
"""simple docstring"""





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMAEForPreTraining, ViTMAEModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor



class __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Tuple							, _snake_case    :       Any							, _snake_case    :       int=13							, _snake_case    :       List[str]=30							, _snake_case    :       int=2							, _snake_case    :       List[Any]=3							, _snake_case    :       List[str]=True							, _snake_case    :       int=True							, _snake_case    :       List[Any]=32							, _snake_case    :       Tuple=5							, _snake_case    :       Tuple=4							, _snake_case    :       Optional[int]=37							, _snake_case    :       Any="gelu"							, _snake_case    :       Dict=0.1							, _snake_case    :       Any=0.1							, _snake_case    :       List[Any]=10							, _snake_case    :       Any=0.0_2							, _snake_case    :       Tuple=3							, _snake_case    :       int=0.6							, _snake_case    :       List[str]=None							, )   ->			Tuple:







								"""simple docstring"""
								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_								=    num_attention_heads
								A_								=    intermediate_size
								A_								=    hidden_act
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    type_sequence_label_size
								A_								=    initializer_range
								A_								=    mask_ratio
								A_								=    scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								A_								=    (image_size // patch_size) ** 2
								A_								=    int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			int:







								"""simple docstring"""
								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.type_sequence_label_size						)

								A_								=    self.get_config()

								return config, pixel_values, labels




			def   lowerCamelCase__  (					self    :       Any						)   ->			Tuple:







								"""simple docstring"""
								return ViTMAEConfig(
								    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							, 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=_snake_case							, initializer_range=self.initializer_range							, mask_ratio=self.mask_ratio							, )




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       int							, _snake_case    :       Tuple							, _snake_case    :       Optional[int]						)   ->			int:







								"""simple docstring"""
								A_								=    ViTMAEModel(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case						)
								self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Dict							, _snake_case    :       int							, _snake_case    :       Optional[Any]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    ViTMAEForPreTraining(_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case						)
								A_								=    (self.image_size // self.patch_size) ** 2
								A_								=    self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, num_patches, expected_num_channels)						)

								# test greyscale images
								A_								=    1
								A_								=    ViTMAEForPreTraining(_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								A_								=    model(_snake_case						)
								A_								=    self.patch_size**2
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, num_patches, expected_num_channels)						)






			def   lowerCamelCase__  (					self    :       int						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    self.prepare_config_and_inputs()
								A_			,     A_			,     A_								=    config_and_inputs
								A_								=    {"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class __lowerCAmelCase					(       _lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			snake_case						   =					{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

			snake_case						   =					False
			snake_case						   =					False
			snake_case						   =					False
			snake_case						   =					False




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    ViTMAEModelTester(self						)
								A_								=    ConfigTester(self							, config_class=_snake_case							, has_text_modality=_snake_case							, hidden_size=37						)




			def   lowerCamelCase__  (					self    :       str						)   ->			int:







								"""simple docstring"""
								self.config_tester.run_common_tests()




			@unittest.skip(reason="ViTMAE does not use inputs_embeds"						)
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			int:







								"""simple docstring"""
								pass




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Any:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

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




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			int:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_								=    model_class(_snake_case						)
													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]							, _snake_case						)




			def   lowerCamelCase__  (					self    :       int						)   ->			str:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_snake_case						)




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       int							, _snake_case    :       Optional[Any]							, _snake_case    :       List[Any]						)   ->			List[Any]:







								"""simple docstring"""
								# make masks reproducible
								np.random.seed(2						)

								A_								=    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2						)
								A_								=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
								A_								=    torch.from_numpy(_snake_case						)

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								A_								=    pt_noise

								super().check_pt_tf_models(_snake_case							, _snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_								=    model_class(_snake_case						)
													model.to(_snake_case						)
													model.eval()
													# make random mask reproducible
													torch.manual_seed(2						)
													with torch.no_grad():
																		A_								=    model(**self._prepare_for_class(_snake_case							, _snake_case						)						)

													A_								=    outputs[0].cpu().numpy()
													A_								=    0

													with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(_snake_case						)
																		A_								=    model_class.from_pretrained(_snake_case						)
																		model.to(_snake_case						)
																		# make random mask reproducible
																		torch.manual_seed(2						)
																		with torch.no_grad():
																							A_								=    model(**self._prepare_for_class(_snake_case							, _snake_case						)						)

																		# Make sure we don't have nans
																		A_								=    after_outputs[0].cpu().numpy()
																		A_								=    0
																		A_								=    np.amax(np.abs(out_a - out_a						)						)
																		self.assertLessEqual(_snake_case							, 1e-5						)




			@unittest.skip(
			    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			int:







								"""simple docstring"""
								pass




			@unittest.skip(
			    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
			def   lowerCamelCase__  (					self    :       str						)   ->			Tuple:







								"""simple docstring"""
								pass




			@unittest.skip(
			    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			int:







								"""simple docstring"""
								pass




			@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"						)
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Optional[int]:







								"""simple docstring"""
								pass




			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			List[Any]:







								"""simple docstring"""
								pass






			@slow
			def   lowerCamelCase__  (					self    :       int						)   ->			Optional[Any]:







								"""simple docstring"""
								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    ViTMAEModel.from_pretrained(_snake_case						)
													self.assertIsNotNone(_snake_case						)





def 				A_  ():

					'''simple docstring'''





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




@require_torch
@require_vision
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@cached_property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			int:







								"""simple docstring"""
								return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"						) if is_vision_available() else None






			@slow
			def   lowerCamelCase__  (					self    :       str						)   ->			Tuple:







								"""simple docstring"""
								# make random mask reproducible across the PT and TF model
								np.random.seed(2						)

								A_								=    ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"						).to(_snake_case						)

								A_								=    self.default_image_processor
								A_								=    prepare_img()
								A_								=    image_processor(images=_snake_case							, return_tensors="pt"						).to(_snake_case						)

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								A_								=    ViTMAEConfig()
								A_								=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
								A_								=    np.random.uniform(size=(1, num_patches)						)

								# forward pass
								with torch.no_grad():
													A_								=    model(**_snake_case							, noise=torch.from_numpy(_snake_case						).to(device=_snake_case						)						)

								# verify the logits
								A_								=    torch.Size((1, 196, 768)						)
								self.assertEqual(outputs.logits.shape							, _snake_case						)

								A_								=    torch.tensor(
								    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]						)

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






"""simple docstring"""





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 				A_  (__a					, __a					, __a					, __a					, __a=True					, __a="pt"					):

					'''simple docstring'''





					A_								=    {"add_prefix_space": True} if isinstance(__a					, __a					) and not line.startswith(" "					) else {}
					A_								=    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 				A_  (__a					, __a					, __a=None					, ):

					'''simple docstring'''





					A_								=    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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			def __init__(					self    :       Optional[Any]							, _snake_case    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       int							, _snake_case    :       Tuple							, _snake_case    :       Dict="train"							, _snake_case    :       List[Any]=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Any=""							, )   ->			List[str]:







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




			def __len__(					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								return len(self.src_lens						)




			def __getitem__(					self    :       str							, _snake_case    :       Optional[int]						)   ->			Dict[str, torch.Tensor]:







								"""simple docstring"""
								A_								=    index + 1  # linecache starts at 1
								A_								=    self.prefix + linecache.getline(str(self.src_file						)							, _snake_case						).rstrip("\n"						)
								A_								=    linecache.getline(str(self.tgt_file						)							, _snake_case						).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							, _snake_case						):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

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

								A_								=    encode_line(_snake_case							, _snake_case							, self.max_source_length							, "right"						)
								A_								=    encode_line(_snake_case							, _snake_case							, self.max_target_length							, "right"						)

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




			@staticmethod
			def   lowerCamelCase__  (					_snake_case    :       List[Any]						)   ->			str:







								"""simple docstring"""
								return [len(_snake_case						) for x in Path(_snake_case						).open().readlines()]






			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       List[str]						)   ->			Dict[str, torch.Tensor]:







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





UpperCamelCase_       :    Any						      =			getLogger(__name__)
def 				A_  (__a					):

					'''simple docstring'''





					return list(itertools.chain.from_iterable(__a					)					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    get_git_info()
					save_json(__a					, os.path.join(__a					, "git_log.json"					)					)
def 				A_  (__a					, __a					, __a=4					, **__a					):

					'''simple docstring'''





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

					'''simple docstring'''





					with open(__a					) as f:
										return json.load(__a					)
def 				A_  ():

					'''simple docstring'''





					A_								=    git.Repo(search_parent_directories=__a					)
					A_								=    {
					    "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 				A_  (__a					, __a					):

					'''simple docstring'''





					return list(map(__a					, __a					)					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					with open(__a					, "wb"					) as f:
										return pickle.dump(__a					, __a					)
def 				A_  (__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					):
										A_								=    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 				A_  (__a					, __a					):

					'''simple docstring'''





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

					'''simple docstring'''





					return normalize_answer(__a					) == normalize_answer(__a					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					assert len(__a					) == len(__a					)
					A_								=    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 				A_  (__a					):

					'''simple docstring'''





					return model_prefix.startswith("rag"					)




def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





					A_								=    {p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					A_								=    "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
															A_								=    p if hasattr(__a					, __a					) else equivalent_param[p]
															setattr(__a					, __a					, getattr(__a					, __a					)					)
															delattr(__a					, __a					)
					return hparams, config




