




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 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Tuple       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     str=2       ,	__lowercase							:     Optional[int]=32       ,	__lowercase							:     List[str]=16       ,	__lowercase							:     List[Any]=3       ,	__lowercase							:     List[Any]=True       ,	__lowercase							:     Dict=True       ,	__lowercase							:     Dict=32       ,	__lowercase							:     List[Any]=4       ,	__lowercase							:     Dict=[0, 1, 2, 3]       ,	__lowercase							:     List[str]=4       ,	__lowercase							:     int=37       ,	__lowercase							:     List[Any]="gelu"       ,	__lowercase							:     Dict=0.1       ,	__lowercase							:     str=0.1       ,	__lowercase							:     Dict=0.02       ,	__lowercase							:     Union[str, Any]=3       ,	__lowercase							:     List[str]=[1, 3_84, 24, 24]       ,	__lowercase							:     Optional[int]=True       ,	__lowercase							:     int=None       ,	):






							"""simple docstring"""






							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        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     {
							    "global_padding": "same",
							    "layer_type": "bottleneck",
							    "depths": [3, 4, 9],
							    "out_features": ["stage1", "stage2", "stage3"],
							    "embedding_dynamic_padding": True,
							    "hidden_sizes": [96, 1_92, 3_84, 7_68],
							    "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        snake_case__						(						self							:     List[str]       ,	__lowercase							:     List[str]       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     Tuple						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Any       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     List[str]       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     str       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							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 		UpperCAmelCase   (				UpperCAmelCase__       ,   UpperCAmelCase__       ,   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        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							snake_case_		    =     DPTModelTester(self						)
							snake_case_		    =     ConfigTester(self       ,	config_class=__lowercase       ,	has_text_modality=__lowercase       ,	hidden_size=37						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						@unittest.skip(reason="DPT does not use inputs_embeds"						)
						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							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        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__lowercase						)






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase						)






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							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        snake_case__						(						self							:     str						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							pass






						@slow
						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
								snake_case_		    =     DPTModel.from_pretrained(__lowercase						)
								self.assertIsNotNone(__lowercase						)






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							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 						lowerCamelCase__    (		):







	'''simple docstring'''





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






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







						'''simple docstring'''





						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							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, 3_84, 3_84)						)
							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] / 1_00       ,	__lowercase       ,	atol=1E-4						)						)






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A=True     ,	_A="pt"							):







	'''simple docstring'''





	snake_case_		    =     {"add_prefix_space": True} if isinstance(_A     ,	_A							) and not line.startswith(" "							) else {}
	snake_case_		    =     padding_side
	return tokenizer(
	    [line]     ,	max_length=_A     ,	padding="max_length" if pad_to_max_length else None     ,	truncation=_A     ,	return_tensors=_A     ,	add_special_tokens=_A     ,	**_A     ,	)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A=None     ,	):







	'''simple docstring'''





	snake_case_		    =     input_ids.ne(_A							).any(dim=0							)
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def __init__(						self							:     int       ,	__lowercase							:     List[Any]       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[Any]       ,	__lowercase							:     str       ,	__lowercase							:     Tuple="train"       ,	__lowercase							:     List[str]=None       ,	__lowercase							:     List[Any]=None       ,	__lowercase							:     Optional[Any]=None       ,	__lowercase							:     Union[str, Any]=""       ,	):






							"""simple docstring"""






							super().__init__()
							snake_case_		    =     Path(__lowercase						).joinpath(type_path + ".source"						)
							snake_case_		    =     Path(__lowercase						).joinpath(type_path + ".target"						)
							snake_case_		    =     self.get_char_lens(self.src_file						)
							snake_case_		    =     max_source_length
							snake_case_		    =     max_target_length
							assert min(self.src_lens						) > 0, f"found empty line in {self.src_file}"
							snake_case_		    =     tokenizer
							snake_case_		    =     prefix
							if n_obs is not None:
								snake_case_		    =     self.src_lens[:n_obs]
							snake_case_		    =     src_lang
							snake_case_		    =     tgt_lang






						def __len__(						self							:     List[Any]						):






							"""simple docstring"""






							return len(self.src_lens						)






						def __getitem__(						self							:     List[Any]       ,	__lowercase							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     index + 1  # linecache starts at 1
							snake_case_		    =     self.prefix + linecache.getline(str(self.src_file						)       ,	__lowercase						).rstrip("\n"						)
							snake_case_		    =     linecache.getline(str(self.tgt_file						)       ,	__lowercase						).rstrip("\n"						)
							assert source_line, f"empty source line for index {index}"
							assert tgt_line, f"empty tgt line for index {index}"

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer       ,	__lowercase						):
								source_line += self.tokenizer.eos_token
								tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							snake_case_		    =     (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,	__lowercase						) else self.tokenizer
							)
							snake_case_		    =     self.tokenizer.generator if isinstance(self.tokenizer       ,	__lowercase						) else self.tokenizer

							snake_case_		    =     encode_line(__lowercase       ,	__lowercase       ,	self.max_source_length       ,	"right"						)
							snake_case_		    =     encode_line(__lowercase       ,	__lowercase       ,	self.max_target_length       ,	"right"						)

							snake_case_		    =     source_inputs["input_ids"].squeeze()
							snake_case_		    =     target_inputs["input_ids"].squeeze()
							snake_case_		    =     source_inputs["attention_mask"].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}






						@staticmethod
						def        snake_case__						(						__lowercase							:     Optional[int]						):






							"""simple docstring"""






							return [len(__lowercase						) for x in Path(__lowercase						).open().readlines()]






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							snake_case_		    =     torch.stack([x["input_ids"] for x in batch]						)
							snake_case_		    =     torch.stack([x["attention_mask"] for x in batch]						)
							snake_case_		    =     torch.stack([x["decoder_input_ids"] for x in batch]						)
							snake_case_		    =     (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer       ,	__lowercase						)
							    else self.tokenizer.pad_token_id
							)
							snake_case_		    =     (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer       ,	__lowercase						)
							    else self.tokenizer.pad_token_id
							)
							snake_case_		    =     trim_batch(__lowercase       ,	__lowercase						)
							snake_case_    ,      snake_case_		    =     trim_batch(__lowercase       ,	__lowercase       ,	attention_mask=__lowercase						)
							snake_case_		    =     {
							    "input_ids": source_ids,
							    "attention_mask": source_mask,
							    "decoder_input_ids": y,
							}
							return batch







lowercase__				:     str 		=						getLogger(__name__)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return list(itertools.chain.from_iterable(_A							)							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     get_git_info()
	save_json(_A     ,	os.path.join(_A     ,	"git_log.json"							)							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A=4     ,	**_A							):







	'''simple docstring'''





	with open(_A     ,	"w"							) as f:
		json.dump(_A     ,	_A     ,	indent=_A     ,	**_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	with open(_A							) as f:
		return json.load(_A							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     git.Repo(search_parent_directories=_A							)
	snake_case_		    =     {
	    "repo_id": str(_A							),
	    "repo_sha": str(repo.head.object.hexsha							),
	    "repo_branch": str(repo.active_branch							),
	    "hostname": str(socket.gethostname()							),
	}
	return repo_infos


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	return list(map(_A     ,	_A							)							)


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	with open(_A     ,	"wb"							) as f:
		return pickle.dump(_A     ,	_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	def remove_articles(_A							):
		return re.sub(R"\b(a|an|the)\b"     ,	" "     ,	_A							)

	def white_space_fix(_A							):
		return " ".join(text.split()							)

	def remove_punc(_A							):
		snake_case_		    =     set(string.punctuation							)
		return "".join(ch for ch in text if ch not in exclude							)

	def lower(_A							):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_A							)							)							)							)


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     normalize_answer(_A							).split()
	snake_case_		    =     normalize_answer(_A							).split()
	snake_case_		    =     Counter(_A							) & Counter(_A							)
	snake_case_		    =     sum(common.values()							)
	if num_same == 0:
		return 0
	snake_case_		    =     1.0 * num_same / len(_A							)
	snake_case_		    =     1.0 * num_same / len(_A							)
	snake_case_		    =     (2 * precision * recall) / (precision + recall)
	return fa


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	return normalize_answer(_A							) == normalize_answer(_A							)


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	assert len(_A							) == len(_A							)
	snake_case_		    =     0
	for hypo, pred in zip(_A     ,	_A							):
		em += exact_match_score(_A     ,	_A							)
	if len(_A							) > 0:
		em /= len(_A							)
	return {"em": em}


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return model_prefix.startswith("rag"							)






def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	snake_case_		    =     "dropout_rate"
	for p in extra_params:
		if getattr(_A     ,	_A     ,	_A							):
			if not hasattr(_A     ,	_A							) and not hasattr(_A     ,	equivalent_param[p]							):
				logger.info("config doesn't have a `{}` attribute".format(_A							)							)
				delattr(_A     ,	_A							)
				continue
			snake_case_		    =     p if hasattr(_A     ,	_A							) else equivalent_param[p]
			setattr(_A     ,	_A     ,	getattr(_A     ,	_A							)							)
			delattr(_A     ,	_A							)
	return hparams, config
