




'''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 		a							:


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


				def     A_					(						self       :   Any   ):
									snake_case_		     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

									snake_case_		     =							self.get_config()

									return config, pixel_values, labels


				def     A_					(						self       :   str   ):
									snake_case_		     =							{
									    '''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=lowercase_			,							initializer_range=self.initializer_range			,							is_hybrid=self.is_hybrid			,							backbone_config=lowercase_			,							backbone_featmap_shape=self.backbone_featmap_shape			,							)


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Dict			,							lowercase_       :   str			,							lowercase_       :   List[str]   ):
									snake_case_		     =							DPTModel(config=lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   str			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict   ):
									snake_case_		     =							self.num_labels
									snake_case_		     =							DPTForDepthEstimation(lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.predicted_depth.shape			,							(self.batch_size, self.image_size, self.image_size)   )


				def     A_					(						self       :   Dict			,							lowercase_       :   List[Any]			,							lowercase_       :   Optional[int]			,							lowercase_       :   Dict   ):
									snake_case_		     =							self.num_labels
									snake_case_		     =							DPTForSemanticSegmentation(lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_			,							labels=lowercase_   )
									self.parent.assertEqual(
									    result.logits.shape			,							(self.batch_size, self.num_labels, self.image_size, self.image_size)   )


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







@require_torch
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				snake_case_           =						(
				    {
				        "depth-estimation": DPTForDepthEstimation,
				        "feature-extraction": DPTModel,
				        "image-segmentation": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							DPTModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							has_text_modality=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   str   ):
									self.config_tester.run_common_tests()


				@unittest.skip(reason='''DPT does not use inputs_embeds'''   )
				def     A_					(						self       :   List[str]   ):
									pass


				def     A_					(						self       :   Dict   ):
									snake_case_     ,snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()

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


				def     A_					(						self       :   List[Any]   ):
									snake_case_     ,snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_		     =							['''pixel_values''']
														self.assertListEqual(arg_names[:1]			,							lowercase_   )


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_   )


				def     A_					(						self       :   Dict   ):
									for model_class in self.all_model_classes:
														if model_class.__name__ == "DPTForDepthEstimation":
																			continue

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

														if model_class in get_values(lowercase_   ):
																			continue

														snake_case_		     =							model_class(lowercase_   )
														model.to(lowercase_   )
														model.train()
														snake_case_		     =							self._prepare_for_class(lowercase_			,							lowercase_			,							return_labels=lowercase_   )
														snake_case_		     =							model(**lowercase_   ).loss
														loss.backward()


				def     A_					(						self       :   Tuple   ):
									for model_class in self.all_model_classes:
														if model_class.__name__ == "DPTForDepthEstimation":
																			continue

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

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


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_     ,snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()

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


				@slow
				def     A_					(						self       :   int   ):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
														snake_case_		     =							DPTModel.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )


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







def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

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






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


				def     A_					(						self       :   Any   ):
									snake_case_		     =							DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''   )
									snake_case_		     =							DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''   ).to(lowercase_   )

									snake_case_		     =							prepare_img()
									snake_case_		     =							image_processor(images=lowercase_			,							return_tensors='''pt'''   ).to(lowercase_   )

									# forward pass
									with torch.no_grad():
														snake_case_		     =							model(**lowercase_   )
														snake_case_		     =							outputs.predicted_depth

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

									snake_case_		     =							torch.tensor(
									    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(lowercase_   )

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







'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 		a							(  unittest.TestCase	):
				snake_case_           =						ViTImageProcessor if is_vision_available() else None


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							(3, 32, 128)
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )

									snake_case_		     =							os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']   )
									with open(self.vocab_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														fp.write(json.dumps(lowercase_   ) + '''\n'''   )

									snake_case_		     =							{
									    '''do_normalize''': False,
									    '''do_resize''': True,
									    '''image_processor_type''': '''ViTImageProcessor''',
									    '''resample''': 3,
									    '''size''': {'''height''': 32, '''width''': 128},
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   str   ):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   List[Any]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )

									snake_case_		     =							Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   )

									return image_input


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )
									snake_case_		     =							MgpstrProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_			,							padding_value=1.0   )

									snake_case_		     =							MgpstrProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_			,							padding_value=1.0   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor_add_kwargs.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							return_tensors='''np'''   )

									for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum()			,							input_processor[key].sum()			,							delta=1e-2   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''

									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							tokenizer(lowercase_   )
									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''pixel_values''', '''labels''']   )

									# test if it raises when no input is passed
									with pytest.raises(lowercase_   ):
														processor()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									snake_case_		     =							processor.char_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )
									snake_case_		     =							[seq.replace(''' '''			,							''''''   ) for seq in decoded_tok]

									self.assertListEqual(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							None
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							processor.model_input_names   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							torch.randn(1			,							27			,							38   )
									snake_case_		     =							torch.randn(1			,							27			,							5_0257   )
									snake_case_		     =							torch.randn(1			,							27			,							3_0522   )

									snake_case_		     =							processor.batch_decode([char_input, bpe_input, wp_input]   )

									self.assertListEqual(list(results.keys()   )			,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']   )

