



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


__SCREAMING_SNAKE_CASE     =	[
    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)

__SCREAMING_SNAKE_CASE     =	logging.getLogger()




def 						UpperCAmelCase (    ):
			A				:       List[str]														=    argparse.ArgumentParser()
			parser.add_argument("-f"     )
			A				:       Optional[Any]														=    parser.parse_args()
			return args.f




def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       List[Any]														=    {}
			A				:       Union[str, Any]														=    os.path.join(_lowerCamelCase			,     "all_results.json"     )
			if os.path.exists(_lowerCamelCase     ):
						with open(_lowerCamelCase			,     "r"     ) as f:
									A				:       int														=    json.load(_lowerCamelCase     )
			else:
						raise ValueError(f"""can't find {path}"""     )
			return results




def 						UpperCAmelCase (    ):
			return bool(tf.config.list_physical_devices("GPU"     )     )


__SCREAMING_SNAKE_CASE     =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@skip("Skipping until shape inference for to_tf_dataset PR is merged."			)
						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Optional[int]:
									A				:       str														=    self.get_auto_remove_tmp_dir()
									A				:       int														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_text_classification.main()
												# Reset the mixed precision policy so we don't break other tests
												tf.keras.mixed_precision.set_global_policy("float32"			)
												A				:       Dict														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["eval_accuracy"]						,						0.75			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Any:
									A				:       Tuple														=    self.get_auto_remove_tmp_dir()
									A				:       Optional[Any]														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_clm.main()
												A				:       Dict														=    get_results(__lowerCamelCase			)
												self.assertLess(result["eval_perplexity"]						,						1_00			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					List[str]:
									A				:       Optional[int]														=    self.get_auto_remove_tmp_dir()
									A				:       Tuple														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_mlm.main()
												A				:       List[str]														=    get_results(__lowerCamelCase			)
												self.assertLess(result["eval_perplexity"]						,						42			)







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

									A				:       int														=    self.get_auto_remove_tmp_dir()
									A				:       str														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_ner.main()
												A				:       Tuple														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["accuracy"]						,						0.75			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Dict:
									A				:       Optional[int]														=    self.get_auto_remove_tmp_dir()
									A				:       int														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_squad.main()
												A				:       str														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["f1"]						,						30			)
												self.assertGreaterEqual(result["exact"]						,						30			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Optional[Any]:
									A				:       str														=    self.get_auto_remove_tmp_dir()
									A				:       Optional[int]														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_swag.main()
												A				:       List[str]														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["val_accuracy"]						,						0.8			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Union[str, Any]:
									A				:       List[str]														=    self.get_auto_remove_tmp_dir()
									A				:       List[Any]														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_summarization.main()
												A				:       str														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["rouge1"]						,						10			)
												self.assertGreaterEqual(result["rouge2"]						,						2			)
												self.assertGreaterEqual(result["rougeL"]						,						7			)
												self.assertGreaterEqual(result["rougeLsum"]						,						7			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Union[str, Any]:
									A				:       Any														=    self.get_auto_remove_tmp_dir()
									A				:       Tuple														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_translation.main()
												A				:       str														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["bleu"]						,						30			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       str														=    self.get_auto_remove_tmp_dir()
									A				:       List[Any]														=    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(__lowerCamelCase						,						"argv"						,						__lowerCamelCase			):
												run_image_classification.main()
												A				:       Optional[Any]														=    get_results(__lowerCamelCase			)
												self.assertGreaterEqual(result["accuracy"]						,						0.7			)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE     =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

									super().__init__(
									    vocab_file=__lowerCamelCase						,						tokenizer_file=__lowerCamelCase						,						do_lower_case=__lowerCamelCase						,						remove_space=__lowerCamelCase						,						keep_accents=__lowerCamelCase						,						bos_token=__lowerCamelCase						,						eos_token=__lowerCamelCase						,						unk_token=__lowerCamelCase						,						sep_token=__lowerCamelCase						,						pad_token=__lowerCamelCase						,						cls_token=__lowerCamelCase						,						mask_token=__lowerCamelCase						,						additional_special_tokens=__lowerCamelCase						,						**__lowerCamelCase						,						)

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       int														=    False if not self.vocab_file else True

									try:
												import jieba
									except ModuleNotFoundError as error:
												raise error.__class__(
												    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
												    "See https://pypi.org/project/jieba/ for installation."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       Tuple														=    [self.cls_token_id]
									if token_ids_a is None:
												return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       Dict														=    [2]

									if token_ids_a is None:
												return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					Tuple[str]:
									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
												    "tokenizer."			)

									if not os.path.isdir(__lowerCamelCase			):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
												return
									A				:       List[Any]														=    os.path.join(
									    __lowerCamelCase						,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCamelCase			):
												copyfile(self.vocab_file						,						__lowerCamelCase			)

									return (out_vocab_file,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text