



"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase  							=					logging.getLogger(__name__)




@dataclass
class 			lowercase__			:
							'''simple docstring'''







							_UpperCAmelCase      				=					field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
							_UpperCAmelCase      				=					field(
							    default=A,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
							_UpperCAmelCase      				=					field(
							    default='''NER''',					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}    )
							_UpperCAmelCase      				=					field(
							    default=A,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
							_UpperCAmelCase      				=					field(default=A,					metadata={'''help''': '''Set this flag to use fast tokenization.'''}    )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							_UpperCAmelCase      				=					field(
							    default=A,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},					)







@dataclass
class 			lowercase__			:
							'''simple docstring'''







							_UpperCAmelCase      				=					field(
							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}    )
							_UpperCAmelCase      				=					field(
							    default=A,					metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''},					)
							_UpperCAmelCase      				=					field(
							    default=1_28,					metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    },					)
							_UpperCAmelCase      				=					field(
							    default=A,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )







def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
		if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
		else:
				_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir       )
		    and os.listdir(training_args.output_dir       )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
				    ' --overwrite_output_dir to overcome.'       )

		_UpperCAmelCase							 =		import_module('tasks'       )
		try:
				_UpperCAmelCase							 =		getattr(A     ,				model_args.task_type       )
				_UpperCAmelCase							 =		token_classification_task_clazz()
		except AttributeError:
				raise ValueError(
				    f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
				    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'       )

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,				datefmt='%m/%d/%Y %H:%M:%S'     ,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,				)
		logger.warning(
		    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'     ,				training_args.local_rank     ,				training_args.device     ,				training_args.n_gpu     ,				bool(training_args.local_rank != -1       )     ,				training_args.fpaa     ,				)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank       ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('Training/evaluation parameters %s'     ,				A       )

		# Set seed
		set_seed(training_args.seed       )

		# Prepare CONLL-2003 task
		_UpperCAmelCase							 =		token_classification_task.get_labels(data_args.labels       )
		_UpperCAmelCase							 =		dict(enumerate(A       )       )
		_UpperCAmelCase							 =		len(A       )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		_UpperCAmelCase							 =		AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,				num_labels=A     ,				idalabel=A     ,				labelaid={label: i for i, label in enumerate(A       )}     ,				cache_dir=model_args.cache_dir     ,				)
		_UpperCAmelCase							 =		AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,				cache_dir=model_args.cache_dir     ,				use_fast=model_args.use_fast     ,				)
		_UpperCAmelCase							 =		AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path     ,				from_tf=bool('.ckpt' in model_args.model_name_or_path       )     ,				config=A     ,				cache_dir=model_args.cache_dir     ,				)

		# Get datasets
		_UpperCAmelCase							 =		(
		    TokenClassificationDataset(
		        token_classification_task=A     ,				data_dir=data_args.data_dir     ,				tokenizer=A     ,				labels=A     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.train     ,				)
		    if training_args.do_train
		    else None
		)
		_UpperCAmelCase							 =		(
		    TokenClassificationDataset(
		        token_classification_task=A     ,				data_dir=data_args.data_dir     ,				tokenizer=A     ,				labels=A     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.dev     ,				)
		    if training_args.do_eval
		    else None
		)

		def align_predictions(A	:						np.ndarray     ,				A	:						np.ndarray       ) -> Tuple[List[int], List[int]]:
				_UpperCAmelCase							 =		np.argmax(A     ,				axis=2       )

				_UpperCAmelCase ,					_UpperCAmelCase							 =		preds.shape

				_UpperCAmelCase							 =		[[] for _ in range(A       )]
				_UpperCAmelCase							 =		[[] for _ in range(A       )]

				for i in range(A       ):
						for j in range(A       ):
								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
										out_label_list[i].append(label_map[label_ids[i][j]]       )
										preds_list[i].append(label_map[preds[i][j]]       )

				return preds_list, out_label_list

		def compute_metrics(A	:						EvalPrediction       ) -> Dict:
				_UpperCAmelCase ,					_UpperCAmelCase							 =		align_predictions(p.predictions     ,				p.label_ids       )
				return {
				    "accuracy_score": accuracy_score(A     ,				A       ),
				    "precision": precision_score(A     ,				A       ),
				    "recall": recall_score(A     ,				A       ),
				    "f1": fa_score(A     ,				A       ),
				}

		# Data collator
		_UpperCAmelCase							 =		DataCollatorWithPadding(A     ,				pad_to_multiple_of=8       ) if training_args.fpaa else None

		# Initialize our Trainer
		_UpperCAmelCase							 =		Trainer(
		    model=A     ,				args=A     ,				train_dataset=A     ,				eval_dataset=A     ,				compute_metrics=A     ,				data_collator=A     ,				)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_process_zero():
						tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
		_UpperCAmelCase							 =		{}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'       )

				_UpperCAmelCase							 =		trainer.evaluate()

				_UpperCAmelCase							 =		os.path.join(training_args.output_dir     ,				'eval_results.txt'       )
				if trainer.is_world_process_zero():
						with open(A     ,				'w'       ) as writer:
								logger.info('***** Eval results *****'       )
								for key, value in result.items():
										logger.info('  %s = %s'     ,				A     ,				A       )
										writer.write('%s = %s\n' % (key, value)       )

						results.update(A       )

    # Predict
		if training_args.do_predict:
				_UpperCAmelCase							 =		TokenClassificationDataset(
				    token_classification_task=A     ,				data_dir=data_args.data_dir     ,				tokenizer=A     ,				labels=A     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.test     ,				)

				_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		trainer.predict(A       )
				_UpperCAmelCase ,					_UpperCAmelCase							 =		align_predictions(A     ,				A       )

				_UpperCAmelCase							 =		os.path.join(training_args.output_dir     ,				'test_results.txt'       )
				if trainer.is_world_process_zero():
						with open(A     ,				'w'       ) as writer:
								for key, value in metrics.items():
										logger.info('  %s = %s'     ,				A     ,				A       )
										writer.write('%s = %s\n' % (key, value)       )

        # Save predictions
				_UpperCAmelCase							 =		os.path.join(training_args.output_dir     ,				'test_predictions.txt'       )
				if trainer.is_world_process_zero():
						with open(A     ,				'w'       ) as writer:
								with open(os.path.join(data_args.data_dir     ,				'test.txt'       )     ,				'r'       ) as f:
										token_classification_task.write_predictions_to_file(A     ,				A     ,				A       )

		return results




def 	UpperCAmelCase     (		A	:						str       ):

		'''simple docstring'''


		main()


if __name__ == "__main__":
					main()







"""simple docstring"""





import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.config_class(**self.inputs_dict						)
									self.parent.assertTrue(hasattr(snake_case	,       'embed_dim'						)						)
									self.parent.assertTrue(hasattr(snake_case	,       'num_heads'						)						)







class 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=13	,       snake_case=64	,       snake_case=3	,       snake_case=[16, 48, 96]	,       snake_case=[1, 3, 6]	,       snake_case=[1, 2, 10]	,       snake_case=[7, 3, 3]	,       snake_case=[4, 2, 2]	,       snake_case=[2, 1, 1]	,       snake_case=[2, 2, 2]	,       snake_case=[False, False, True]	,       snake_case=[0.0, 0.0, 0.0]	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=True	,       snake_case=True	,       snake_case=2	,       )			->							Tuple:
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		batch_size
									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_sizes
									_UpperCAmelCase							 =		patch_stride
									_UpperCAmelCase							 =		patch_padding
									_UpperCAmelCase							 =		is_training
									_UpperCAmelCase							 =		use_labels
									_UpperCAmelCase							 =		num_labels
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		stride_kv
									_UpperCAmelCase							 =		depth
									_UpperCAmelCase							 =		cls_token
									_UpperCAmelCase							 =		attention_drop_rate
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

									_UpperCAmelCase							 =		self.get_config()
									return config, pixel_values, labels






							def        lowerCamelCase_   (  self						)			->							List[str]:
									return CvtConfig(
									    image_size=self.image_size	,       num_labels=self.num_labels	,       num_channels=self.num_channels	,       embed_dim=self.embed_dim	,       num_heads=self.num_heads	,       patch_sizes=self.patch_sizes	,       patch_padding=self.patch_padding	,       patch_stride=self.patch_stride	,       stride_kv=self.stride_kv	,       depth=self.depth	,       cls_token=self.cls_token	,       attention_drop_rate=self.attention_drop_rate	,       initializer_range=self.initializer_range	,       )






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		CvtModel(config=snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case						)
									_UpperCAmelCase							 =		(self.image_size, self.image_size)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		image_size[0], image_size[1]
									for i in range(len(self.depth						)						):
											_UpperCAmelCase							 =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
											_UpperCAmelCase							 =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
									self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.embed_dim[-1], height, width)						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.num_labels
									_UpperCAmelCase							 =		CvtForImageClassification(snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case	,       labels=snake_case						)
									self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)						)






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







@require_torch
class 			lowercase__			(      A,					A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      				=					(
							    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		CvtModelTester(self						)
									_UpperCAmelCase							 =		ConfigTester(self	,       config_class=snake_case	,       has_text_modality=snake_case	,       hidden_size=37						)






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






							@unittest.skip(reason='Cvt does not output attentions'						)
							def        lowerCamelCase_   (  self						)			->							str:
									pass






							@unittest.skip(reason='Cvt does not use inputs_embeds'						)
							def        lowerCamelCase_   (  self						)			->							int:
									pass






							@unittest.skip(reason='Cvt does not support input and output embeddings'						)
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									pass






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

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

											_UpperCAmelCase							 =		['pixel_values']
											self.assertListEqual(arg_names[:1]	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									def check_hidden_states_output(snake_case	,       snake_case	,       snake_case						):
											_UpperCAmelCase							 =		model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

											with torch.no_grad():
													_UpperCAmelCase							 =		model(**self._prepare_for_class(snake_case	,       snake_case						)						)

											_UpperCAmelCase							 =		outputs.hidden_states

											_UpperCAmelCase							 =		len(self.model_tester.depth						)
											self.assertEqual(len(snake_case						)	,       snake_case						)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]						)	,       [
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]	,       )

									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_UpperCAmelCase							 =		True
											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)

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

											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case						)






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






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase							 =		CvtModel.from_pretrained(snake_case						)
											self.assertIsNotNone(snake_case						)







def 	UpperCAmelCase     (		):

		'''simple docstring'''


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






@require_torch
@require_vision
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							@cached_property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case						)

									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(images=snake_case	,       return_tensors='pt'						).to(snake_case						)

									# forward pass
									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)

									# verify the logits
									_UpperCAmelCase							 =		torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape	,       snake_case						)

									_UpperCAmelCase							 =		torch.tensor([0.9285, 0.9015, -0.3150]						).to(snake_case						)

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


