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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__lowercase							 =					[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
    import run_clm_flax
    import run_flax_glue
    import run_flax_ner
    import run_mlm_flax
    import run_qa
    import run_summarization_flax
    import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__lowercase							 =					logging.getLogger()



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		argparse.ArgumentParser()
    parser.add_argument('''-f'''	)
    __UpperCamelCase     :Dict			  =		parser.parse_args()
    return args.f



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE="eval"	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			f"""{split}_results.json"""	)
    if os.path.exists(SCREAMING_SNAKE_CASE	):
        with open(SCREAMING_SNAKE_CASE  ,			'''r'''	) as f:
            return json.load(SCREAMING_SNAKE_CASE	)
    raise ValueError(f"""can't find {path}"""	)


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


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :List[str]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[str]			  =		f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

        with patch.object(__lowercase , '''argv''' , __lowercase):
            run_flax_glue.main()
            __UpperCamelCase     :Optional[Any]			  =		get_results(__lowercase)
            self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75)







    @slow
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Optional[int]			  =		f"""
            run_clm_flax.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 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

        with patch.object(__lowercase , '''argv''' , __lowercase):
            run_clm_flax.main()
            __UpperCamelCase     :Tuple			  =		get_results(__lowercase)
            self.assertLess(result['''eval_perplexity'''] , 100)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :int			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[str]			  =		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
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

        with patch.object(__lowercase , '''argv''' , __lowercase):
            run_summarization_flax.main()
            __UpperCamelCase     :Tuple			  =		get_results(__lowercase , split='''test''')
            self.assertGreaterEqual(result['''test_rouge1'''] , 10)
            self.assertGreaterEqual(result['''test_rouge2'''] , 2)
            self.assertGreaterEqual(result['''test_rougeL'''] , 7)
            self.assertGreaterEqual(result['''test_rougeLsum'''] , 7)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :List[str]			  =		f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

        with patch.object(__lowercase , '''argv''' , __lowercase):
            run_mlm_flax.main()
            __UpperCamelCase     :Union[str, Any]			  =		get_results(__lowercase)
            self.assertLess(result['''eval_perplexity'''] , 42)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Union[str, Any]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Optional[int]			  =		f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

        with patch.object(__lowercase , '''argv''' , __lowercase):
            run_ta_mlm_flax.main()
            __UpperCamelCase     :Dict			  =		get_results(__lowercase)
            self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42)







    @slow
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __UpperCamelCase     :Any			  =		7 if get_gpu_count() > 1 else 2

        __UpperCamelCase     :List[str]			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Union[str, Any]			  =		f"""
            run_flax_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
            --logging_steps 2 --eval_steps 2
            --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_flax_ner.main()
            __UpperCamelCase     :Tuple			  =		get_results(__lowercase)
            self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75)
            self.assertGreaterEqual(result['''eval_f1'''] , 0.3)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Dict			  =		self.get_auto_remove_tmp_dir()
        __UpperCamelCase     :Dict			  =		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
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

        with patch.object(__lowercase , '''argv''' , __lowercase):
            run_qa.main()
            __UpperCamelCase     :Any			  =		get_results(__lowercase)
            self.assertGreaterEqual(result['''eval_f1'''] , 30)
            self.assertGreaterEqual(result['''eval_exact'''] , 30)






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      VOCAB_FILES_NAMES
    a__	:						Tuple						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						List[str]						=      ["""input_ids""", """attention_mask"""]
    a__	:						int						=      None







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="<unk>" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="<pad>" , __lowercase=False , __lowercase=False , **__lowercase , )       ->	List[str]:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , unk_token=__lowercase , bos_token=__lowercase , eos_token=__lowercase , pad_token=__lowercase , add_prefix_space=__lowercase , clean_up_tokenization_spaces=__lowercase , **__lowercase , )
        __UpperCamelCase     :int			  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
            __UpperCamelCase     :Any			  =		getattr(__lowercase , pre_tok_state.pop('''type'''))
            __UpperCamelCase     :str			  =		add_prefix_space
            __UpperCamelCase     :List[str]			  =		pre_tok_class(**__lowercase)

        __UpperCamelCase     :Tuple			  =		add_prefix_space







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :Tuple			  =		kwargs.get('''is_split_into_words''' , __lowercase)
        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

        return super()._batch_encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :List[str]			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

        return super()._encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        __UpperCamelCase     :Optional[Any]			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[int]:
        __UpperCamelCase     :str			  =		[]
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(__lowercase , add_special_tokens=__lowercase) + [self.eos_token_id])

        if len(__lowercase) > self.model_max_length:
            __UpperCamelCase     :Any			  =		input_ids[-self.model_max_length :]
        return input_ids





