'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						A_	   = "platform"

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Dict,      _lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			int=None,      _lowerCAmelCase    :			Tuple=None)    ->		Any:



						"""simple docstring"""

						if attention_mask is None:
												snake_case :       Optional[Any]				=    np.where(input_ids != config.pad_token_id,      1,      0)
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}




@require_flax
class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=13				,			snake_case_=7				,			snake_case_=True				,			snake_case_=False				,			snake_case_=99				,			snake_case_=16				,			snake_case_=2				,			snake_case_=4				,			snake_case_=4				,			snake_case_="gelu"				,			snake_case_=0.1				,			snake_case_=0.1				,			snake_case_=20				,			snake_case_=2				,			snake_case_=1				,			snake_case_=0				,			snake_case_=16				,			snake_case_=16				,			snake_case_=0.02				,			):
									snake_case :       List[str]				=    parent
									snake_case :       Optional[int]				=    batch_size
									snake_case :       str				=    seq_length
									snake_case :       List[str]				=    is_training
									snake_case :       Optional[int]				=    use_labels
									snake_case :       Tuple				=    vocab_size
									snake_case :       List[Any]				=    hidden_size
									snake_case :       Any				=    num_hidden_layers
									snake_case :       Any				=    num_attention_heads
									snake_case :       List[str]				=    intermediate_size
									snake_case :       Optional[int]				=    hidden_act
									snake_case :       List[Any]				=    hidden_dropout_prob
									snake_case :       Optional[int]				=    attention_probs_dropout_prob
									snake_case :       str				=    max_position_embeddings
									snake_case :       Optional[Any]				=    eos_token_id
									snake_case :       Optional[int]				=    pad_token_id
									snake_case :       str				=    bos_token_id
									snake_case :       List[str]				=    embed_dim
									snake_case :       Optional[int]				=    word_embed_proj_dim
									snake_case :       Union[str, Any]				=    initializer_range
									snake_case :       Dict				=    False



			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,			self.vocab_size					)				,			3				,			self.vocab_size					)
									snake_case :       Optional[Any]				=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,			dtype=np.intaa					))				,			-1					)

									snake_case :       Any				=    OPTConfig(
									    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				,			is_encoder_decoder=_a				,			word_embed_proj_dim=self.word_embed_proj_dim				,			initializer_range=self.initializer_range				,			use_cache=_a				,			)
									snake_case :       int				=    prepare_opt_inputs_dict(_a				,			_a					)
									return config, inputs_dict



			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    self.prepare_config_and_inputs()
									return config, inputs_dict



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       int				=    20
									snake_case :       Dict				=    model_class_name(_a					)

									snake_case :       Optional[Any]				=    inputs_dict["input_ids"]
									snake_case :       Dict				=    inputs_dict["attention_mask"]

									snake_case :       Optional[Any]				=    model.init_cache(input_ids.shape[0]				,			_a					)
									snake_case :       List[str]				=    jnp.ones((input_ids.shape[0], max_length)				,			dtype="i4"					)

									snake_case :       Optional[int]				=    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]				,			(input_ids.shape[0], input_ids.shape[-1] - 1)				,			)
									snake_case :       int				=    model(
									    input_ids[:, :-1]				,			attention_mask=_a				,			past_key_values=_a				,			position_ids=_a				,			)

									snake_case :       Union[str, Any]				=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,			dtype="i4"					)
									snake_case :       List[str]				=    model(
									    input_ids[:, -1:]				,			attention_mask=_a				,			past_key_values=outputs_cache.past_key_values				,			position_ids=_a				,			)

									snake_case :       List[str]				=    model(_a					)

									snake_case :       int				=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1e-3				,			msg=F"""Max diff is {diff}"""					)






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Union[str, Any]				=    20
									snake_case :       List[str]				=    model_class_name(_a					)

									snake_case :       Tuple				=    (
									    inputs_dict["input_ids"],
									    inputs_dict["attention_mask"],
									)

									snake_case :       Any				=    jnp.concatenate(
									    [
									        attention_mask,
									        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
									    ]				,			axis=-1				,			)

									snake_case :       int				=    model.init_cache(input_ids.shape[0]				,			_a					)
									snake_case :       Optional[int]				=    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]				,			(input_ids.shape[0], input_ids.shape[-1] - 1)				,			)

									snake_case :       Optional[Any]				=    model(
									    input_ids[:, :-1]				,			attention_mask=_a				,			past_key_values=_a				,			position_ids=_a				,			)
									snake_case :       Optional[Any]				=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,			dtype="i4"					)
									snake_case :       Optional[int]				=    model(
									    input_ids[:, -1:]				,			past_key_values=outputs_cache.past_key_values				,			attention_mask=_a				,			position_ids=_a				,			)

									snake_case :       Any				=    model(_a				,			attention_mask=_a					)

									snake_case :       Union[str, Any]				=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1e-3				,			msg=F"""Max diff is {diff}"""					)







@require_flax
class 		SCREAMING_SNAKE_CASE_					(_a							,				unittest.TestCase							,				_a      ):



			"""simple docstring"""






			__magic_name__ :				Optional[Any]   							=       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
			__magic_name__ :				int   							=       () if is_flax_available() else ()



			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    FlaxOPTModelTester(self					)



			def   lowerCamelCase (							self					):
									snake_case :       int				=    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(_a				,			_a				,			_a					)



			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(_a				,			_a				,			_a					)






			@slow
			def   lowerCamelCase (							self					):
									for model_class_name in self.all_model_classes:
															snake_case :       Tuple				=    model_class_name.from_pretrained("facebook/opt-125m"					)
															snake_case :       Union[str, Any]				=    np.ones((1, 1)					) * model.config.eos_token_id
															snake_case :       Optional[int]				=    model(_a					)
															self.assertIsNotNone(_a					)







@require_sentencepiece
@require_flax
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			@slow
			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    FlaxOPTModel.from_pretrained("facebook/opt-350m"					)
									snake_case :       List[Any]				=    jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					)
									snake_case :       Any				=    model(input_ids=_a					).last_hidden_state
									snake_case :       Tuple				=    (1, 11, 512)
									self.assertEqual(output.shape				,			_a					)
									snake_case :       int				=    jnp.array(
									    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]					)
									self.assertTrue(jnp.allclose(output[:, :3, :3]				,			_a				,			atol=4e-2					)					)







@require_flax
@slow
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									super().setUp()
									snake_case :       Dict				=    "facebook/opt-350m"






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
									snake_case :       Any				=    GPTaTokenizer.from_pretrained(self.path_model					)

									snake_case :       Any				=    [
									    "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
									snake_case :       Optional[Any]				=    tokenizer(_a				,			return_tensors="jax"				,			padding=_a				,			add_special_tokens=_a					)
									snake_case :       int				=    model(inputs.input_ids				,			attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
									snake_case :       List[str]				=    jnp.array(
									    [
									        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
									        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
									        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
									        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
									    ]					)
									self.assertTrue(jnp.allclose(_a				,			_a				,			atol=4e-2					)					)

									snake_case :       Optional[int]				=    jax.jit(_a					)
									snake_case :       int				=    model(inputs.input_ids				,			attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
									self.assertTrue(jnp.allclose(_a				,			_a				,			atol=4e-2					)					)







@require_flax
@slow
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			@property
			def   lowerCamelCase (							self					):
									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   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    "facebook/opt-125m"

									snake_case :       str				=    [
									    "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",
									]

									snake_case :       Union[str, Any]				=    []

									snake_case :       Optional[Any]				=    FlaxOPTForCausalLM.from_pretrained(_a					)
									snake_case :       Tuple				=    GPTaTokenizer.from_pretrained(_a					)

									for prompt in self.prompts:
															snake_case :       int				=    tokenizer(_a				,			return_tensors="jax"					).input_ids

															snake_case :       Any				=    model.generate(_a				,			max_length=10					)
															snake_case :       List[Any]				=    generated_ids[0]

															snake_case :       Any				=    tokenizer.batch_decode(_a				,			skip_special_tokens=_a					)
															predicted_outputs += generated_string

									self.assertListEqual(_a				,			_a					)



			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    "facebook/opt-350m"

									snake_case :       List[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",
									]

									snake_case :       Optional[Any]				=    []
									snake_case :       Optional[int]				=    FlaxOPTForCausalLM.from_pretrained(_a					)
									snake_case :       str				=    GPTaTokenizer.from_pretrained(_a					)

									for prompt in self.prompts:
															snake_case :       Any				=    tokenizer(_a				,			return_tensors="jax"					).input_ids

															snake_case :       Optional[int]				=    model.generate(_a				,			max_length=10					)
															snake_case :       Tuple				=    generated_ids[0]

															snake_case :       Optional[Any]				=    tokenizer.batch_decode(_a				,			skip_special_tokens=_a					)
															predicted_outputs += generated_string

									self.assertListEqual(_a				,			_a					)



			def   lowerCamelCase (							self					):
									snake_case :       Any				=    "facebook/opt-125m"
									snake_case :       int				=    [
									    "Today is a beautiful day and I want to thank",
									    "In the city of Rome Canaver Canaver Canaver Canaver",
									]
									snake_case :       Union[str, Any]				=    FlaxOPTForCausalLM.from_pretrained(_a					)
									snake_case :       List[Any]				=    GPTaTokenizer.from_pretrained(_a					)
									snake_case :       Optional[Any]				=    tokenizer(
									    [
									        "Today is a beautiful day and I want to",
									        "In the city of",
									    ]				,			return_tensors="jax"				,			padding=_a				,			)

									snake_case :       Optional[int]				=    jax.jit(model.generate					)

									snake_case :       Optional[int]				=    jit_generate(inputs["input_ids"]				,			attention_mask=inputs["attention_mask"]					).sequences

									snake_case :       str				=    tokenizer.batch_decode(_a				,			skip_special_tokens=_a					)

									self.assertIsNotNone(_a				,			_a					)






			def   lowerCamelCase (							self					):
									snake_case :       int				=    "facebook/opt-350m"

									snake_case :       List[str]				=    GPTaTokenizer.from_pretrained(_a					)
									snake_case :       Optional[int]				=    FlaxOPTForCausalLM.from_pretrained(_a					)

									snake_case :       List[Any]				=    "left"

									# use different length sentences to test batching
									snake_case :       Any				=    [
									    "Hello, my dog is a little",
									    "Today, I",
									]

									snake_case :       str				=    tokenizer(_a				,			return_tensors="jax"				,			padding=_a					)
									snake_case :       Any				=    inputs["input_ids"]

									snake_case :       List[str]				=    model.generate(input_ids=_a				,			attention_mask=inputs["attention_mask"]				,			trace=_a					)

									snake_case :       Any				=    tokenizer(sentences[0]				,			return_tensors="jax"					).input_ids
									snake_case :       Optional[Any]				=    model.generate(input_ids=_a					)

									snake_case :       str				=    inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
									snake_case :       Union[str, Any]				=    tokenizer(sentences[1]				,			return_tensors="jax"					).input_ids
									snake_case :       Dict				=    model.generate(input_ids=_a				,			max_length=model.config.max_length - num_paddings					)

									snake_case :       List[str]				=    tokenizer.batch_decode(outputs[0]				,			skip_special_tokens=_a					)
									snake_case :       str				=    tokenizer.decode(output_non_padded[0][0]				,			skip_special_tokens=_a					)
									snake_case :       Optional[Any]				=    tokenizer.decode(output_padded[0][0]				,			skip_special_tokens=_a					)

									snake_case :       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(_a				,			_a					)
									self.assertListEqual(_a				,			[non_padded_sentence, padded_sentence]					)










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 						lowerCamelCase__			:


						def __init__(			self    :	List[str]   ,							_a    :	Any   ,							_a    :	str=2   ,							_a    :	List[str]=3_2   ,							_a    :	str=1_6   ,							_a    :	Tuple=3   ,							_a    :	Any=True   ,							_a    :	Optional[Any]=True   ,							_a    :	Optional[Any]=3_2   ,							_a    :	Optional[Any]=4   ,							_a    :	str=[0, 1, 2, 3]   ,							_a    :	Any=4   ,							_a    :	List[Any]=3_7   ,							_a    :	Optional[Any]="gelu"   ,							_a    :	str=0.1   ,							_a    :	Optional[Any]=0.1   ,							_a    :	Optional[Any]=0.0_2   ,							_a    :	Optional[int]=3   ,							_a    :	List[str]=False   ,							_a    :	Dict=None   ,							):
													a__:							List[Any]									=parent
													a__:							List[str]									=batch_size
													a__:							Optional[Any]									=image_size
													a__:							Optional[Any]									=patch_size
													a__:							Dict									=num_channels
													a__:							Optional[int]									=is_training
													a__:							List[Any]									=use_labels
													a__:							List[str]									=hidden_size
													a__:							int									=num_hidden_layers
													a__:							List[Any]									=backbone_out_indices
													a__:							int									=num_attention_heads
													a__:							List[Any]									=intermediate_size
													a__:							int									=hidden_act
													a__:							int									=hidden_dropout_prob
													a__:							Union[str, Any]									=attention_probs_dropout_prob
													a__:							Tuple									=initializer_range
													a__:							Dict									=num_labels
													a__:							Tuple									=scope
													a__:							List[Any]									=is_hybrid
													# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
													a__:							Any									=(image_size // patch_size) ** 2
													a__:							Tuple									=num_patches + 1


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							str									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

													a__:							Union[str, Any]									=self.get_config()

													return config, pixel_values, labels


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													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=_a   ,							initializer_range=self.initializer_range   ,							is_hybrid=self.is_hybrid   ,							)


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


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	Tuple   ,							_a    :	Union[str, Any]   ,							_a    :	List[Any]      ):
													a__:							Union[str, Any]									=self.num_labels
													a__:							List[str]									=DPTForDepthEstimation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Optional[int]									=model(_a      )
													self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size)      )


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







						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							List[str]									=self.prepare_config_and_inputs()
													a__				,      a__				,      a__:							Union[str, Any]									=config_and_inputs
													a__:							Tuple									={"pixel_values": pixel_values}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase__			(		_a					,       _a					,       unittest.TestCase ):
						_lowerCAmelCase             =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						_lowerCAmelCase             =			(
						    {
						        '''depth-estimation''': DPTForDepthEstimation,
						        '''feature-extraction''': DPTModel,
						        '''image-segmentation''': DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Dict									=DPTModelTester(self      )
													a__:							int									=ConfigTester(self   ,							config_class=_a   ,							has_text_modality=_a   ,							hidden_size=3_7      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.config_tester.run_common_tests()


						@unittest.skip(reason="DPT does not use inputs_embeds"      )
						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													pass


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

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


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__				,      a__:							int									=self.model_tester.prepare_config_and_inputs_for_common()

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

																				a__:							List[str]									=["pixel_values"]
																				self.assertListEqual(arg_names[:1]   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							List[str]									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*_a      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													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[int]									=True

																				if model_class in get_values(_a      ):
																											continue

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


						def 	_lowerCamelCase      (			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__:							Union[str, Any]									=False
																				a__:							Any									=True

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


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__				,      a__:							str									=self.model_tester.prepare_config_and_inputs_for_common()

													a__:							Tuple									=_config_zero_init(_a      )
													for model_class in self.all_model_classes:
																				a__:							List[Any]									=model_class(config=_a      )
																				# Skip the check for the backbone
																				a__:							List[Any]									=[]
																				for name, module in model.named_modules():
																											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																																		a__:							Optional[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 	_lowerCamelCase      (			self    :	Dict      ):
													pass







						@slow
						def 	_lowerCamelCase      (			self    :	str      ):
													for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:							List[Any]									=DPTModel.from_pretrained(_a      )
																				self.assertIsNotNone(_a      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							return image







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


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Any									=DPTImageProcessor.from_pretrained("Intel/dpt-large"      )
													a__:							str									=DPTForDepthEstimation.from_pretrained("Intel/dpt-large"      ).to(_a      )

													a__:							Optional[int]									=prepare_img()
													a__:							List[Any]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )
																				a__:							List[str]									=outputs.predicted_depth

													# verify the predicted depth
													a__:							Optional[int]									=torch.Size((1, 3_8_4, 3_8_4)      )
													self.assertEqual(predicted_depth.shape   ,							_a      )

													a__:							Union[str, Any]									=torch.tensor(
													    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      ).to(_a      )

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


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Optional[Any]									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							Union[str, Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							Dict									=prepare_img()
													a__:							Optional[int]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )

													# verify the logits
													a__:							int									=torch.Size((1, 1_5_0, 4_8_0, 4_8_0)      )
													self.assertEqual(outputs.logits.shape   ,							_a      )

													a__:							Any									=torch.tensor(
													    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      ).to(_a      )

													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,							_a   ,							atol=1e-4      )      )







						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							Dict									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							List[Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							int									=prepare_img()
													a__:							Any									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							Optional[int]									=model(**_a      )

													a__:							Union[str, Any]									=outputs.logits.detach().cpu()

													a__:							List[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a   ,							target_sizes=[(5_0_0, 3_0_0)]      )
													a__:							Dict									=torch.Size((5_0_0, 3_0_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )

													a__:							Optional[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a      )
													a__:							List[str]									=torch.Size((4_8_0, 4_8_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )





