






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   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

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

							def 					__A       (     self:      Tuple      )			-> Tuple:
												_A									=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

												_A									=       self.get_config()

												return config, pixel_values, labels

							def 					__A       (     self:      Dict      )			-> 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 					__A       (     self:      Tuple						,   __A:      int						,   __A:      int						,   __A:      List[Any]      )			-> Tuple:
												_A									=       DPTModel(config=__A      )
												model.to(__A      )
												model.eval()
												_A									=       model(__A      )
												self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)      )

							def 					__A       (     self:      Dict						,   __A:      Union[str, Any]						,   __A:      Dict						,   __A:      Dict      )			-> List[Any]:
												_A									=       self.num_labels
												_A									=       DPTForDepthEstimation(__A      )
												model.to(__A      )
												model.eval()
												_A									=       model(__A      )
												self.parent.assertEqual(result.predicted_depth.shape						,   (self.batch_size, self.image_size, self.image_size)      )

							def 					__A       (     self:      int						,   __A:      int						,   __A:      List[Any]						,   __A:      Tuple      )			-> int:
												_A									=       self.num_labels
												_A									=       DPTForSemanticSegmentation(__A      )
												model.to(__A      )
												model.eval()
												_A									=       model(__A						,   labels=__A      )
												self.parent.assertEqual(
												    result.logits.shape						,   (self.batch_size, self.num_labels, self.image_size, self.image_size)      )



							def 					__A       (     self:      Union[str, Any]      )			-> str:
												_A									=       self.prepare_config_and_inputs()
												_A							,_A							,_A									=       config_and_inputs
												_A									=       {'''pixel_values''': pixel_values}
												return config, inputs_dict





@require_torch
class   SCREAMING_SNAKE_CASE (       snake_case     ,		snake_case     ,		unittest.TestCase    ):





							"""simple docstring"""

							A_       =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							A_       =    (
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							A_       =    False
							A_       =    False
							A_       =    False

							def 					__A       (     self:      Union[str, Any]      )			-> Optional[int]:
												_A									=       DPTModelTester(self      )
												_A									=       ConfigTester(self						,   config_class=__A						,   has_text_modality=__A						,   hidden_size=37      )

							def 					__A       (     self:      Optional[Any]      )			-> str:
												self.config_tester.run_common_tests()

							@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
							def 					__A       (     self:      str      )			-> str:
												pass

							def 					__A       (     self:      str      )			-> Any:
												_A							,_A									=       self.model_tester.prepare_config_and_inputs_for_common()

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

							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A							,_A									=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	_A									=       ['''pixel_values''']
																	self.assertListEqual(arg_names[:1]						,   __A      )

							def 					__A       (     self:      Optional[int]      )			-> str:
												_A									=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A      )

							def 					__A       (     self:      Optional[Any]      )			-> Optional[int]:
												_A									=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*__A      )

							def 					__A       (     self:      Optional[Any]      )			-> Any:
												_A									=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*__A      )

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

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

																	if model_class in get_values(__A      ):
																						continue

																	_A									=       model_class(__A      )
																	model.to(__A      )
																	model.train()
																	_A									=       self._prepare_for_class(__A						,   __A						,   return_labels=__A      )
																	_A									=       model(**__A      ).loss
																	loss.backward()

							def 					__A       (     self:      Optional[int]      )			-> Dict:
												for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																						continue

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

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

							def 					__A       (     self:      Tuple      )			-> Dict:
												_A							,_A									=       self.model_tester.prepare_config_and_inputs_for_common()

												_A									=       _config_zero_init(__A      )
												for model_class in self.all_model_classes:
																	_A									=       model_class(config=__A      )
																	# Skip the check for the backbone
																	_A									=       []
																	for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																											_A									=       [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:      Optional[int]      )			-> Union[str, Any]:
												pass



							@slow
							def 					__A       (     self:      Tuple      )			-> Union[str, Any]:
												for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A									=       DPTModel.from_pretrained(__A      )
																	self.assertIsNotNone(__A      )




def    __A    (   ):

					'''simple docstring'''







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





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





							"""simple docstring"""

							def 					__A       (     self:      Dict      )			-> int:
												_A									=       DPTImageProcessor.from_pretrained('''Intel/dpt-large'''      )
												_A									=       DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''      ).to(__A      )

												_A									=       prepare_img()
												_A									=       image_processor(images=__A						,   return_tensors='''pt'''      ).to(__A      )

												# forward pass
												with torch.no_grad():
																	_A									=       model(**__A      )
																	_A									=       outputs.predicted_depth

												# verify the predicted depth
												_A									=       torch.Size((1, 3_84, 3_84)      )
												self.assertEqual(predicted_depth.shape						,   __A      )

												_A									=       torch.tensor(
												    [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]      ).to(__A      )

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

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

												_A									=       prepare_img()
												_A									=       image_processor(images=__A						,   return_tensors='''pt'''      ).to(__A      )

												# forward pass
												with torch.no_grad():
																	_A									=       model(**__A      )

												# verify the logits
												_A									=       torch.Size((1, 1_50, 4_80, 4_80)      )
												self.assertEqual(outputs.logits.shape						,   __A      )

												_A									=       torch.tensor(
												    [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]      ).to(__A      )

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



							def 					__A       (     self:      Dict      )			-> List[str]:
												_A									=       DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''      )
												_A									=       DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''      ).to(__A      )

												_A									=       prepare_img()
												_A									=       image_processor(images=__A						,   return_tensors='''pt'''      ).to(__A      )

												# forward pass
												with torch.no_grad():
																	_A									=       model(**__A      )

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

												_A									=       image_processor.post_process_semantic_segmentation(outputs=__A						,   target_sizes=[(5_00, 3_00)]      )
												_A									=       torch.Size((5_00, 3_00)      )
												self.assertEqual(segmentation[0].shape						,   __A      )

												_A									=       image_processor.post_process_semantic_segmentation(outputs=__A      )
												_A									=       torch.Size((4_80, 4_80)      )
												self.assertEqual(segmentation[0].shape						,   __A      )











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__A          = logging.get_logger(__name__)

__A          = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A          = {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

__A          = {
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    VOCAB_FILES_NAMES
							A_       =    PRETRAINED_VOCAB_FILES_MAP
							A_       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_       =    ["input_ids", "attention_mask"]
							A_       =    LongformerTokenizer

							def __init__(     self:      List[str]						,   __A:      Dict=None						,   __A:      int=None						,   __A:      Tuple=None						,   __A:      Any="replace"						,   __A:      List[str]="<s>"						,   __A:      int="</s>"						,   __A:      Dict="</s>"						,   __A:      Tuple="<s>"						,   __A:      Optional[int]="<unk>"						,   __A:      Optional[int]="<pad>"						,   __A:      List[Any]="<mask>"						,   __A:      Tuple=False						,   __A:      Dict=True						,   **__A:      List[Any]						,   )			-> List[Any]:
												super().__init__(
												    __A						,   __A						,   tokenizer_file=__A						,   errors=__A						,   bos_token=__A						,   eos_token=__A						,   sep_token=__A						,   cls_token=__A						,   unk_token=__A						,   pad_token=__A						,   mask_token=__A						,   add_prefix_space=__A						,   trim_offsets=__A						,   **__A						,   )

												_A									=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																	_A									=       getattr(__A						,   pre_tok_state.pop('''type'''      )      )
																	_A									=       add_prefix_space
																	_A									=       pre_tok_class(**__A      )

												_A									=       add_prefix_space

												_A									=       '''post_processor'''
												_A									=       getattr(self.backend_tokenizer						,   __A						,   __A      )
												if tokenizer_component_instance:
																	_A									=       json.loads(tokenizer_component_instance.__getstate__()      )

																	# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																	if "sep" in state:
																						_A									=       tuple(state['''sep''']      )
																	if "cls" in state:
																						_A									=       tuple(state['''cls''']      )

																	_A									=       False

																	if state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																						_A									=       add_prefix_space
																						_A									=       True

																	if state.get('''trim_offsets'''						,   __A      ) != trim_offsets:
																						_A									=       trim_offsets
																						_A									=       True

																	if changes_to_apply:
																						_A									=       getattr(__A						,   state.pop('''type'''      )      )
																						_A									=       component_class(**__A      )
																						setattr(self.backend_tokenizer						,   __A						,   __A      )

							@property
							def 					__A       (     self:      Union[str, Any]      )			-> str:
												if self._mask_token is None:
																	if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''      )
																	return None
												return str(self._mask_token      )

							@mask_token.setter
							def 					__A       (     self:      Optional[int]						,   __A:      str      )			-> Union[str, Any]:
												_A									=       AddedToken(__A						,   lstrip=__A						,   rstrip=__A      ) if isinstance(__A						,   __A      ) else value
												_A									=       value

							def 					__A       (     self:      Optional[Any]						,   *__A:      Any						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )
												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      Optional[Any]						,   *__A:      List[str]						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )

												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      List[str]						,   __A:      str						,   __A:      Optional[str] = None      )			-> Tuple[str]:
												_A									=       self._tokenizer.model.save(__A						,   name=__A      )
												return tuple(__A      )

							def 					__A       (     self:      Tuple						,   __A:      Dict						,   __A:      Optional[Any]=None      )			-> Optional[int]:
												_A									=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																	return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



							def 					__A       (     self:      Optional[int]						,   __A:      List[int]						,   __A:      Optional[List[int]] = None      )			-> List[int]:
												_A									=       [self.sep_token_id]
												_A									=       [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]



