

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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=True		,					UpperCAmelCase__="pt"      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=None		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							input_ids.ne(UpperCAmelCase__      ).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     snake_case_			(				_a							):


							"""simple docstring"""





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




							def __len__( self							):
												return len(self.src_lens							)




							def __getitem__( self					,						_A							):
												__lowerCAmelCase					=							index + 1  # linecache starts at 1
												__lowerCAmelCase					=							self.prefix + linecache.getline(str(self.src_file							)					,						_A							).rstrip('\n'							)
												__lowerCAmelCase					=							linecache.getline(str(self.tgt_file							)					,						_A							).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					,						_A							):
																	source_line += self.tokenizer.eos_token
																	tgt_line += self.tokenizer.eos_token

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

												__lowerCAmelCase					=							encode_line(_A					,						_A					,						self.max_source_length					,						'right'							)
												__lowerCAmelCase					=							encode_line(_A					,						_A					,						self.max_target_length					,						'right'							)

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




							@staticmethod
							def        A__  ( _A							):
												return [len(_A							) for x in Path(_A							).open().readlines()]




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							torch.stack([x['input_ids'] for x in batch]							)
												__lowerCAmelCase					=							torch.stack([x['attention_mask'] for x in batch]							)
												__lowerCAmelCase					=							torch.stack([x['decoder_input_ids'] for x in batch]							)
												__lowerCAmelCase					=							(
												    self.tokenizer.generator.pad_token_id
												    if isinstance(self.tokenizer					,						_A							)
												    else self.tokenizer.pad_token_id
												)
												__lowerCAmelCase					=							(
												    self.tokenizer.question_encoder.pad_token_id
												    if isinstance(self.tokenizer					,						_A							)
												    else self.tokenizer.pad_token_id
												)
												__lowerCAmelCase					=							trim_batch(_A					,						_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							trim_batch(_A					,						_A					,						attention_mask=_A							)
												__lowerCAmelCase					=							{
												    'input_ids': source_ids,
												    'attention_mask': source_mask,
												    'decoder_input_ids': y,
												}
												return batch
lowerCamelCase      				=     getLogger(__name__)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							get_git_info()
					save_json(UpperCAmelCase__		,					os.path.join(UpperCAmelCase__		,					'git_log.json'      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=4		,					**UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					'w'      ) as f:
										json.dump(UpperCAmelCase__		,					UpperCAmelCase__		,					indent=UpperCAmelCase__		,					**UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return list(map(UpperCAmelCase__		,					UpperCAmelCase__      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					'wb'      ) as f:
										return pickle.dump(UpperCAmelCase__		,					UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

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

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

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

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__      )      )      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return normalize_answer(UpperCAmelCase__      ) == normalize_answer(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					return model_prefix.startswith('rag'      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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









import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												return TimmBackboneConfig(
												    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												__lowerCAmelCase,	__lowerCAmelCase					=							config_and_inputs
												__lowerCAmelCase					=							{'pixel_values': pixel_values}
												return config, inputs_dict





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												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        A__  ( self							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(len(timm_model.stage_names							)					,						len(transformers_model.stage_names							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices					,						(-1,)							)
												self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names							) - 1]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						out_indices=[1, 2, 3]							)

												self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices							)
												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)




							@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	__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]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

												# no need to test all models as different heads yield the same functionality
												__lowerCAmelCase					=							self.all_model_classes[0]
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

												# Encoder-/Decoder-only models
												__lowerCAmelCase					=							outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	__lowerCAmelCase					=							outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=_A							)

												self.assertIsNotNone(hidden_states.grad							)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						len(config.out_indices							)							)
																	self.assertEqual(len(model.channels							)					,						len(config.out_indices							)							)

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						1							)
																	self.assertEqual(len(model.channels							)					,						1							)

																	# Check backbone can be initialized with fresh weights
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							False
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)






