
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


__A       =      -1 if torch_device == "cpu" else 0





@is_pipeline_test
class      A							(    unittest.TestCase			):
			lowerCamelCase							:							str    						=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			lowerCamelCase							:							List[Any]    						=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



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




									'''simple docstring'''



									lowercase__								=    SummarizationPipeline(model=__lowercase    ,       tokenizer=__lowercase    )
									return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    summarizer.model

									lowercase__								=    summarizer("""(CNN)The Palestinian Authority officially became"""    )
									self.assertEqual(__lowercase    ,       [{"""summary_text""": ANY(__lowercase    )}]    )

									lowercase__								=    summarizer(
									    """(CNN)The Palestinian Authority officially became """    ,       num_beams=2    ,       min_length=2    ,       max_length=5    ,       )
									self.assertEqual(__lowercase    ,       [{"""summary_text""": ANY(__lowercase    )}]    )

									# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
									lowercase__								=    [
									    """SwitchTransformersConfig""",
									    """T5Config""",
									    """LongT5Config""",
									    """LEDConfig""",
									    """PegasusXConfig""",
									    """FSMTConfig""",
									    """M2M100Config""",
									    """ProphetNetConfig""",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
									]
									if model.config.__class__.__name__ not in model_can_handle_longer_seq:
															# Too long and exception is expected.
															# For TF models, if the weights are initialized in GPU context, we won't get expected index error from
															# the embedding layer.
															if not (
															    isinstance(__lowercase    ,       __lowercase    )
															    and get_gpu_count() > 0
															    and len(summarizer.model.trainable_weights    ) > 0
															):
																					with self.assertRaises(__lowercase    ):
																											lowercase__								=    summarizer("""This """ * 1_000    )
									lowercase__								=    summarizer("""This """ * 1_000    ,       truncation=TruncationStrategy.ONLY_FIRST    )



			@require_torch
			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    pipeline(task="""summarization"""    ,       model="""sshleifer/tiny-mbart"""    ,       framework="""pt"""    )
									lowercase__								=    summarizer("""This is a small test"""    )
									self.assertEqual(
									    __lowercase    ,       [
									        {
									            """summary_text""": """เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"""
									        }
									    ]    ,       )



			@require_tf
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    pipeline(task="""summarization"""    ,       model="""sshleifer/tiny-mbart"""    ,       framework="""tf"""    )
									lowercase__								=    summarizer("""This is a small test"""    )
									self.assertEqual(
									    __lowercase    ,       [
									        {
									            """summary_text""": """เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"""
									        }
									    ]    ,       )



			@require_torch
			@slow
			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    pipeline(task="""summarization"""    ,       device=__lowercase    )
									lowercase__								=    (
									    """ (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"""
									    """ Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"""
									    """ formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."""
									    """ The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"""
									    """ jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"""
									    """ Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"""
									    """ situation in Palestinian territories, paving the way for possible war crimes investigations against"""
									    """ Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"""
									    """ the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"""
									    """ body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"""
									    """ move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"""
									    """ world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"""
									    """ ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"""
									    """ Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"""
									    """ Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"""
									    """ acquires all the rights as well as responsibilities that come with being a State Party to the Statute."""
									    """ These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"""
									    """ Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"""
									    """ immediately end their pressure, and countries that support universal acceptance of the court's treaty"""
									    """ should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"""
									    """ group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"""
									    """ decision to join a treaty to which over 100 countries around the world are members.\" In January, when"""
									    """ the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"""
									    """ outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""""
									    """ disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"""
									    """ state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"""
									    """ a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"""
									    """ will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""""
									    """ it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"""
									    """ territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"""
									    """ court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"""
									    """ Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"""
									    """ between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"""
									    """ will include alleged war crimes committed since June. The International Criminal Court was set up in"""
									    """ 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"""
									    """ and Faith Karimi contributed to this report."""
									)
									lowercase__								=    (
									    """ The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"""
									    """ the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"""
									    """ opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"""
									    """ says governments seeking to penalize Palestine should end pressure ."""
									)
									lowercase__								=    summarizer(__lowercase    )
									self.assertEqual(result[0]["""summary_text"""]    ,       __lowercase    )








'''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()


__A       =      logging.getLogger(__name__)





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





@dataclass
class      A							:
			lowerCamelCase							:							str    						=					field(
			    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
			lowerCamelCase							:							Optional[str]    						=					field(
			    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}			)
			lowerCamelCase							:							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."""
			        )
			    }						,  )
			lowerCamelCase							:							bool    						=					field(
			    default=__UpperCAmelCase						,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)





def 	_A						(							):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.
						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(lowercase__						,						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"""						,						lowercase__		)

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

						# 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=lowercase__						,						idalabel=lowercase__						,						labelaid={label: i for i, label in enumerate(lowercase__		)}						,						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=lowercase__						,						cache_dir=model_args.cache_dir						,						)

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

												for i in range(lowercase__		):
																		for j in range(lowercase__		):
																								if label_ids[i, j] != -100:
																														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(lowercase__		) -> Dict:
												lowercase__	,  lowercase__								=    align_predictions(p.predictions						,						p.label_ids		)

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

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

						# 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(lowercase__						,						"""w"""		) as writer:
																		logger.info("""***** Eval results *****"""		)

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

																		results.update(lowercase__		)

    # Predict
						if training_args.do_predict:
												lowercase__								=    TFTokenClassificationDataset(
												    token_classification_task=lowercase__						,						data_dir=data_args.data_dir						,						tokenizer=lowercase__						,						labels=lowercase__						,						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(lowercase__						,						lowercase__		)
												lowercase__								=    classification_report(lowercase__						,						lowercase__		)

												logger.info("""\n%s"""						,						lowercase__		)

												lowercase__								=    os.path.join(training_args.output_dir						,						"""test_results.txt"""		)

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

												# Save predictions
												lowercase__								=    os.path.join(training_args.output_dir						,						"""test_predictions.txt"""		)

												with open(lowercase__						,						"""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(lowercase__		)

																																				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(lowercase__		)
																														else:
																																				logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""						,						line.split()[0]		)

						return results


if __name__ == "__main__":
				main()






