



import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


UpperCAmelCase_							:      Any      =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
		UpperCAmelCase_							:      str      =  json.load(f)






@require_torch
class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self			,	UpperCAmelCase__		):
							return FSMTTokenizer.from_pretrained(UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       FSMTForConditionalGeneration.from_pretrained(UpperCAmelCase__		).to(UpperCAmelCase__		)
							if torch_device == "cuda":
													model.half()
							return model



	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							A__							=       F"""facebook/wmt19-{pair}"""
							A__							=       self.get_tokenizer(UpperCAmelCase__		)
							A__							=       self.get_model(UpperCAmelCase__		)

							A__							=       bleu_data[pair]["src"]
							A__							=       bleu_data[pair]["tgt"]

							A__							=       tokenizer(UpperCAmelCase__			,	return_tensors="pt"			,	truncation=UpperCAmelCase__			,	padding="longest"		).to(UpperCAmelCase__		)
							A__							=       model.generate(
							    input_ids=batch.input_ids			,	num_beams=8			,	)
							A__							=       tokenizer.batch_decode(
							    UpperCAmelCase__			,	skip_special_tokens=UpperCAmelCase__			,	clean_up_tokenization_spaces=UpperCAmelCase__		)
							A__							=       calculate_bleu(UpperCAmelCase__			,	UpperCAmelCase__		)
							print(UpperCAmelCase__		)
							self.assertGreaterEqual(scores["bleu"]			,	UpperCAmelCase__		)










import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							super().__init__(
							    UpperCAmelCase__			,	split=UpperCAmelCase__			,	features=UpperCAmelCase__			,	cache_dir=UpperCAmelCase__			,	keep_in_memory=UpperCAmelCase__			,	streaming=UpperCAmelCase__			,	num_proc=UpperCAmelCase__			,	**UpperCAmelCase__			,	)
							A__							=       field
							A__							=       path_or_paths if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else {self.split: path_or_paths}
							A__							=       Json(
							    cache_dir=UpperCAmelCase__			,	data_files=UpperCAmelCase__			,	features=UpperCAmelCase__			,	field=UpperCAmelCase__			,	**UpperCAmelCase__			,	)



	def 			__A							(       self		):
							# Build iterable dataset
							if self.streaming:
													A__							=       self.builder.as_streaming_dataset(split=self.split		)
							# Build regular (map-style) dataset
							else:
													A__							=       None
													A__							=       None
													A__							=       None
													A__							=       None

													self.builder.download_and_prepare(
													    download_config=UpperCAmelCase__			,	download_mode=UpperCAmelCase__			,	verification_mode=UpperCAmelCase__			,	base_path=UpperCAmelCase__			,	num_proc=self.num_proc			,	)
													A__							=       self.builder.as_dataset(
													    split=self.split			,	verification_mode=UpperCAmelCase__			,	in_memory=self.keep_in_memory		)
							return dataset






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							if num_proc is not None and num_proc <= 0:
													raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

							A__							=       dataset
							A__							=       path_or_buf
							A__							=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							A__							=       num_proc
							A__							=       "utf-8"
							A__							=       to_json_kwargs



	def 			__A							(       self		):
							A__							=       self.to_json_kwargs.pop("path_or_buf"			,	UpperCAmelCase__		)
							A__							=       self.to_json_kwargs.pop("orient"			,	"records"		)
							A__							=       self.to_json_kwargs.pop("lines"			,	True if orient == "records" else False		)
							A__							=       self.to_json_kwargs.pop("index"			,	False if orient in ["split", "table"] else True		)
							A__							=       self.to_json_kwargs.pop("compression"			,	UpperCAmelCase__		)

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
													raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""		)

							if isinstance(self.path_or_buf			,	(str, bytes, os.PathLike)		):
													with fsspec.open(self.path_or_buf			,	"wb"			,	compression=UpperCAmelCase__		) as buffer:
																			A__							=       self._write(file_obj=UpperCAmelCase__			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							else:
													if compression:
																			raise NotImplementedError(
																			    F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
																			    " was passed. Please provide a local path instead."		)
													A__							=       self._write(
													    file_obj=self.path_or_buf			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							return written



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__						,       A__						,       A__						,       A__						,       A__							=       args

							A__							=       query_table(
							    table=self.dataset.data			,	key=slice(UpperCAmelCase__			,	offset + self.batch_size		)			,	indices=self.dataset._indices			,	)
							A__							=       batch.to_pandas().to_json(
							    path_or_buf=UpperCAmelCase__			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**UpperCAmelCase__		)
							if not json_str.endswith("\n"		):
													json_str += "\n"
							return json_str.encode(self.encoding		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__			,	):
							A__							=       0

							if self.num_proc is None or self.num_proc == 1:
													for offset in logging.tqdm(
													    range(0			,	len(self.dataset		)			,	self.batch_size		)			,	unit="ba"			,	disable=not logging.is_progress_bar_enabled()			,	desc="Creating json from Arrow format"			,	):
																			A__							=       self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
																			written += file_obj.write(UpperCAmelCase__		)
							else:
													A__						,       A__							=       len(self.dataset		), self.batch_size
													with multiprocessing.Pool(self.num_proc		) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,	UpperCAmelCase__			,	UpperCAmelCase__		)]			,	)			,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,	unit="ba"			,	disable=not logging.is_progress_bar_enabled()			,	desc="Creating json from Arrow format"			,	):
																									written += file_obj.write(UpperCAmelCase__		)

							return written





