from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case			=						{
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case			=						['''CamembertTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case			=						['''CamembertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case			=						[
	    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CamembertForCausalLM''',
	    '''CamembertForMaskedLM''',
	    '''CamembertForMultipleChoice''',
	    '''CamembertForQuestionAnswering''',
	    '''CamembertForSequenceClassification''',
	    '''CamembertForTokenClassification''',
	    '''CamembertModel''',
	    '''CamembertPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case			=						[
	    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFCamembertForCausalLM''',
	    '''TFCamembertForMaskedLM''',
	    '''TFCamembertForMultipleChoice''',
	    '''TFCamembertForQuestionAnswering''',
	    '''TFCamembertForSequenceClassification''',
	    '''TFCamembertForTokenClassification''',
	    '''TFCamembertModel''',
	    '''TFCamembertPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert import CamembertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert_fast import CamembertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_camembert import (
		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CamembertForCausalLM,
		    CamembertForMaskedLM,
		    CamembertForMultipleChoice,
		    CamembertForQuestionAnswering,
		    CamembertForSequenceClassification,
		    CamembertForTokenClassification,
		    CamembertModel,
		    CamembertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_camembert import (
		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCamembertForCausalLM,
		    TFCamembertForMaskedLM,
		    TFCamembertForMultipleChoice,
		    TFCamembertForQuestionAnswering,
		    TFCamembertForSequenceClassification,
		    TFCamembertForTokenClassification,
		    TFCamembertModel,
		    TFCamembertPreTrainedModel,
		)

else:
	import sys

	_snake_case			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import io
import json
import subprocess


_snake_case			=						[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

_snake_case			=						8




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   List[Any]:
							__UpperCAmelCase						:			int         =   {}
							for src, tgt in pairs:
														__UpperCAmelCase						:			Optional[Any]         =   f'''{src}-{tgt}'''
														__UpperCAmelCase						:			Union[str, Any]         =   f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
														__UpperCAmelCase						:			Any         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			Tuple         =   f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
														__UpperCAmelCase						:			List[Any]         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			str         =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
							return text


_snake_case			=						get_all_data(pairs, n_objs)
_snake_case			=						'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
	_snake_case			=						json.dump(text, f, indent=2, ensure_ascii=False)




