


'''simple docstring'''

import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase					:			Optional[Any]				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Any				     =					{
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_UpperCAmelCase					:			List[str]				     =					{
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

_UpperCAmelCase					:			str				     =					{"""Helsinki-NLP/opus-mt-en-de""": 5_1_2}
_UpperCAmelCase					:			str				     =					{}


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						VOCAB_FILES_NAMES
				__UpperCamelCase     :		Optional[int]						=						PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase     :		str						=						PRETRAINED_INIT_CONFIGURATION
				__UpperCamelCase     :		List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase     :		int						=						['input_ids', 'attention_mask']
				__UpperCamelCase     :		List[str]						=						re.compile('>>.+<<'   )  # type: re.Pattern




				def __init__(self							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase="<unk>"							,      __lowercase="</s>"							,      __lowercase="<pad>"							,      __lowercase=5_12							,      __lowercase = None							,      __lowercase=False							,      **__lowercase							,      ):
										__lowerCAmelCase											=					{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
										    source_lang=__lowercase							,      target_lang=__lowercase							,      unk_token=__lowercase							,      eos_token=__lowercase							,      pad_token=__lowercase							,      model_max_length=__lowercase							,      sp_model_kwargs=self.sp_model_kwargs							,      target_vocab_file=__lowercase							,      separate_vocabs=__lowercase							,      **__lowercase							,      )
										assert Path(__lowercase		).exists(), F"""cannot find spm source {source_spm}"""

										__lowerCAmelCase											=					separate_vocabs
										__lowerCAmelCase											=					load_json(__lowercase		)
										if self.unk_token not in self.encoder:
																raise KeyError('''<unk> token must be in vocab'''		)
										assert self.pad_token in self.encoder

										if separate_vocabs:
																__lowerCAmelCase											=					load_json(__lowercase		)
																__lowerCAmelCase											=					{v: k for k, v in self.target_encoder.items()}
																__lowerCAmelCase											=					[]
										else:
																__lowerCAmelCase											=					{v: k for k, v in self.encoder.items()}
																__lowerCAmelCase											=					[k for k in self.encoder if k.startswith('''>>'''		) and k.endswith('''<<'''		)]

										__lowerCAmelCase											=					source_lang
										__lowerCAmelCase											=					target_lang
										__lowerCAmelCase											=					[source_spm, target_spm]

										# load SentencePiece model for pre-processing
										__lowerCAmelCase											=					load_spm(__lowercase							,      self.sp_model_kwargs		)
										__lowerCAmelCase											=					load_spm(__lowercase							,      self.sp_model_kwargs		)
										__lowerCAmelCase											=					self.spm_source
										__lowerCAmelCase											=					self.encoder

										# Multilingual target side: default to using first supported language code.

										self._setup_normalizer()




				def        _snake_case	(self		):
										try:
																from sacremoses import MosesPunctNormalizer

																__lowerCAmelCase											=					MosesPunctNormalizer(self.source_lang		).normalize
										except (ImportError, FileNotFoundError):
																warnings.warn('''Recommended: pip install sacremoses.'''		)
																__lowerCAmelCase											=					lambda __lowercase		: x




				def        _snake_case	(self							,      __lowercase		):
										return self.punc_normalizer(__lowercase		) if x else ""




				def        _snake_case	(self							,      __lowercase		):
										return self.current_encoder.get(__lowercase							,      self.current_encoder[self.unk_token]		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					self.language_code_re.match(__lowercase		)
										__lowerCAmelCase											=					[match.group(0		)] if match else []
										return code, self.language_code_re.sub(''''''							,      __lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase							,       __lowerCAmelCase											=					self.remove_language_code(__lowercase		)
										__lowerCAmelCase											=					self.current_spm.encode(__lowercase							,      out_type=__lowercase		)
										return code + pieces




				def        _snake_case	(self							,      __lowercase		):
										return self.decoder.get(__lowercase							,      self.unk_token		)




				def        _snake_case	(self							,      __lowercase							,      **__lowercase		):
										return super().batch_decode(__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      **__lowercase		):
										return super().decode(__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					self.spm_source if self._decode_use_source_tokenizer else self.spm_target
										__lowerCAmelCase											=					[]
										__lowerCAmelCase											=					''''''
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += sp_model.decode_pieces(__lowercase		) + token + " "
																						__lowerCAmelCase											=					[]
																else:
																						current_sub_tokens.append(__lowercase		)
										out_string += sp_model.decode_pieces(__lowercase		)
										return out_string.strip()




				def        _snake_case	(self							,      __lowercase							,      __lowercase=None		):
										if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.spm_source
										__lowerCAmelCase											=					self.encoder




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.spm_target
										if self.separate_vocabs:
																__lowerCAmelCase											=					self.target_encoder




				@property
				def        _snake_case	(self		):
										return len(self.encoder		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase = None		):
										if not os.path.isdir(__lowercase		):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																return
										__lowerCAmelCase											=					[]

										if self.separate_vocabs:
																__lowerCAmelCase											=					os.path.join(
																    __lowercase							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']							,      )
																__lowerCAmelCase											=					os.path.join(
																    __lowercase							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']							,      )
																save_json(self.encoder							,      __lowercase		)
																save_json(self.target_encoder							,      __lowercase		)
																saved_files.append(__lowercase		)
																saved_files.append(__lowercase		)
										else:
																__lowerCAmelCase											=					os.path.join(
																    __lowercase							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']		)
																save_json(self.encoder							,      __lowercase		)
																saved_files.append(__lowercase		)

										for spm_save_filename, spm_orig_path, spm_model in zip(
										    [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]							,      self.spm_files							,      [self.spm_source, self.spm_target]							,      ):
																__lowerCAmelCase											=					os.path.join(
																    __lowercase							,      (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename		)
																if os.path.abspath(__lowercase		) != os.path.abspath(__lowercase		) and os.path.isfile(__lowercase		):
																						copyfile(__lowercase							,      __lowercase		)
																						saved_files.append(__lowercase		)
																elif not os.path.isfile(__lowercase		):
																						with open(__lowercase							,      '''wb'''		) as fi:
																												__lowerCAmelCase											=					spm_model.serialized_model_proto()
																												fi.write(__lowercase		)
																						saved_files.append(__lowercase		)

										return tuple(__lowercase		)




				def        _snake_case	(self		):
										return self.get_src_vocab()




				def        _snake_case	(self		):
										return dict(self.encoder							,      **self.added_tokens_encoder		)




				def        _snake_case	(self		):
										return dict(self.target_encoder							,      **self.added_tokens_decoder		)




				def __getstate__(self		):
										__lowerCAmelCase											=					self.__dict__.copy()
										state.update(
										    {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}		)
										return state




				def __setstate__(self							,      __lowercase		):
										__lowerCAmelCase											=					d

										# for backward compatibility
										if not hasattr(self							,      '''sp_model_kwargs'''		):
																__lowerCAmelCase											=					{}

										__lowerCAmelCase							,       __lowerCAmelCase											=					(load_spm(__lowercase							,      self.sp_model_kwargs		) for f in self.spm_files)
										__lowerCAmelCase											=					self.spm_source
										self._setup_normalizer()




				def        _snake_case	(self							,      *__lowercase							,      **__lowercase		):
										return 1




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					set(self.all_special_ids		)  # call it once instead of inside list comp
										all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special
										return [1 if x in all_special_ids else 0 for x in seq]




				def        _snake_case	(self							,      __lowercase							,      __lowercase = None							,      __lowercase = False		):
										if already_has_special_tokens:
																return self._special_token_mask(__lowercase		)
										elif token_ids_a is None:
																return self._special_token_mask(__lowercase		) + [1]
										else:
																return self._special_token_mask(token_ids_a + token_ids_a		) + [1]

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					sentencepiece.SentencePieceProcessor(**lowerCamelCase)
						spm.Load(lowerCamelCase)
						return spm

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						with open(lowerCamelCase,					'''w''') as f:
												json.dump(lowerCamelCase,					lowerCamelCase,					indent=2)




def 				__magic_name__(    lowerCamelCase):
						with open(lowerCamelCase,					'''r''') as f:
												return json.load(lowerCamelCase)





'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_UpperCAmelCase					:			str				     =					[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm
	import run_image_classification
	import run_mlm
	import run_ner
	import run_qa as run_squad
	import run_summarization
	import run_swag
	import run_text_classification
	import run_translation


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase					:			Optional[Any]				     =					logging.getLogger()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument('''-f''')
						__lowerCAmelCase											=					parser.parse_args()
						return args.f

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					{}
						__lowerCAmelCase											=					os.path.join(lowerCamelCase,					'''all_results.json''')
						if os.path.exists(lowerCamelCase):
												with open(lowerCamelCase,					'''r''') as f:
																		__lowerCAmelCase											=					json.load(lowerCamelCase)
						else:
												raise ValueError(F"""can't find {path}""")
						return results

def 				__magic_name__(    ):
						return bool(tf.config.list_physical_devices('''GPU'''))


_UpperCAmelCase					:			str				     =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class    a__					(			__A   ):







				"""simple docstring"""



				@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''		)
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

										if is_cuda_available():
																testargs.append('''--fp16'''		)

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_text_classification.main()
																# Reset the mixed precision policy so we don't break other tests
																tf.keras.mixed_precision.set_global_policy('''float32'''		)
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''eval_accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

										if len(tf.config.list_physical_devices('''GPU'''		)		) > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_clm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      1_00		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_mlm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      42		)




				def        _snake_case	(self		):
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										__lowerCAmelCase											=					7 if get_gpu_count() > 1 else 2

										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_ner.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_squad.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''f1''']							,      30		)
																self.assertGreaterEqual(result['''exact''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_swag.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''val_accuracy''']							,      0.8		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_summarization.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''rouge1''']							,      10		)
																self.assertGreaterEqual(result['''rouge2''']							,      2		)
																self.assertGreaterEqual(result['''rougeL''']							,      7		)
																self.assertGreaterEqual(result['''rougeLsum''']							,      7		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_translation.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''bleu''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_image_classification.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7		)

