






'''simple docstring'''







import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image




class 		UpperCamelCase__     :




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase=3 ,_lowerCAmelCase=32 ,_lowerCAmelCase=3 ,_lowerCAmelCase=10 ,_lowerCAmelCase=[8, 16, 32, 64] ,_lowerCAmelCase=[1, 1, 2, 1] ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase="relu" ,_lowerCAmelCase=3 ,_lowerCAmelCase=None ,_lowerCAmelCase=["stage2", "stage3", "stage4"] ,_lowerCAmelCase=[2, 3, 4] ,_lowerCAmelCase=1 ,):
								lowerCamelCase__								=	parent
								lowerCamelCase__								=	batch_size
								lowerCamelCase__								=	image_size
								lowerCamelCase__								=	num_channels
								lowerCamelCase__								=	embeddings_size
								lowerCamelCase__								=	hidden_sizes
								lowerCamelCase__								=	depths
								lowerCamelCase__								=	is_training
								lowerCamelCase__								=	use_labels
								lowerCamelCase__								=	hidden_act
								lowerCamelCase__								=	num_labels
								lowerCamelCase__								=	scope
								lowerCamelCase__								=	len(_lowerCAmelCase							)
								lowerCamelCase__								=	out_features
								lowerCamelCase__								=	out_indices
								lowerCamelCase__								=	num_groups






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

								lowerCamelCase__								=	self.get_config()

								return config, pixel_values, labels






		def       UpperCamelCase_       (   self							):
								return BitConfig(
								    num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,out_features=self.out_features ,out_indices=self.out_indices ,num_groups=self.num_groups ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	BitModel(config=_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase							)
								self.parent.assertEqual(
								    result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	self.num_labels
								lowerCamelCase__								=	BitForImageClassification(_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase ,labels=_lowerCAmelCase							)
								self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels)							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	BitBackbone(config=_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							) ,len(config.out_features							)							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							) ,[self.batch_size, self.hidden_sizes[1], 4, 4]							)

								# verify channels
								self.parent.assertEqual(len(model.channels							) ,len(config.out_features							)							)
								self.parent.assertListEqual(model.channels ,config.hidden_sizes[1:]							)

								# verify backbone works with out_features=None
								lowerCamelCase__								=	None
								lowerCamelCase__								=	BitBackbone(config=_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							) ,1							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							) ,[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

								# verify channels
								self.parent.assertEqual(len(model.channels							) ,1							)
								self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]]							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	config_and_inputs
								lowerCamelCase__								=	{"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class 		UpperCamelCase__     (a ,a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		_UpperCamelCase						=       (
		    {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_UpperCamelCase						=       False
		_UpperCamelCase						=       False
		_UpperCamelCase						=       False
		_UpperCamelCase						=       False
		_UpperCamelCase						=       False






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	BitModelTester(self							)
								lowerCamelCase__								=	ConfigTester(self ,config_class=_lowerCAmelCase ,has_text_modality=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()






		def       UpperCamelCase_       (   self							):
								return






		@unittest.skip(reason="""Bit does not output attentions"""							)
		def       UpperCamelCase_       (   self							):
								pass






		@unittest.skip(reason="""Bit does not use inputs_embeds"""							)
		def       UpperCamelCase_       (   self							):
								pass






		@unittest.skip(reason="""Bit does not support input and output embeddings"""							)
		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCamelCase__								=	["""pixel_values"""]
														self.assertListEqual(arg_names[:1] ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__								=	model_class(config=_lowerCAmelCase							)
														for name, module in model.named_modules():
																				if isinstance(_lowerCAmelCase ,(nn.BatchNormad, nn.GroupNorm)							):
																										self.assertTrue(
																										    torch.all(module.weight == 1							) ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,)
																										self.assertTrue(
																										    torch.all(module.bias == 0							) ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,)






		def       UpperCamelCase_       (   self							):
								def check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
														lowerCamelCase__								=	model_class(_lowerCAmelCase							)
														model.to(_lowerCAmelCase							)
														model.eval()

														with torch.no_grad():
																				lowerCamelCase__								=	model(**self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase							)							)

														lowerCamelCase__								=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														lowerCamelCase__								=	self.model_tester.num_stages
														self.assertEqual(len(_lowerCAmelCase							) ,expected_num_stages + 1							)

														# Bit's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]							) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)

								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__								=	["""preactivation""", """bottleneck"""]
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				lowerCamelCase__								=	layer_type
																				lowerCamelCase__								=	True
																				check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				lowerCamelCase__								=	True

																				check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		@unittest.skip(reason="""Bit does not use feedforward chunking"""							)
		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase							)






		@slow
		def       UpperCamelCase_       (   self							):
								for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCamelCase__								=	BitModel.from_pretrained(_lowerCAmelCase							)
														self.assertIsNotNone(_lowerCAmelCase							)



def     A__      (					):
						lowerCamelCase__								=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
						return image




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




		'''simple docstring'''





		@cached_property
		def       UpperCamelCase_       (   self							):
								return (
								    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
								)






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(_lowerCAmelCase							)

								lowerCamelCase__								=	self.default_image_processor
								lowerCamelCase__								=	prepare_img()
								lowerCamelCase__								=	image_processor(images=_lowerCAmelCase ,return_tensors="""pt"""							).to(_lowerCAmelCase							)

								# forward pass
								with torch.no_grad():
														lowerCamelCase__								=	model(**_lowerCAmelCase							)

								# verify the logits
								lowerCamelCase__								=	torch.Size((1, 10_00)							)
								self.assertEqual(outputs.logits.shape ,_lowerCAmelCase							)

								lowerCamelCase__								=	torch.tensor([[-0.6526, -0.5263, -1.4398]]							).to(_lowerCAmelCase							)

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




@require_torch
class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       (BitBackbone,) if is_torch_available() else ()
		_UpperCamelCase						=       BitConfig

		_UpperCamelCase						=       False






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	BitModelTester(self							)



"""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




