






"""simple docstring"""



import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_UpperCamelCase						:    Tuple		=						logging.getLogger(__name__)

@dataclass
class 			a	:
    UpperCAmelCase_					:	str      	=field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
    UpperCAmelCase_					:	Optional[str]      	=field(
        default=a_,							metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
    UpperCAmelCase_					:	Optional[str]      	=field(
        default="NER",							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}      )
    UpperCAmelCase_					:	Optional[str]      	=field(
        default=a_,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
    UpperCAmelCase_					:	bool      	=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_					:	Optional[str]      	=field(
        default=a_,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},							)


@dataclass
class 			a	:
    UpperCAmelCase_					:	str      	=field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}      )
    UpperCAmelCase_					:	Optional[str]      	=field(
        metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}      )
    UpperCAmelCase_					:	int      	=field(
        default=128,							metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },							)
    UpperCAmelCase_					:	bool      	=field(
        default=a_,							metadata={"help": "Overwrite the cached training and evaluation sets"}      )


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
  lowercase       ,	lowercase       ,	lowercase				       =			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.'       )

  lowercase				       =			import_module('tasks'       )

  try:
    lowercase				       =			getattr(__snake_case						,   model_args.task_type       )
    lowercase				       =			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						,   )
  logger.info(
      'n_replicas: %s, distributed training: %s, 16-bits training: %s'						,   training_args.n_replicas						,   bool(training_args.n_replicas > 1       )						,   training_args.fpaa						,   )
  logger.info('Training/evaluation parameters %s'						,   __snake_case       )

  # Prepare Token Classification task
  lowercase				       =			token_classification_task.get_labels(data_args.labels       )
  lowercase				       =			dict(enumerate(__snake_case       )       )
  lowercase				       =			len(__snake_case       )

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

  lowercase				       =			AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=__snake_case						,   idalabel=__snake_case						,   labelaid={label: i for i, label in enumerate(__snake_case       )}						,   cache_dir=model_args.cache_dir						,   )
  lowercase				       =			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						,   )

  with training_args.strategy.scope():
    lowercase				       =			TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path						,   from_pt=bool('.bin' in model_args.model_name_or_path       )						,   config=__snake_case						,   cache_dir=model_args.cache_dir						,   )

  # Get datasets
  lowercase				       =			(
      TFTokenClassificationDataset(
          token_classification_task=__snake_case						,   data_dir=data_args.data_dir						,   tokenizer=__snake_case						,   labels=__snake_case						,   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
  )
  lowercase				       =			(
      TFTokenClassificationDataset(
          token_classification_task=__snake_case						,   data_dir=data_args.data_dir						,   tokenizer=__snake_case						,   labels=__snake_case						,   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(__snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray       ) -> Tuple[List[int], List[int]]:
    lowercase				       =			np.argmax(__snake_case						,   axis=2       )
    lowercase       ,	lowercase				       =			preds.shape
    lowercase				       =			[[] for _ in range(__snake_case       )]
    lowercase				       =			[[] for _ in range(__snake_case       )]

    for i in range(__snake_case       ):
      for j in range(__snake_case       ):
        if label_ids[i, j] != -1_00:
          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(__snake_case     :    EvalPrediction       ) -> Dict:
    lowercase       ,	lowercase				       =			align_predictions(p.predictions						,   p.label_ids       )

    return {
        "precision": precision_score(__snake_case						,   __snake_case       ),
        "recall": recall_score(__snake_case						,   __snake_case       ),
        "f1": fa_score(__snake_case						,   __snake_case       ),
    }

  # Initialize our Trainer
  lowercase				       =			TFTrainer(
      model=__snake_case						,   args=__snake_case						,   train_dataset=train_dataset.get_dataset() if train_dataset else None						,   eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,   compute_metrics=__snake_case						,   )

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir       )

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

    lowercase				       =			trainer.evaluate()
    lowercase				       =			os.path.join(training_args.output_dir						,   'eval_results.txt'       )

    with open(__snake_case						,   'w'       ) as writer:
      logger.info('***** Eval results *****'       )

      for key, value in result.items():
        logger.info('  %s = %s'						,   __snake_case						,   __snake_case       )
        writer.write('%s = %s\n' % (key, value)       )

      results.update(__snake_case       )

    # Predict
  if training_args.do_predict:
    lowercase				       =			TFTokenClassificationDataset(
        token_classification_task=__snake_case						,   data_dir=data_args.data_dir						,   tokenizer=__snake_case						,   labels=__snake_case						,   model_type=config.model_type						,   max_seq_length=data_args.max_seq_length						,   overwrite_cache=data_args.overwrite_cache						,   mode=Split.test						,   )

    lowercase       ,	lowercase       ,	lowercase				       =			trainer.predict(test_dataset.get_dataset()       )
    lowercase       ,	lowercase				       =			align_predictions(__snake_case						,   __snake_case       )
    lowercase				       =			classification_report(__snake_case						,   __snake_case       )

    logger.info('\n%s'						,   __snake_case       )

    lowercase				       =			os.path.join(training_args.output_dir						,   'test_results.txt'       )

    with open(__snake_case						,   'w'       ) as writer:
      writer.write('%s\n' % report       )

    # Save predictions
    lowercase				       =			os.path.join(training_args.output_dir						,   'test_predictions.txt'       )

    with open(__snake_case						,   'w'       ) as writer:
      with open(os.path.join(data_args.data_dir						,   'test.txt'       )						,   'r'       ) as f:
        lowercase				       =			0

        for line in f:
          if line.startswith('-DOCSTART-'       ) or line == "" or line == "\n":
            writer.write(__snake_case       )

            if not preds_list[example_id]:
              example_id += 1
          elif preds_list[example_id]:
            lowercase				       =			line.split()[0] + ' ' + preds_list[example_id].pop(0       ) + '\n'

            writer.write(__snake_case       )
          else:
            logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'						,   line.split()[0]       )

  return results


if __name__ == "__main__":
    main()










"""simple docstring"""



import inspect
import unittest

import requests

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

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor

class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=False				,						_lowerCamelCase=1_3				,						_lowerCamelCase=(3_2, 1_2_8)				,						_lowerCamelCase=4				,						_lowerCamelCase=3				,						_lowerCamelCase=2_7				,						_lowerCamelCase=3_8				,						_lowerCamelCase=9_9				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=4.0				,						_lowerCamelCase=2_5_7				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			is_training
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			patch_size
      lowercase				       =			num_channels
      lowercase				       =			max_token_length
      lowercase				       =			num_character_labels
      lowercase				       =			num_bpe_labels
      lowercase				       =			num_wordpiece_labels
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			mlp_ratio
      lowercase				       =			patch_embeds_hidden_size
      lowercase				       =			output_hidden_states







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
      lowercase				       =			self.get_config()
      return config, pixel_values







    def      UpperCamelCase_ (     self			):
      return MgpstrConfig(
          image_size=self.image_size				,						patch_size=self.patch_size				,						num_channels=self.num_channels				,						max_token_length=self.max_token_length				,						num_character_labels=self.num_character_labels				,						num_bpe_labels=self.num_bpe_labels				,						num_wordpiece_labels=self.num_wordpiece_labels				,						hidden_size=self.hidden_size				,						num_hidden_layers=self.num_hidden_layers				,						num_attention_heads=self.num_attention_heads				,						mlp_ratio=self.mlp_ratio				,						output_hidden_states=self.output_hidden_states				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			MgpstrForSceneTextRecognition(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)
      self.parent.assertEqual(
          generated_ids[0][0].shape				,						(self.batch_size, self.max_token_length, self.num_character_labels)			)







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


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
    UpperCAmelCase_					:	Optional[Any]      	={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
    UpperCAmelCase_					:	Union[str, Any]      	=False

    UpperCAmelCase_					:	Optional[Any]      	=False
    UpperCAmelCase_					:	Dict      	=False
    UpperCAmelCase_					:	str      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			MgpstrModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						has_text_modality=_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      self.config_tester.run_common_tests()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase			)







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







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

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







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

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

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







    @unittest.skip(reason='MgpstrModel does not support feedforward chunking'			)
    def      UpperCamelCase_ (     self			):
      pass







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

      for model_class in self.all_model_classes:
        if not model_class.supports_gradient_checkpointing:
          continue

        lowercase				       =			True
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertTrue(model.is_gradient_checkpointing			)







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			getattr(
            self.model_tester				,						'expected_num_hidden_layers'				,						self.model_tester.num_hidden_layers + 1			)
        self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]			)				,						[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]				,						)

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

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

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







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

      lowercase				       =			_config_zero_init(_lowerCamelCase			)
      for model_class in self.all_model_classes:
        lowercase				       =			model_class(config=_lowerCamelCase			)
        for name, param in model.named_parameters():
          if isinstance(_lowerCamelCase				,						(nn.Linear, nn.Convad, nn.LayerNorm)			):
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()				,						[0.0, 1.0]				,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,						)







    @unittest.skip(reason='Retain_grad is tested in individual model tests'			)
    def      UpperCamelCase_ (     self			):
      pass


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       ).convert('RGB'       )
  return im



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







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			'alibaba-damo/mgp-str-base'
      lowercase				       =			MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase			).to(_lowerCamelCase			)
      lowercase				       =			MgpstrProcessor.from_pretrained(_lowerCamelCase			)

      lowercase				       =			prepare_img()
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='pt'			).pixel_values.to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)

      # verify the logits
      self.assertEqual(outputs.logits[0].shape				,						torch.Size((1, 2_7, 3_8)			)			)

      lowercase				       =			processor.batch_decode(outputs.logits			)
      lowercase				       =			'ticket'

      self.assertEqual(out_strs['generated_text'][0]				,						_lowerCamelCase			)

      lowercase				       =			torch.tensor(
          [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]				,						device=_lowerCamelCase				,						)

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


