ]	,			)

						__magic_name__,						__magic_name__				=     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__magic_name__				=     True
											check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )

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

											check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						if not self.model_tester.is_training:
											return

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

						for model_class in self.all_model_classes:
											if model_class in get_values(__UpperCamelCase   ):
																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":
																__magic_name__,						__magic_name__,						__magic_name__,						__magic_name__				=     inputs_dict['pixel_values'].shape
																__magic_name__				=     torch.zeros(
																    [self.model_tester.batch_size, height, width]	,			device=__UpperCamelCase   ).long()
											__magic_name__				=     model_class(__UpperCamelCase   )
											model.to(__UpperCamelCase   )
											model.train()
											__magic_name__				=     self._prepare_for_class(__UpperCamelCase	,			__UpperCamelCase	,			return_labels=__UpperCamelCase   )
											__magic_name__				=     model(**__UpperCamelCase   ).loss
											loss.backward()







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





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




def        _lowercase  (					)					->						List[str]:




					'''simple docstring'''







					__magic_name__				=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
					return image


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







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
						__magic_name__				=     GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__UpperCamelCase   )

						__magic_name__				=     prepare_img()
						__magic_name__				=     image_processor(images=__UpperCamelCase	,			return_tensors='pt'   ).to(__UpperCamelCase   )

						# forward pass
						with torch.no_grad():
											__magic_name__				=     model(**__UpperCamelCase   )

						# verify the predicted depth
						__magic_name__				=     torch.Size([1, 4_80, 6_40]   )
						self.assertEqual(outputs.predicted_depth.shape	,			__UpperCamelCase   )

						__magic_name__				=     torch.tensor(
						    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]   ).to(__UpperCamelCase   )

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Dict												=	"ernie"







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict=3_05_22	,			__UpperCamelCase:			str=7_68	,			__UpperCamelCase:			Tuple=12	,			__UpperCamelCase:			int=12	,			__UpperCamelCase:			Optional[Any]=30_72	,			__UpperCamelCase:			str="gelu"	,			__UpperCamelCase:			Any=0.1	,			__UpperCamelCase:			int=0.1	,			__UpperCamelCase:			int=5_12	,			__UpperCamelCase:			Any=2	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Optional[int]=0.02	,			__UpperCamelCase:			List[Any]=1E-12	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Tuple="absolute"	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Optional[Any]=None	,			**__UpperCamelCase:			List[Any]	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     hidden_act
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     type_vocab_size
						__magic_name__				=     task_type_vocab_size
						__magic_name__				=     use_task_id
						__magic_name__				=     initializer_range
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     position_embedding_type
						__magic_name__				=     use_cache
						__magic_name__				=     classifier_dropout




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__				=     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__				=     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )




