


'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
							snake_case			:      Dict 							=   'platform'

							import jax
							import jax.numpy as jnp

							from transformers import AutoTokenizer
							from transformers.models.mbart.modeling_flax_mbart import (
							    FlaxMBartForConditionalGeneration,
							    FlaxMBartForQuestionAnswering,
							    FlaxMBartForSequenceClassification,
							    FlaxMBartModel,
							    shift_tokens_right,
							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,):

				"""simple docstring"""






				if attention_mask is None:
								_SCREAMING_SNAKE_CASE      = np.where(input_ids != config.pad_token_id   ,1   ,0							)
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE      = np.where(decoder_input_ids != config.pad_token_id   ,1   ,0							)
				if head_mask is None:
								_SCREAMING_SNAKE_CASE      = np.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE      = np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE      = np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}



class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=13				, A_=7				, A_=True				, A_=False				, A_=99				, A_=16				, A_=2				, A_=4				, A_=4				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=32				, A_=2				, A_=1				, A_=0				, A_=2				, A_=0.02				, )->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = seq_length
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = vocab_size
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = max_position_embeddings
											_SCREAMING_SNAKE_CASE      = eos_token_id
											_SCREAMING_SNAKE_CASE      = pad_token_id
											_SCREAMING_SNAKE_CASE      = bos_token_id
											_SCREAMING_SNAKE_CASE      = decoder_start_token_id
											_SCREAMING_SNAKE_CASE      = initializer_range





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				, self.vocab_size	)				, 3				, self.vocab_size	)
											_SCREAMING_SNAKE_CASE      = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				, dtype=np.intaa	))				, -1	)

											_SCREAMING_SNAKE_CASE      = shift_tokens_right(A_				, 1	)

											_SCREAMING_SNAKE_CASE      = MBartConfig(
											    vocab_size=self.vocab_size				, d_model=self.hidden_size				, encoder_layers=self.num_hidden_layers				, decoder_layers=self.num_hidden_layers				, encoder_attention_heads=self.num_attention_heads				, decoder_attention_heads=self.num_attention_heads				, encoder_ffn_dim=self.intermediate_size				, decoder_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				, decoder_start_token_id=self.decoder_start_token_id				, initializer_range=self.initializer_range				, use_cache=A_				, )
											_SCREAMING_SNAKE_CASE      = prepare_mbart_inputs_dict(A_				, A_				, A_	)
											return config, inputs_dict





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											return config, inputs_dict





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = 20
											_SCREAMING_SNAKE_CASE      = model_class_name(A_	)

											_SCREAMING_SNAKE_CASE      = model.encode(inputs_dict['input_ids']	)

											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											_SCREAMING_SNAKE_CASE      = model.init_cache(decoder_input_ids.shape[0]				, A_				, A_	)
											_SCREAMING_SNAKE_CASE      = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				, dtype='i4'	)

											_SCREAMING_SNAKE_CASE      = jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]				, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				, )
											_SCREAMING_SNAKE_CASE      = model.decode(
											    decoder_input_ids[:, :-1]				, A_				, decoder_attention_mask=A_				, past_key_values=A_				, decoder_position_ids=A_				, )

											_SCREAMING_SNAKE_CASE      = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				, dtype='i4'	)
											_SCREAMING_SNAKE_CASE      = model.decode(
											    decoder_input_ids[:, -1:]				, A_				, decoder_attention_mask=A_				, past_key_values=outputs_cache.past_key_values				, decoder_position_ids=A_				, )

											_SCREAMING_SNAKE_CASE      = model.decode(A_				, A_	)

											_SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = 20
											_SCREAMING_SNAKE_CASE      = model_class_name(A_	)

											_SCREAMING_SNAKE_CASE      = model.encode(inputs_dict['input_ids']	)

											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											_SCREAMING_SNAKE_CASE      = jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
											    ]				, axis=-1				, )

											_SCREAMING_SNAKE_CASE      = model.init_cache(decoder_input_ids.shape[0]				, A_				, A_	)
											_SCREAMING_SNAKE_CASE      = jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]				, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				, )

											_SCREAMING_SNAKE_CASE      = model.decode(
											    decoder_input_ids[:, :-1]				, A_				, decoder_attention_mask=A_				, past_key_values=A_				, decoder_position_ids=A_				, )
											_SCREAMING_SNAKE_CASE      = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				, dtype='i4'	)
											_SCREAMING_SNAKE_CASE      = model.decode(
											    decoder_input_ids[:, -1:]				, A_				, past_key_values=outputs_cache.past_key_values				, decoder_attention_mask=A_				, decoder_position_ids=A_				, )

											_SCREAMING_SNAKE_CASE      = model.decode(A_				, A_				, decoder_attention_mask=A_	)

											_SCREAMING_SNAKE_CASE      = 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    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						99





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = np.array(
											    [
											        [71, 82, 18, 33, 46, 91, 2],
											        [68, 34, 26, 58, 30, 82, 2],
											        [5, 97, 17, 39, 94, 40, 2],
											        [76, 83, 94, 25, 70, 78, 2],
											        [87, 59, 41, 35, 48, 66, 2],
											        [55, 13, 16, 58, 5, 2, 1],  # note padding
											        [64, 27, 31, 51, 12, 75, 2],
											        [52, 64, 86, 17, 83, 39, 2],
											        [48, 61, 9, 24, 71, 82, 2],
											        [26, 1, 60, 48, 22, 13, 2],
											        [21, 5, 62, 28, 14, 76, 2],
											        [45, 98, 37, 86, 59, 48, 2],
											        [70, 70, 50, 9, 28, 0, 2],
											    ]				, dtype=np.intaa				, )

											_SCREAMING_SNAKE_CASE      = input_ids.shape[0]
											_SCREAMING_SNAKE_CASE      = MBartConfig(
											    vocab_size=self.vocab_size				, d_model=24				, encoder_layers=2				, decoder_layers=2				, encoder_attention_heads=2				, decoder_attention_heads=2				, encoder_ffn_dim=32				, decoder_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





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForSequenceClassification(A_	)
											_SCREAMING_SNAKE_CASE      = model(input_ids=A_				, decoder_input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (batch_size, config.num_labels)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForQuestionAnswering(A_	)
											_SCREAMING_SNAKE_CASE      = model(input_ids=A_	)

											self.assertEqual(outputs['start_logits'].shape				, input_ids.shape	)
											self.assertEqual(outputs['end_logits'].shape				, input_ids.shape	)





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration(A_	)
											_SCREAMING_SNAKE_CASE      = lm_model(input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = MBartConfig(
											    vocab_size=self.vocab_size				, d_model=14				, encoder_layers=2				, decoder_layers=2				, encoder_attention_heads=2				, decoder_attention_heads=2				, encoder_ffn_dim=8				, decoder_ffn_dim=8				, max_position_embeddings=48				, )
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration(A_	)
											_SCREAMING_SNAKE_CASE      = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = lm_model(input_ids=A_				, decoder_input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = shift_tokens_right(A_				, 1	)
											_SCREAMING_SNAKE_CASE      = np.equal(A_				, 1	).astype(np.floataa	).sum()
											_SCREAMING_SNAKE_CASE      = np.equal(A_				, 1	).astype(np.floataa	).sum()
											self.assertEqual(shifted.shape				, input_ids.shape	)
											self.assertEqual(A_				, n_pad_before - 1	)
											self.assertTrue(np.equal(shifted[:, 0]				, 2	).all()	)







@require_flax
class    __lowercase  (       UpperCAmelCase_      ,     unittest.TestCase      ,     UpperCAmelCase_							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[str]        =						True
							SCREAMING_SNAKE_CASE   :					Union[str, Any]        =						(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = FlaxMBartModelTester(self	)





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = 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_	)





							def 		__magic_name__			(    self	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__	):
																			_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_	)
																			_SCREAMING_SNAKE_CASE      = model_class(A_	)

																			@jax.jit
																			def encode_jitted(A_				, A_=None				, **A_	):
																							return model.encode(input_ids=A_				, attention_mask=A_	)

																			with self.subTest('JIT Enabled'	):
																							_SCREAMING_SNAKE_CASE      = encode_jitted(**A_	).to_tuple()

																			with self.subTest('JIT Disabled'	):
																							with jax.disable_jit():
																											_SCREAMING_SNAKE_CASE      = encode_jitted(**A_	).to_tuple()

																			self.assertEqual(len(A_	)				, len(A_	)	)
																			for jitted_output, output in zip(A_				, A_	):
																							self.assertEqual(jitted_output.shape				, output.shape	)





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__	):
																			_SCREAMING_SNAKE_CASE      = model_class(A_	)
																			_SCREAMING_SNAKE_CASE      = model.encode(inputs_dict['input_ids']				, inputs_dict['attention_mask']	)

																			_SCREAMING_SNAKE_CASE      = {
																			    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																			    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																			    'encoder_outputs': encoder_outputs,
																			}

																			@jax.jit
																			def decode_jitted(A_				, A_				, A_	):
																							return model.decode(
																							    decoder_input_ids=A_				, decoder_attention_mask=A_				, encoder_outputs=A_				, )

																			with self.subTest('JIT Enabled'	):
																							_SCREAMING_SNAKE_CASE      = decode_jitted(**A_	).to_tuple()

																			with self.subTest('JIT Disabled'	):
																							with jax.disable_jit():
																											_SCREAMING_SNAKE_CASE      = decode_jitted(**A_	).to_tuple()

																			self.assertEqual(len(A_	)				, len(A_	)	)
																			for jitted_output, output in zip(A_				, A_	):
																							self.assertEqual(jitted_output.shape				, output.shape	)





							@slow
							def 		__magic_name__			(    self	)->	str:
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class_name.from_pretrained('facebook/mbart-large-cc25'				, from_pt=A_	)
															# FlaxMBartForSequenceClassification expects eos token in input_ids
															_SCREAMING_SNAKE_CASE      = np.ones((1, 1)	) * model.config.eos_token_id
															_SCREAMING_SNAKE_CASE      = model(A_	)
															self.assertIsNotNone(A_	)







@require_flax
@require_sentencepiece
@require_tokenizers
class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					str        =						[
							    " UN Chief Says There Is No Military Solution in Syria",
							]
							SCREAMING_SNAKE_CASE   :					str        =						[
							    "Şeful ONU declară că nu există o soluţie militară în Siria",
							]
							SCREAMING_SNAKE_CASE   :					Any        =						"facebook/mbart-large-en-ro"





							@cached_property
							def 		__magic_name__			(    self	)->	int:
											return AutoTokenizer.from_pretrained(self.model_name	)





							@cached_property
							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				, from_pt=A_	)
											return model





							def 		__magic_name__			(    self				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = self.translate_src_text(**A_	)
											self.assertListEqual(self.expected_text				, A_	)





							def 		__magic_name__			(    self				, **A_	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.tokenizer(self.src_text				, **A_				, return_tensors='np'	)
											_SCREAMING_SNAKE_CASE      = self.model.generate(
											    model_inputs.input_ids				, attention_mask=model_inputs.attention_mask				, decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']				, early_stopping=A_				, num_beams=2				, ).sequences
											_SCREAMING_SNAKE_CASE      = self.tokenizer.batch_decode(A_				, skip_special_tokens=A_	)
											return generated_words





							@slow
							def 		__magic_name__			(    self	)->	List[str]:
											self._assert_generated_batch_equal_expected()




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    __lowercase  :

							"""simple docstring"""


							def __init__(    self				, A_				, A_=2				, A_=32				, A_=16				, A_=3				, A_=True				, A_=True				, A_=32				, A_=4				, A_=[0, 1, 2, 3]				, A_=4				, A_=37				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=0.02				, A_=3				, A_=False				, A_=None				, )->	int:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = image_size
											_SCREAMING_SNAKE_CASE      = patch_size
											_SCREAMING_SNAKE_CASE      = num_channels
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = backbone_out_indices
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = initializer_range
											_SCREAMING_SNAKE_CASE      = num_labels
											_SCREAMING_SNAKE_CASE      = scope
											_SCREAMING_SNAKE_CASE      = is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											_SCREAMING_SNAKE_CASE      = (image_size // patch_size) ** 2
											_SCREAMING_SNAKE_CASE      = num_patches + 1





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

											_SCREAMING_SNAKE_CASE      = self.get_config()

											return config, pixel_values, labels





							def 		__magic_name__			(    self	)->	Optional[int]:
											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 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = DPTModel(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, labels=A_	)
											self.parent.assertEqual(
											    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = config_and_inputs
											_SCREAMING_SNAKE_CASE      = {'pixel_values': pixel_values}
											return config, inputs_dict







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

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE   :					Any        =						(
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE   :					Tuple        =						False
							SCREAMING_SNAKE_CASE   :					str        =						False
							SCREAMING_SNAKE_CASE   :					Dict        =						False





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = DPTModelTester(self	)
											_SCREAMING_SNAKE_CASE      = ConfigTester(self				, config_class=A_				, has_text_modality=A_				, hidden_size=37	)





							def 		__magic_name__			(    self	)->	str:
											self.config_tester.run_common_tests()





							@unittest.skip(reason='DPT does not use inputs_embeds'	)
							def 		__magic_name__			(    self	)->	Optional[Any]:
											pass





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)	)
															_SCREAMING_SNAKE_CASE      = model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(A_				, nn.Linear	)	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

															_SCREAMING_SNAKE_CASE      = ['pixel_values']
															self.assertListEqual(arg_names[:1]				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*A_	)





							def 		__magic_name__			(    self	)->	List[Any]:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

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

															if model_class in get_values(A_	):
																			continue

															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	Any:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()
															_SCREAMING_SNAKE_CASE      = False
															_SCREAMING_SNAKE_CASE      = True

															if model_class in get_values(A_	) or not model_class.supports_gradient_checkpointing:
																			continue
															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.gradient_checkpointing_enable()
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE      = _config_zero_init(A_	)
											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class(config=A_	)
															# Skip the check for the backbone
															_SCREAMING_SNAKE_CASE      = []
															for name, module in model.named_modules():
																			if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																							_SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self	)->	List[Any]:
											pass





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE      = DPTModel.from_pretrained(A_	)
															self.assertIsNotNone(A_	)







def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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



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

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)
															_SCREAMING_SNAKE_CASE      = outputs.predicted_depth

											# verify the predicted depth
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 384, 384)	)
											self.assertEqual(predicted_depth.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											# verify the logits
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 150, 480, 480)	)
											self.assertEqual(outputs.logits.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											_SCREAMING_SNAKE_CASE      = outputs.logits.detach().cpu()

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_				, target_sizes=[(500, 300)]	)
											_SCREAMING_SNAKE_CASE      = torch.Size((500, 300)	)
											self.assertEqual(segmentation[0].shape				, A_	)

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_	)
											_SCREAMING_SNAKE_CASE      = torch.Size((480, 480)	)
											self.assertEqual(segmentation[0].shape				, A_	)
