"""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 	__UpperCAmelCase						(_a    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.config_class(**self.inputs_dict       )
										self.parent.assertTrue(hasattr(lowerCamelCase__      ,			"""hidden_sizes"""       )       )
										self.parent.assertTrue(hasattr(lowerCamelCase__      ,			"""num_attention_heads"""       )       )
										self.parent.assertTrue(hasattr(lowerCamelCase__      ,			"""num_encoder_blocks"""       )       )





class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=13      ,			snake_case_=64      ,			snake_case_=3      ,			snake_case_=4      ,			snake_case_=[2, 2, 2, 2]      ,			snake_case_=[8, 4, 2, 1]      ,			snake_case_=[16, 32, 64, 128]      ,			snake_case_=[1, 4, 8, 16]      ,			snake_case_=[1, 2, 4, 8]      ,			snake_case_=True      ,			snake_case_=True      ,			snake_case_="gelu"      ,			snake_case_=0.1      ,			snake_case_=0.1      ,			snake_case_=0.02      ,			snake_case_=16      ,			snake_case_=3      ,			snake_case_=None      ,			):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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





			def 		lowerCamelCase				( 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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= GLPNModel(config=lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()
										A__					:    Any														= model(lowerCamelCase__       )
										A__					:    List[str]														= 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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.prepare_config_and_inputs()
										A__					:    List[Any]														= config_and_inputs
										A__					:    List[str]														= {"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class 	__UpperCAmelCase						(_a       ,      _a       ,      unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	str				    =							(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
			_UpperCamelCase     :	Optional[int]				    =							(
			    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
			)

			_UpperCamelCase     :	str				    =							False
			_UpperCamelCase     :	List[Any]				    =							False
			_UpperCamelCase     :	int				    =							False





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= GLPNModelTester(self       )
										A__					:    List[str]														= GLPNConfigTester(self      ,			config_class=lowerCamelCase__       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										self.config_tester.run_common_tests()





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase__       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__       )





			@unittest.skip("""GLPN does not use inputs_embeds"""       )
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										pass





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







										'''simple docstring'''


										pass





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__					:    Union[str, Any]														= model_class(lowerCamelCase__       )
																	A__					:    List[Any]														= inspect.signature(model.forward       )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__					:    Any														= [*signature.parameters.keys()]

																	A__					:    str														= ["pixel_values"]
																	self.assertListEqual(arg_names[:1]      ,			lowerCamelCase__       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= self.model_tester.prepare_config_and_inputs_for_common()
										A__					:    Optional[int]														= True

										for model_class in self.all_model_classes:
																	A__					:    str														= True
																	A__					:    Optional[int]														= False
																	A__					:    Union[str, Any]														= True
																	A__					:    List[Any]														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.eval()
																	with torch.no_grad():
																								A__					:    List[str]														= model(**self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__       )       )
																	A__					:    Optional[Any]														= outputs.attentions

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

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

																	self.assertEqual(len(lowerCamelCase__       )      ,			lowerCamelCase__       )

																	# verify the first attentions (first block, first layer)
																	A__					:    Optional[Any]														= (self.model_tester.image_size // 4) ** 2
																	A__					:    Any														= (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)
																	A__					:    int														= (self.model_tester.image_size // 32) ** 2
																	A__					:    Tuple														= (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]      ,			)
																	A__					:    Tuple														= len(lowerCamelCase__       )

																	# Check attention is always last and order is fine
																	A__					:    str														= True
																	A__					:    int														= True
																	A__					:    Any														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.eval()
																	with torch.no_grad():
																								A__					:    Union[str, Any]														= model(**self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__       )       )

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

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

																	self.assertEqual(len(lowerCamelCase__       )      ,			lowerCamelCase__       )
																	# verify the first attentions (first block, first layer)
																	A__					:    int														= (self.model_tester.image_size // 4) ** 2
																	A__					:    List[str]														= (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 		lowerCamelCase				( self       ):







										'''simple docstring'''


										def check_hidden_states_output(snake_case_      ,			snake_case_      ,			snake_case_       ):
																	A__					:    int														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.eval()

																	with torch.no_grad():
																								A__					:    Dict														= model(**self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__       )       )

																	A__					:    str														= outputs.hidden_states

																	A__					:    Union[str, Any]														= self.model_tester.num_encoder_blocks
																	self.assertEqual(len(lowerCamelCase__       )      ,			lowerCamelCase__       )

																	# 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,
																	    ]      ,			)

										A__					:    Union[str, Any]														= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__					:    Any														= True
																	check_hidden_states_output(lowerCamelCase__      ,			lowerCamelCase__      ,			lowerCamelCase__       )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	A__					:    str														= True

																	check_hidden_states_output(lowerCamelCase__      ,			lowerCamelCase__      ,			lowerCamelCase__       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if not self.model_tester.is_training:
																	return

										A__					:    Union[str, Any]														= self.model_tester.prepare_config_and_inputs_for_common()
										A__					:    List[Any]														= True

										for model_class in self.all_model_classes:
																	if model_class in get_values(lowerCamelCase__       ):
																								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":
																								A__					:    int														= inputs_dict["pixel_values"].shape
																								A__					:    Optional[int]														= torch.zeros(
																								    [self.model_tester.batch_size, height, width]      ,			device=lowerCamelCase__       ).long()
																	A__					:    Optional[Any]														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.train()
																	A__					:    Any														= self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__      ,			return_labels=lowerCamelCase__       )
																	A__					:    Union[str, Any]														= model(**lowerCamelCase__       ).loss
																	loss.backward()





			@slow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__					:    List[Any]														= GLPNModel.from_pretrained(lowerCamelCase__       )
																	self.assertIsNotNone(lowerCamelCase__       )





def    _A(		):
							A__					:    Any														= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image



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







			'''simple docstring'''





			@slow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
										A__					:    List[str]														= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowerCamelCase__       )

										A__					:    str														= prepare_img()
										A__					:    Tuple														= image_processor(images=lowerCamelCase__      ,			return_tensors="""pt"""       ).to(lowerCamelCase__       )

										# forward pass
										with torch.no_grad():
																	A__					:    int														= model(**lowerCamelCase__       )

										# verify the predicted depth
										A__					:    Any														= torch.Size([1, 480, 640]       )
										self.assertEqual(outputs.predicted_depth.shape      ,			lowerCamelCase__       )

										A__					:    int														= torch.tensor(
										    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]       ).to(lowerCamelCase__       )

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






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							'opt'
			_UpperCamelCase     :	Optional[Any]				    =							['past_key_values']





			def __init__( self      ,			snake_case_=50_272      ,			snake_case_=768      ,			snake_case_=12      ,			snake_case_=3_072      ,			snake_case_=2_048      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=None      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=12      ,			snake_case_="relu"      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=True      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=2      ,			snake_case_=True      ,			snake_case_=True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(
										    pad_token_id=snake_case_      ,			bos_token_id=snake_case_      ,			eos_token_id=snake_case_      ,			**snake_case_      ,			)
										A__					:    int														= vocab_size
										A__					:    List[str]														= max_position_embeddings
										A__					:    str														= num_attention_heads
										A__					:    Dict														= word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
										A__					:    Optional[int]														= ffn_dim
										A__					:    Optional[int]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    str														= dropout
										A__					:    Tuple														= attention_dropout
										A__					:    Optional[Any]														= activation_function
										A__					:    Any														= init_std
										A__					:    Optional[int]														= layerdrop
										A__					:    Optional[int]														= use_cache
										A__					:    int														= do_layer_norm_before
										# We keep these variables at `True` for backward compatibility.
										A__					:    str														= enable_bias
										A__					:    List[str]														= layer_norm_elementwise_affine

										# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
										# with checkpoints that have been fine-tuned before transformers v4.20.1
										# see https://github.com/facebookresearch/metaseq/pull/164
										A__					:    str														= _remove_final_layer_norm





