



'''simple docstring'''

from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None  )      ->				int:
		if attention_mask is None:
				_a					:				List[Any]				=							tf.cast(tf.math.not_equal(lowerCAmelCase_						,      config.pad_token_id  )						,      tf.inta  )
		return {"input_ids": input_ids, "attention_mask": attention_mask}

@require_tf
class      __magic_name__						:
							lowerCAmelCase	:   Tuple           = OPTConfig
							lowerCAmelCase	:   Dict           = {}
							lowerCAmelCase	:   str           = 'gelu'







							def __init__(							self :	Optional[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	str=13     ,_UpperCAmelCase :	Optional[Any]=7     ,_UpperCAmelCase :	List[Any]=True     ,_UpperCAmelCase :	Any=False     ,_UpperCAmelCase :	Dict=99     ,_UpperCAmelCase :	List[str]=16     ,_UpperCAmelCase :	List[Any]=2     ,_UpperCAmelCase :	str=4     ,_UpperCAmelCase :	Union[str, Any]=4     ,_UpperCAmelCase :	str="gelu"     ,_UpperCAmelCase :	List[str]=0.1     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	Optional[int]=20     ,_UpperCAmelCase :	str=2     ,_UpperCAmelCase :	Dict=1     ,_UpperCAmelCase :	int=0     ,_UpperCAmelCase :	Optional[int]=16     ,_UpperCAmelCase :	List[Any]=16     ,):
									_a					:				Dict				=							parent
									_a					:				Any				=							batch_size
									_a					:				str				=							seq_length
									_a					:				Optional[int]				=							is_training
									_a					:				Optional[int]				=							use_labels
									_a					:				Dict				=							vocab_size
									_a					:				List[Any]				=							hidden_size
									_a					:				str				=							num_hidden_layers
									_a					:				str				=							num_attention_heads
									_a					:				Optional[Any]				=							intermediate_size
									_a					:				Optional[int]				=							hidden_act
									_a					:				int				=							hidden_dropout_prob
									_a					:				Optional[Any]				=							attention_probs_dropout_prob
									_a					:				Optional[Any]				=							max_position_embeddings
									_a					:				Optional[Any]				=							eos_token_id
									_a					:				str				=							pad_token_id
									_a					:				int				=							bos_token_id
									_a					:				Dict				=							embed_dim
									_a					:				str				=							word_embed_proj_dim
									_a					:				int				=							False







							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[int]				=							ids_tensor([self.batch_size, self.seq_length - 1]     ,self.vocab_size		)
									_a					:				List[Any]				=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)     ,1		)
									_a					:				List[Any]				=							tf.concat([input_ids, eos_tensor]     ,axis=1		)

									_a					:				Any				=							self.config_cls(
									    vocab_size=self.vocab_size     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,ffn_dim=self.intermediate_size     ,dropout=self.hidden_dropout_prob     ,attention_dropout=self.attention_probs_dropout_prob     ,max_position_embeddings=self.max_position_embeddings     ,eos_token_id=self.eos_token_id     ,bos_token_id=self.bos_token_id     ,pad_token_id=self.pad_token_id     ,embed_dim=self.embed_dim     ,word_embed_proj_dim=self.word_embed_proj_dim     ,is_encoder_decoder=_UpperCAmelCase     ,**self.config_updates     ,)
									_a					:				str				=							prepare_opt_inputs_dict(_UpperCAmelCase     ,_UpperCAmelCase		)
									return config, inputs_dict







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	List[str]		):
									_a					:				Any				=							TFOPTModel(config=_UpperCAmelCase		)
									_a					:				Any				=							inputs_dict['input_ids']

									_a					:				Dict				=							input_ids[:1, :]
									_a					:				List[str]				=							inputs_dict['attention_mask'][:1, :]
									_a					:				Tuple				=							1

									# first forward pass
									_a					:				List[Any]				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,use_cache=_UpperCAmelCase		)

									_a	, _a					:				List[str]				=							outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									_a					:				int				=							ids_tensor((self.batch_size, 3)     ,config.vocab_size		)
									_a					:				List[Any]				=							tf.cast(ids_tensor((self.batch_size, 3)     ,2		)     ,tf.inta		)

									# append to next input_ids and
									_a					:				Tuple				=							tf.concat([input_ids, next_tokens]     ,axis=-1		)
									_a					:				Union[str, Any]				=							tf.concat([attention_mask, next_attn_mask]     ,axis=-1		)

									_a					:				Dict				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)[0]
									_a					:				List[str]				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,past_key_values=_UpperCAmelCase		)[0]

									self.parent.assertEqual(next_tokens.shape[1]     ,output_from_past.shape[1]		)

									# select random slice
									_a					:				Union[str, Any]				=							int(ids_tensor((1,)     ,output_from_past.shape[-1]		)		)
									_a					:				List[str]				=							output_from_no_past[:, -3:, random_slice_idx]
									_a					:				List[str]				=							output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(_UpperCAmelCase     ,_UpperCAmelCase     ,rtol=1E-3		)

@require_tf
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
							lowerCAmelCase	:   Optional[int]           = (TFOPTForCausalLM,) if is_tf_available() else ()
							lowerCAmelCase	:   List[str]           = (
							    {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
							)
							lowerCAmelCase	:   List[Any]           = False
							lowerCAmelCase	:   str           = False
							lowerCAmelCase	:   List[str]           = False
							lowerCAmelCase	:   Dict           = 1_0







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				int				=							TFOPTModelTester(self		)
									_a					:				Dict				=							ConfigTester(self     ,config_class=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									self.config_tester.run_common_tests()







							def 							__lowercase      (							self :	int		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a	, _a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

									def _get_word_embedding_weight(_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	int		):
											if hasattr(_UpperCAmelCase     ,'weight'		):
													return embedding_layer.weight
											else:
													# Here we build the word embeddings weights if not exists.
													# And then we retry to get the attribute once built.
													model.build()
													if hasattr(_UpperCAmelCase     ,'weight'		):
															return embedding_layer.weight
													else:
															return None

									for model_class in self.all_model_classes:
											for size in [config.vocab_size - 10, config.vocab_size + 10]:
													# build the embeddings
													_a					:				Optional[int]				=							model_class(config=_UpperCAmelCase		)
													_a					:				Dict				=							_get_word_embedding_weight(_UpperCAmelCase     ,model.get_input_embeddings()		)
													_a					:				Optional[Any]				=							_get_word_embedding_weight(_UpperCAmelCase     ,model.get_output_embeddings()		)

													# reshape the embeddings
													model.resize_token_embeddings(_UpperCAmelCase		)
													_a					:				Union[str, Any]				=							_get_word_embedding_weight(_UpperCAmelCase     ,model.get_input_embeddings()		)
													_a					:				Optional[Any]				=							_get_word_embedding_weight(_UpperCAmelCase     ,model.get_output_embeddings()		)

													# check that the resized embeddings size matches the desired size.
													_a					:				Optional[int]				=							size if size is not None else config.vocab_size

													self.assertEqual(new_input_embeddings.shape[0]     ,_UpperCAmelCase		)

													# check that weights remain the same after resizing
													_a					:				Tuple				=							True
													for pa, pa in zip(old_input_embeddings.value()     ,new_input_embeddings.value()		):
															if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
																	_a					:				int				=							False
													self.assertTrue(_UpperCAmelCase		)

													if old_output_embeddings is not None and new_output_embeddings is not None:
															self.assertEqual(new_output_embeddings.shape[0]     ,_UpperCAmelCase		)

															_a					:				str				=							True
															for pa, pa in zip(old_output_embeddings.value()     ,new_output_embeddings.value()		):
																	if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
																			_a					:				List[str]				=							False
															self.assertTrue(_UpperCAmelCase		)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Any:
		return tf.constant(lowerCAmelCase_						,      dtype=tf.intaa  )

@require_tf
class      __magic_name__						(   unittest.TestCase       ):
							lowerCAmelCase	:   Optional[Any]           = 9_9







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Tuple				=							tf.ones((4, 1)     ,dtype=tf.intaa		) * 2
									_a					:				List[str]				=							tf.concat([ids_tensor((4, 6)     ,self.vocab_size - 3		) + 3, eos_column_vector]     ,axis=1		)
									_a					:				Any				=							input_ids.shape[0]
									_a					:				Optional[int]				=							OPTConfig(
									    vocab_size=self.vocab_size     ,hidden_size=24     ,num_hidden_layers=2     ,num_attention_heads=2     ,ffn_dim=32     ,max_position_embeddings=48     ,eos_token_id=2     ,pad_token_id=1     ,bos_token_id=0     ,)
									return config, input_ids, batch_size

@require_sentencepiece
@require_tf
class      __magic_name__						(   unittest.TestCase       ):







							@slow
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Any				=							TFOPTModel.from_pretrained('facebook/opt-350m'		)
									_a					:				Optional[Any]				=							_long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
									_a					:				Any				=							tf.not_equal(_UpperCAmelCase     ,model.config.pad_token_id		)
									with tf.GradientTape():
											_a					:				Tuple				=							model(input_ids=_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		).last_hidden_state
									_a					:				List[str]				=							(1, 11, 512)
									self.assertEqual(output.shape     ,_UpperCAmelCase		)
									_a					:				Dict				=							tf.constant(
									    [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]		)
									self.assertTrue(np.allclose(output[:, :3, :3]     ,_UpperCAmelCase     ,atol=4E-3		)		)

									_a					:				str				=							tf.function(_UpperCAmelCase     ,jit_compile=_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							xla_generate(_UpperCAmelCase     ,_UpperCAmelCase		)[0]
									self.assertTrue(np.allclose(output[:, :3, :3]     ,_UpperCAmelCase     ,atol=4E-2		)		)

@require_tf
@slow
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	Union[str, Any]		):
									super().setUp()
									_a					:				List[str]				=							'facebook/opt-350m'







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[Any]				=							TFOPTForCausalLM.from_pretrained(self.path_model		)
									_a					:				Dict				=							GPTaTokenizer.from_pretrained(self.path_model		)

									_a					:				Optional[int]				=							[
									    'Today is a beautiful day and I want to',
									    'In the city of',
									    'Paris is the capital of France and',
									    'Computers and mobile phones have taken',
									]
									# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
									_a					:				List[str]				=							tokenizer(_UpperCAmelCase     ,return_tensors='tf'     ,padding=_UpperCAmelCase     ,add_special_tokens=_UpperCAmelCase		)
									_a					:				List[str]				=							tf.math.reduce_mean(model(inputs.input_ids     ,attention_mask=inputs.attention_mask		)[0]     ,axis=-1		)
									_a					:				str				=							tf.constant(
									    [
									        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
									        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
									        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
									        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
									    ]		)
									self.assertTrue(np.allclose(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=1E-4		)		)

									_a					:				Tuple				=							tf.function(_UpperCAmelCase     ,jit_compile=_UpperCAmelCase		)
									_a					:				List[Any]				=							tf.math.reduce_mean(xla_generate(inputs.input_ids     ,attention_mask=inputs.attention_mask		)[0]     ,axis=-1		)
									self.assertTrue(np.allclose(_UpperCAmelCase     ,_UpperCAmelCase     ,atol=1E-4		)		)

@require_tf
@slow
class      __magic_name__						(   unittest.TestCase       ):







							@property
							def 							__lowercase      (							self :	Tuple		):
									return [
									    "Today is a beautiful day and I want",
									    "In the city of",
									    "Paris is the capital of France and",
									    "Computers and mobile phones have taken",
									]







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Tuple				=							'facebook/opt-125m'

									_a					:				Union[str, Any]				=							[
									    'Today is a beautiful day and I want to',
									    'In the city of New York, the city',
									    'Paris is the capital of France and the capital',
									    'Computers and mobile phones have taken over the',
									]

									_a					:				Dict				=							[]
									_a					:				List[str]				=							GPTaTokenizer.from_pretrained(_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							TFOPTForCausalLM.from_pretrained(_UpperCAmelCase		)

									for prompt in self.prompts:
											_a					:				str				=							tokenizer(_UpperCAmelCase     ,return_tensors='tf'		).input_ids

											_a					:				int				=							model.generate(_UpperCAmelCase     ,max_length=10		)

											_a					:				List[str]				=							tokenizer.batch_decode(_UpperCAmelCase     ,skip_special_tokens=_UpperCAmelCase		)
											predicted_outputs += generated_string

									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Union[str, Any]				=							'facebook/opt-350m'

									_a					:				List[str]				=							GPTaTokenizer.from_pretrained(_UpperCAmelCase		)
									_a					:				List[Any]				=							TFOPTForCausalLM.from_pretrained(_UpperCAmelCase		)

									_a					:				List[Any]				=							'left'

									# use different length sentences to test batching
									_a					:				Union[str, Any]				=							[
									    'Hello, my dog is a little',
									    'Today, I',
									]

									_a					:				Tuple				=							tokenizer(_UpperCAmelCase     ,return_tensors='tf'     ,padding=_UpperCAmelCase		)
									_a					:				str				=							inputs['input_ids']

									_a					:				Dict				=							model.generate(input_ids=_UpperCAmelCase     ,attention_mask=inputs['attention_mask']		)

									_a					:				int				=							tokenizer(sentences[0]     ,return_tensors='tf'		).input_ids
									_a					:				Tuple				=							model.generate(input_ids=_UpperCAmelCase		)

									_a					:				List[Any]				=							inputs_non_padded.shape[-1] - tf.math.reduce_sum(
									    tf.cast(inputs['attention_mask'][-1]     ,tf.intaa		)		)
									_a					:				Any				=							tokenizer(sentences[1]     ,return_tensors='tf'		).input_ids
									_a					:				Optional[Any]				=							model.generate(input_ids=_UpperCAmelCase     ,max_length=model.config.max_length - num_paddings		)

									_a					:				int				=							tokenizer.batch_decode(_UpperCAmelCase     ,skip_special_tokens=_UpperCAmelCase		)
									_a					:				List[Any]				=							tokenizer.decode(output_non_padded[0]     ,skip_special_tokens=_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							tokenizer.decode(output_padded[0]     ,skip_special_tokens=_UpperCAmelCase		)

									_a					:				Tuple				=							[
									    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
									    'Today, I was in the middle of a conversation with a friend about the',
									]
									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase     ,[non_padded_sentence, padded_sentence]		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							'facebook/opt-350m'

									_a					:				Any				=							[
									    'Today is a beautiful day and I want to',
									    'In the city of San Francisco, the city',
									    'Paris is the capital of France and the capital',
									    'Computers and mobile phones have taken over the',
									]

									_a					:				List[str]				=							[]
									_a					:				Optional[Any]				=							GPTaTokenizer.from_pretrained(_UpperCAmelCase		)
									_a					:				List[str]				=							TFOPTForCausalLM.from_pretrained(_UpperCAmelCase		)

									for prompt in self.prompts:
											_a					:				Dict				=							tokenizer(_UpperCAmelCase     ,return_tensors='tf'		).input_ids

											_a					:				List[str]				=							model.generate(_UpperCAmelCase     ,max_length=10		)

											_a					:				Tuple				=							tokenizer.batch_decode(_UpperCAmelCase     ,skip_special_tokens=_UpperCAmelCase		)
											predicted_outputs += generated_string

									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)









'''simple docstring'''

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
							from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor

class      __magic_name__						:







							def __init__(							self :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int]=2     ,_UpperCAmelCase :	Tuple=32     ,_UpperCAmelCase :	Dict=16     ,_UpperCAmelCase :	Optional[Any]=3     ,_UpperCAmelCase :	Tuple=True     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	str=32     ,_UpperCAmelCase :	Tuple=4     ,_UpperCAmelCase :	List[Any]=[0, 1, 2, 3]     ,_UpperCAmelCase :	List[str]=4     ,_UpperCAmelCase :	Any=37     ,_UpperCAmelCase :	List[Any]="gelu"     ,_UpperCAmelCase :	Optional[int]=0.1     ,_UpperCAmelCase :	Tuple=0.1     ,_UpperCAmelCase :	Optional[Any]=0.02     ,_UpperCAmelCase :	Optional[Any]=3     ,_UpperCAmelCase :	List[str]=[1, 384, 24, 24]     ,_UpperCAmelCase :	Union[str, Any]=True     ,_UpperCAmelCase :	List[str]=None     ,):
									_a					:				int				=							parent
									_a					:				Optional[Any]				=							batch_size
									_a					:				str				=							image_size
									_a					:				str				=							patch_size
									_a					:				Any				=							num_channels
									_a					:				Optional[Any]				=							is_training
									_a					:				str				=							use_labels
									_a					:				Union[str, Any]				=							hidden_size
									_a					:				int				=							num_hidden_layers
									_a					:				str				=							backbone_out_indices
									_a					:				List[Any]				=							num_attention_heads
									_a					:				Any				=							intermediate_size
									_a					:				List[Any]				=							hidden_act
									_a					:				int				=							hidden_dropout_prob
									_a					:				Optional[int]				=							attention_probs_dropout_prob
									_a					:				Optional[int]				=							initializer_range
									_a					:				Tuple				=							num_labels
									_a					:				Tuple				=							backbone_featmap_shape
									_a					:				Optional[int]				=							scope
									_a					:				Dict				=							is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									_a					:				str				=							(image_size // patch_size) ** 2
									_a					:				Optional[int]				=							num_patches + 1







							def 							__lowercase      (							self :	Dict		):
									_a					:				str				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a					:				Any				=							None
									if self.use_labels:
											_a					:				Union[str, Any]				=							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 							__lowercase      (							self :	int		):
									_a					:				Dict				=							{
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									    'hidden_sizes': [96, 192, 384, 768],
									    'num_groups': 2,
									}

									return DPTConfig(
									    image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,backbone_out_indices=self.backbone_out_indices     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=_UpperCAmelCase     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=_UpperCAmelCase     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Optional[Any]				=							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[Any]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							self.num_labels
									_a					:				Dict				=							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				str				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[Any]		):
									_a					:				Optional[int]				=							self.num_labels
									_a					:				Optional[int]				=							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[int]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[str]				=							self.prepare_config_and_inputs()
									_a	, _a	, _a					:				List[Any]				=							config_and_inputs
									_a					:				List[Any]				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase	:   int           = (
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Dict           = False
							lowerCAmelCase	:   List[str]           = False







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Union[str, Any]				=							DPTModelTester(self		)
									_a					:				int				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase     ,hidden_size=37		)







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







							@unittest.skip(reason='DPT does not use inputs_embeds'		)
							def 							__lowercase      (							self :	Tuple		):
									pass







							def 							__lowercase      (							self :	List[str]		):
									_a	, _a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)
											self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)		)
											_a					:				List[Any]				=							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCAmelCase     ,nn.Linear		)		)







							def 							__lowercase      (							self :	Any		):
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a					:				Optional[Any]				=							['pixel_values']
											self.assertListEqual(arg_names[:1]     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a	, _a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Tuple				=							True

											if model_class in get_values(_UpperCAmelCase		):
													continue

											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a					:				Optional[int]				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Optional[Any]				=							False
											_a					:				List[Any]				=							True

											if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
													continue
											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a					:				int				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase      (							self :	str		):
									_a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				List[str]				=							_config_zero_init(_UpperCAmelCase		)
									for model_class in self.all_model_classes:
											_a					:				Tuple				=							model_class(config=_UpperCAmelCase		)
											# Skip the check for the backbone
											_a					:				Any				=							[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															_a					:				Union[str, Any]				=							[F"""{name}.{key}""" for key in module.state_dict().keys()]
															break

											for name, param in model.named_parameters():
													if param.requires_grad:
															if name in backbone_params:
																	continue
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item()     ,[0.0, 1.0]     ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,)







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
							def 							__lowercase      (							self :	Optional[Any]		):
									pass







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a					:				List[Any]				=							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
									_a					:				Optional[Any]				=							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a					:				List[str]				=							DPTForDepthEstimation(_UpperCAmelCase		)



def 					__lowerCamelCase					(				)      ->				Optional[Any]:
		_a					:				Optional[Any]				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[Any]				=							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a					:				Union[str, Any]				=							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a					:				Optional[int]				=							prepare_img()
									_a					:				Optional[Any]				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				Tuple				=							model(**_UpperCAmelCase		)
											_a					:				Union[str, Any]				=							outputs.predicted_depth

									# verify the predicted depth
									_a					:				str				=							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape     ,_UpperCAmelCase		)

									_a					:				int				=							torch.tensor(
									    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]		).to(_UpperCAmelCase		)

									self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,_UpperCAmelCase     ,atol=1E-4		)		)




