




"""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 lowerCAmelCase__			:






			'''simple docstring'''



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

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

							_lowerCamelCase  :    List[str]					=					self.get_config()

							return config, pixel_values, labels
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					{
							    '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						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					DPTModel(config=lowercase						)
							model.to(lowercase						)
							model.eval()
							_lowerCamelCase  :    Any					=					model(lowercase						)
							self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					self.num_labels
							_lowerCamelCase  :    Union[str, Any]					=					DPTForDepthEstimation(lowercase						)
							model.to(lowercase						)
							model.eval()
							_lowerCamelCase  :    Optional[Any]					=					model(lowercase						)
							self.parent.assertEqual(result.predicted_depth.shape						,   (self.batch_size, self.image_size, self.image_size)						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Any					=					self.num_labels
							_lowerCamelCase  :    Union[str, Any]					=					DPTForSemanticSegmentation(lowercase						)
							model.to(lowercase						)
							model.eval()
							_lowerCamelCase  :    List[str]					=					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						):
							_lowerCamelCase  :    List[Any]					=					self.prepare_config_and_inputs()
							_lowerCamelCase  :    List[Any]					=					config_and_inputs
							_lowerCamelCase  :    Union[str, Any]					=					{'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__			(  lowercase,			lowercase,			unittest.TestCase		):






			'''simple docstring'''



			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       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					DPTModelTester(self						)
							_lowerCamelCase  :    Any					=					ConfigTester(self						,   config_class=lowercase						,   has_text_modality=lowercase						,   hidden_size=37						)
			def       A_					(						self						):
							self.config_tester.run_common_tests()
			@unittest.skip(reason='DPT does not use inputs_embeds'						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase  :    Union[str, Any]					=					model_class(lowercase						)
											self.assertIsInstance(model.get_input_embeddings()						,   (nn.Module)						)
											_lowerCamelCase  :    Optional[int]					=					model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowercase						,   nn.Linear						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.model_tester.prepare_config_and_inputs_for_common()

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

											_lowerCamelCase  :    Optional[int]					=					['pixel_values']
											self.assertListEqual(arg_names[:1]						,   lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase						)
			def       A_					(						self						):
							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											_lowerCamelCase  :    List[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCamelCase  :    str					=					True

											if model_class in get_values(lowercase						):
															continue

											_lowerCamelCase  :    Dict					=					model_class(lowercase						)
											model.to(lowercase						)
											model.train()
											_lowerCamelCase  :    int					=					self._prepare_for_class(lowercase						,   lowercase						,   return_labels=lowercase						)
											_lowerCamelCase  :    Union[str, Any]					=					model(**lowercase						).loss
											loss.backward()
			def       A_					(						self						):
							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											_lowerCamelCase  :    List[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCamelCase  :    Any					=					False
											_lowerCamelCase  :    Dict					=					True

											if model_class in get_values(lowercase						) or not model_class.supports_gradient_checkpointing:
															continue
											_lowerCamelCase  :    Dict					=					model_class(lowercase						)
											model.to(lowercase						)
											model.gradient_checkpointing_enable()
											model.train()
											_lowerCamelCase  :    Any					=					self._prepare_for_class(lowercase						,   lowercase						,   return_labels=lowercase						)
											_lowerCamelCase  :    Union[str, Any]					=					model(**lowercase						).loss
											loss.backward()
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.model_tester.prepare_config_and_inputs_for_common()

							_lowerCamelCase  :    List[str]					=					_config_zero_init(lowercase						)
							for model_class in self.all_model_classes:
											_lowerCamelCase  :    Optional[int]					=					model_class(config=lowercase						)
											# Skip the check for the backbone
											_lowerCamelCase  :    List[str]					=					[]
											for name, module in model.named_modules():
															if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																			_lowerCamelCase  :    int					=					[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						):
							pass
			@slow
			def       A_					(						self						):
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_lowerCamelCase  :    List[Any]					=					DPTModel.from_pretrained(lowercase						)
											self.assertIsNotNone(lowercase						)






			def       A_					(						self						):
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							_lowerCamelCase  :    Any					=					self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase  :    Dict					=					'add'
							with self.assertRaises(lowercase						):
											_lowerCamelCase  :    Dict					=					DPTForDepthEstimation(lowercase						)







def   _snake_case    (       ):
				_lowerCamelCase  :    int					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
				return image






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






			'''simple docstring'''



			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'						)
							_lowerCamelCase  :    str					=					DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'						).to(lowercase						)

							_lowerCamelCase  :    int					=					prepare_img()
							_lowerCamelCase  :    str					=					image_processor(images=lowercase						,   return_tensors='pt'						).to(lowercase						)

							# forward pass
							with torch.no_grad():
											_lowerCamelCase  :    str					=					model(**lowercase						)
											_lowerCamelCase  :    int					=					outputs.predicted_depth

							# verify the predicted depth
							_lowerCamelCase  :    Optional[Any]					=					torch.Size((1, 384, 384)						)
							self.assertEqual(predicted_depth.shape						,   lowercase						)

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

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








"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		"""▁"""

lowercase__  		=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase__  		=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase__  		=		{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="</s>"						,   lowercase="<s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<mask>"						,   lowercase = None						,   **lowercase						,   ):
							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase  :    str					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else mask_token

							_lowerCamelCase  :    Union[str, Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=lowercase						,   eos_token=lowercase						,   unk_token=lowercase						,   sep_token=lowercase						,   cls_token=lowercase						,   pad_token=lowercase						,   mask_token=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowercase						,   )

							_lowerCamelCase  :    Any					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(lowercase						)						)
							_lowerCamelCase  :    str					=					vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_lowerCamelCase  :    List[str]					=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_lowerCamelCase  :    List[Any]					=					1

							_lowerCamelCase  :    List[Any]					=					len(self.sp_model						) + self.fairseq_offset
							_lowerCamelCase  :    int					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
			def __getstate__(						self						):
							_lowerCamelCase  :    Tuple					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.serialized_model_proto()
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					d

							# for backward compatibility
							if not hasattr(self						,   'sp_model_kwargs'						):
											_lowerCamelCase  :    str					=					{}

							_lowerCamelCase  :    List[str]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)
			def       A_					(						self						,   lowercase						,   lowercase = None						):

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowerCamelCase  :    Any					=					[self.cls_token_id]
							_lowerCamelCase  :    List[Any]					=					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
							return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Tuple					=					[self.sep_token_id]
							_lowerCamelCase  :    Optional[int]					=					[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]
			@property
			def       A_					(						self						):
							return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def       A_					(						self						,   lowercase						):
							return self.sp_model.encode(lowercase						,   out_type=lowercase						)
			def       A_					(						self						,   lowercase						):
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.PieceToId(lowercase						)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
			def       A_					(						self						,   lowercase						):
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					''.join(lowercase						).replace(lowercase						,   ' '						).strip()
							return out_string






			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    int					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.vocab_file						):
											copyfile(self.vocab_file						,   lowercase						)
							elif not os.path.isfile(self.vocab_file						):
											with open(lowercase						,   'wb'						) as fi:
															_lowerCamelCase  :    str					=					self.sp_model.serialized_model_proto()
															fi.write(lowercase						)

							return (out_vocab_file,)