
'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__ :			Tuple											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Union[str, Any]											=		"▁"

UpperCAmelCase__ :			List[str]											=		{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__ :			Union[str, Any]											=		{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase__ :			Optional[Any]											=		{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
UpperCAmelCase__ :			Tuple											=		["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  VOCAB_FILES_NAMES
	snake_case__   :str   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Any   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Tuple   =  ['input_ids', 'attention_mask']

	snake_case__   :List[int]   =  []
	snake_case__   :List[int]   =  []





	def __init__(       self      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Optional[int]="</s>"   ,				__magic_name__      :							List[Any]="</s>"   ,				__magic_name__      :							List[Any]="<s>"   ,				__magic_name__      :							Tuple="<unk>"   ,				__magic_name__      :							List[Any]="<pad>"   ,				__magic_name__      :							List[Any]="<mask>"   ,				__magic_name__      :							Optional[Dict[str, Any]] = None   ,				**__magic_name__      :							List[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				AddedToken(__magic_name__   ,				lstrip=__magic_name__   ,				rstrip=__magic_name__				) if isinstance(__magic_name__   ,				__magic_name__				) else mask_token

				lowerCAmelCase__										=				{} if sp_model_kwargs is None else sp_model_kwargs

				lowerCAmelCase__										=				kwargs.get("additional_special_tokens"   ,				[]				)
				kwargs["additional_special_tokens"] += [
				    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=__magic_name__   ,				tgt_lang=__magic_name__   ,				eos_token=__magic_name__   ,				unk_token=__magic_name__   ,				sep_token=__magic_name__   ,				cls_token=__magic_name__   ,				pad_token=__magic_name__   ,				mask_token=__magic_name__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__magic_name__   ,				)

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(str(__magic_name__				)				)
				lowerCAmelCase__										=				vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowerCAmelCase__										=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowerCAmelCase__										=				1

				lowerCAmelCase__										=				len(self.sp_model				)
				lowerCAmelCase__										=				{
				    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__magic_name__				)
				}
				lowerCAmelCase__										=				{v: k for k, v in self.lang_code_to_id.items()}
				lowerCAmelCase__										=				len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

				self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
				lowerCAmelCase__										=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

				lowerCAmelCase__										=				src_lang if src_lang is not None else "en_XX"
				lowerCAmelCase__										=				self.lang_code_to_id[self._src_lang]
				lowerCAmelCase__										=				tgt_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self._src_lang





	@src_lang.setter
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				new_src_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	def __getstate__(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.__dict__.copy()
				lowerCAmelCase__										=				None
				return state





	def __setstate__(       self      :							List[Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				d

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

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(self.vocab_file				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				{self.convert_ids_to_tokens(__magic_name__				): i for i in range(self.vocab_size				)}
				vocab.update(self.added_tokens_encoder				)
				return vocab





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							str				):


				"""simple docstring"""

				return self.sp_model.encode(__magic_name__   ,				out_type=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				lowerCAmelCase__										=				self.sp_model.PieceToId(__magic_name__				)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							int				):


				"""simple docstring"""

				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				[]
				lowerCAmelCase__										=				""
				lowerCAmelCase__										=				False
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										if not prev_is_special:
													out_string += " "
										out_string += self.sp_model.decode(__magic_name__				) + token
										lowerCAmelCase__										=				True
										lowerCAmelCase__										=				[]
							else:
										current_sub_tokens.append(__magic_name__				)
										lowerCAmelCase__										=				False
				out_string += self.sp_model.decode(__magic_name__				)
				return out_string.strip()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

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

				if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.vocab_file				):
							copyfile(self.vocab_file   ,				__magic_name__				)
				elif not os.path.isfile(self.vocab_file				):
							with open(__magic_name__   ,				"wb"				) as fi:
										lowerCAmelCase__										=				self.sp_model.serialized_model_proto()
										fi.write(__magic_name__				)

				return (out_vocab_file,)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None   ,				__magic_name__      :							bool = False				):


				"""simple docstring"""

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__magic_name__   ,				token_ids_a=__magic_name__   ,				already_has_special_tokens=__magic_name__				)

				lowerCAmelCase__										=				[1] * len(self.prefix_tokens				)
				lowerCAmelCase__										=				[1] * len(self.suffix_tokens				)
				if token_ids_a is None:
							return prefix_ones + ([0] * len(__magic_name__				)) + suffix_ones
				return prefix_ones + ([0] * len(__magic_name__				)) + ([0] * len(__magic_name__				)) + suffix_ones





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str]   ,				__magic_name__      :							Optional[str]   ,				**__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				self(__magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)
				lowerCAmelCase__										=				self.convert_tokens_to_ids(__magic_name__				)
				lowerCAmelCase__										=				tgt_lang_id
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							str = "en_XX"   ,				__magic_name__      :							Optional[List[str]] = None   ,				__magic_name__      :							str = "ro_RO"   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				tgt_lang
				return super().prepare_seqaseq_batch(__magic_name__   ,				__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return self.set_src_lang_special_tokens(self.src_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return self.set_tgt_lang_special_tokens(self.tgt_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.lang_code_to_id[src_lang]
				lowerCAmelCase__										=				[self.cur_lang_code_id]
				lowerCAmelCase__										=				[self.eos_token_id]







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.lang_code_to_id[tgt_lang]
				lowerCAmelCase__										=				[self.cur_lang_code_id]
				lowerCAmelCase__										=				[self.eos_token_id]






'''simple docstring'''


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


__lowerCAmelCase						:							int    							=						[
    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)

__lowerCAmelCase						:							List[Any]    							=						logging.getLogger()





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      argparse.ArgumentParser()
	parser.add_argument('''-f'''							)
	__UpperCAmelCase      					=      parser.parse_args()
	return args.f





def    lowerCAmelCase       (		UpperCamelCase__					: Union[str, Any]     ,       UpperCamelCase__					: Any="eval"							):


	"""simple docstring"""
	__UpperCAmelCase      					=      os.path.join(UpperCamelCase__     ,       f"""{split}_results.json"""							)
	if os.path.exists(UpperCamelCase__							):
		with open(UpperCamelCase__     ,       '''r'''							) as f:
			return json.load(UpperCamelCase__							)
	raise ValueError(f"""can't find {path}"""							)


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






class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				List[Any]						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_flax_glue.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)





							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_clm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		1_0_0						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_summarization_flax.main()
									__UpperCAmelCase      					=      get_results(__a  ,		split='''test'''						)
									self.assertGreaterEqual(result['''test_rouge1''']  ,		1_0						)
									self.assertGreaterEqual(result['''test_rouge2''']  ,		2						)
									self.assertGreaterEqual(result['''test_rougeL''']  ,		7						)
									self.assertGreaterEqual(result['''test_rougeLsum''']  ,		7						)





							@slow
							def    snake_case__    (   self					:				Any						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_mlm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						str:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_ta_mlm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.4_2						)





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

								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_flax_ner.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		0.3						)






							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_qa.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		3_0						)
									self.assertGreaterEqual(result['''eval_exact''']  ,		3_0						)
