





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''platform'''

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case=None	,      snake_case=None	,      snake_case=None	,      snake_case=None	,      snake_case=None	,      snake_case=None	,      )		->						Optional[Any]:
		if attention_mask is None:
				__lowercase					=    np.where(input_ids != config.pad_token_id	,      1	,      0       )
		if decoder_attention_mask is None:
				__lowercase					=    np.where(decoder_input_ids != config.pad_token_id	,      1	,      0       )
		if head_mask is None:
				__lowercase					=    np.ones((config.encoder_layers, config.encoder_attention_heads)       )
		if decoder_head_mask is None:
				__lowercase					=    np.ones((config.decoder_layers, config.decoder_attention_heads)       )
		if cross_attn_head_mask is None:
				__lowercase					=    np.ones((config.decoder_layers, config.decoder_attention_heads)       )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    int				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    List[Any]=13				,			__lowerCamelCase       :    List[str]=7				,			__lowerCamelCase       :    int=True				,			__lowerCamelCase       :    List[Any]=False				,			__lowerCamelCase       :    Union[str, Any]=99				,			__lowerCamelCase       :    Dict=16				,			__lowerCamelCase       :    int=2				,			__lowerCamelCase       :    Tuple=4				,			__lowerCamelCase       :    str=4				,			__lowerCamelCase       :    int="gelu"				,			__lowerCamelCase       :    Optional[Any]=0.1				,			__lowerCamelCase       :    List[str]=0.1				,			__lowerCamelCase       :    List[Any]=32				,			__lowerCamelCase       :    Union[str, Any]=2				,			__lowerCamelCase       :    Optional[int]=1				,			__lowerCamelCase       :    str=0				,			__lowerCamelCase       :    int=0.02				,			)					->   Dict:

			'''simple docstring'''




			__lowercase					=    parent
			__lowercase					=    batch_size
			__lowercase					=    seq_length
			__lowercase					=    is_training
			__lowercase					=    use_labels
			__lowercase					=    vocab_size
			__lowercase					=    hidden_size
			__lowercase					=    num_hidden_layers
			__lowercase					=    num_attention_heads
			__lowercase					=    intermediate_size
			__lowercase					=    hidden_act
			__lowercase					=    hidden_dropout_prob
			__lowercase					=    attention_probs_dropout_prob
			__lowercase					=    max_position_embeddings
			__lowercase					=    eos_token_id
			__lowercase					=    pad_token_id
			__lowercase					=    bos_token_id
			__lowercase					=    initializer_range




	def       UpperCAmelCase (	self       :    int      )					->   Any:

			'''simple docstring'''




			__lowercase					=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,			self.vocab_size      )				,			3				,			self.vocab_size      )
			__lowercase					=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,			dtype=np.intaa      ))				,			-1      )

			__lowercase					=    shift_tokens_right(__lowerCamelCase				,			1				,			2      )

			__lowercase					=    MarianConfig(
			    vocab_size=self.vocab_size				,			d_model=self.hidden_size				,			encoder_layers=self.num_hidden_layers				,			decoder_layers=self.num_hidden_layers				,			encoder_attention_heads=self.num_attention_heads				,			decoder_attention_heads=self.num_attention_heads				,			encoder_ffn_dim=self.intermediate_size				,			decoder_ffn_dim=self.intermediate_size				,			dropout=self.hidden_dropout_prob				,			attention_dropout=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			eos_token_id=self.eos_token_id				,			bos_token_id=self.bos_token_id				,			pad_token_id=self.pad_token_id				,			initializer_range=self.initializer_range				,			use_cache=__lowerCamelCase				,			)
			__lowercase					=    prepare_marian_inputs_dict(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      )
			return config, inputs_dict




	def       UpperCAmelCase (	self       :    int      )					->   Dict:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_config_and_inputs()
			return config, inputs_dict




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Any      )					->   Any:

			'''simple docstring'''




			__lowercase					=    20
			__lowercase					=    model_class_name(__lowerCamelCase      )

			__lowercase					=    model.encode(inputs_dict['input_ids']      )

			__lowercase	,			__lowercase					=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			__lowercase					=    model.init_cache(decoder_input_ids.shape[0]				,			__lowerCamelCase				,			__lowerCamelCase      )
			__lowercase					=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,			dtype='i4'      )

			__lowercase					=    jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]				,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,			)
			__lowercase					=    model.decode(
			    decoder_input_ids[:, :-1]				,			__lowerCamelCase				,			decoder_attention_mask=__lowerCamelCase				,			past_key_values=__lowerCamelCase				,			decoder_position_ids=__lowerCamelCase				,			)

			__lowercase					=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,			dtype='i4'      )
			__lowercase					=    model.decode(
			    decoder_input_ids[:, -1:]				,			__lowerCamelCase				,			decoder_attention_mask=__lowerCamelCase				,			past_key_values=outputs_cache.past_key_values				,			decoder_position_ids=__lowerCamelCase				,			)

			__lowercase					=    model.decode(__lowerCamelCase				,			__lowerCamelCase      )

			__lowercase					=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
			self.parent.assertTrue(diff < 1E-3				,			msg=F"Max diff is {diff}"      )






	def       UpperCAmelCase (	self       :    Any				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    List[str]      )					->   Any:

			'''simple docstring'''




			__lowercase					=    20
			__lowercase					=    model_class_name(__lowerCamelCase      )

			__lowercase					=    model.encode(inputs_dict['input_ids']      )

			__lowercase	,			__lowercase					=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			__lowercase					=    jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
			    ]				,			axis=-1				,			)

			__lowercase					=    model.init_cache(decoder_input_ids.shape[0]				,			__lowerCamelCase				,			__lowerCamelCase      )
			__lowercase					=    jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]				,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,			)

			__lowercase					=    model.decode(
			    decoder_input_ids[:, :-1]				,			__lowerCamelCase				,			decoder_attention_mask=__lowerCamelCase				,			past_key_values=__lowerCamelCase				,			decoder_position_ids=__lowerCamelCase				,			)
			__lowercase					=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,			dtype='i4'      )
			__lowercase					=    model.decode(
			    decoder_input_ids[:, -1:]				,			__lowerCamelCase				,			past_key_values=outputs_cache.past_key_values				,			decoder_attention_mask=__lowerCamelCase				,			decoder_position_ids=__lowerCamelCase				,			)

			__lowercase					=    model.decode(__lowerCamelCase				,			__lowerCamelCase				,			decoder_attention_mask=__lowerCamelCase      )

			__lowercase					=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
			self.parent.assertTrue(diff < 1E-3				,			msg=F"Max diff is {diff}"      )





@require_flax
class 		snake_case_      (       UpperCAmelCase_    ,    unittest.TestCase    ,    UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		True
	__UpperCamelCase        =		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	__UpperCamelCase        =		(FlaxMarianMTModel,) if is_flax_available() else ()




	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    FlaxMarianModelTester(self      )




	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__      ):
							__lowercase					=    self._prepare_for_class(__lowerCamelCase				,			__lowerCamelCase      )
							__lowercase					=    model_class(__lowerCamelCase      )

							@jax.jit
							def encode_jitted(__lowerCamelCase       :    str				,			__lowerCamelCase       :    str=None				,			**__lowerCamelCase       :    Optional[Any]      ):
									return model.encode(input_ids=__lowerCamelCase				,			attention_mask=__lowerCamelCase      )

							with self.subTest('JIT Enabled'      ):
									__lowercase					=    encode_jitted(**__lowerCamelCase      ).to_tuple()

							with self.subTest('JIT Disabled'      ):
									with jax.disable_jit():
											__lowercase					=    encode_jitted(**__lowerCamelCase      ).to_tuple()

							self.assertEqual(len(__lowerCamelCase      )				,			len(__lowerCamelCase      )      )
							for jitted_output, output in zip(__lowerCamelCase				,			__lowerCamelCase      ):
									self.assertEqual(jitted_output.shape				,			output.shape      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   str:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__      ):
							__lowercase					=    model_class(__lowerCamelCase      )
							__lowercase					=    model.encode(inputs_dict['input_ids']				,			inputs_dict['attention_mask']      )

							__lowercase					=    {
							    'decoder_input_ids': inputs_dict['decoder_input_ids'],
							    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
							    'encoder_outputs': encoder_outputs,
							}

							@jax.jit
							def decode_jitted(__lowerCamelCase       :    Any				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    Dict      ):
									return model.decode(
									    decoder_input_ids=__lowerCamelCase				,			decoder_attention_mask=__lowerCamelCase				,			encoder_outputs=__lowerCamelCase				,			)

							with self.subTest('JIT Enabled'      ):
									__lowercase					=    decode_jitted(**__lowerCamelCase      ).to_tuple()

							with self.subTest('JIT Disabled'      ):
									with jax.disable_jit():
											__lowercase					=    decode_jitted(**__lowerCamelCase      ).to_tuple()

							self.assertEqual(len(__lowerCamelCase      )				,			len(__lowerCamelCase      )      )
							for jitted_output, output in zip(__lowerCamelCase				,			__lowerCamelCase      ):
									self.assertEqual(jitted_output.shape				,			output.shape      )






	@slow
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Any:

			'''simple docstring'''




			for model_class_name in self.all_model_classes:
					__lowercase					=    model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'      )
					# FlaxMarianForSequenceClassification expects eos token in input_ids
					__lowercase					=    np.ones((1, 1)      ) * model.config.eos_token_id
					__lowercase					=    model(__lowerCamelCase      )
					self.assertIsNotNone(__lowerCamelCase      )





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None




	@classmethod
	def       UpperCAmelCase (	cls       :    Dict      )					->   None:

			'''simple docstring'''




			__lowercase					=    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
			return cls




	@cached_property
	def       UpperCAmelCase (	self       :    List[str]      )					->   int:

			'''simple docstring'''




			return MarianTokenizer.from_pretrained(self.model_name      )




	@property
	def       UpperCAmelCase (	self       :    Tuple      )					->   int:

			'''simple docstring'''




			return self.tokenizer.eos_token_id




	@cached_property
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    FlaxMarianMTModel.from_pretrained(self.model_name      )
			self.assertEqual(model.config.decoder_start_token_id				,			model.config.pad_token_id      )
			return model




	def       UpperCAmelCase (	self       :    int				,			**__lowerCamelCase       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.translate_src_text(**__lowerCamelCase      )
			self.assertListEqual(self.expected_text				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    List[Any]				,			**__lowerCamelCase       :    Tuple      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    self.tokenizer(self.src_text				,			padding=__lowerCamelCase				,			return_tensors='np'				,			**__lowerCamelCase      )
			__lowercase					=    self.model.generate(
			    model_inputs.input_ids				,			attention_mask=model_inputs.attention_mask				,			num_beams=2				,			max_length=128				,			).sequences
			__lowercase					=    self.tokenizer.batch_decode(__lowerCamelCase				,			skip_special_tokens=__lowerCamelCase      )
			return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''en'''
	__UpperCamelCase        =		'''fr'''
	__UpperCamelCase        =		[
	    '''I am a small frog.''',
	    '''Now I can forget the 100 words of german that I know.''',
	]
	__UpperCamelCase        =		[
	    '''Je suis une petite grenouille.''',
	    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
	]




	@slow
	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''fr'''
	__UpperCamelCase        =		'''en'''
	__UpperCamelCase        =		[
	    '''Donnez moi le micro.''',
	    '''Tom et Mary étaient assis à une table.''',  # Accents
	]
	__UpperCamelCase        =		[
	    '''Give me the microphone.''',
	    '''Tom and Mary were sitting at a table.''',
	]




	@slow
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Tuple:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''mt'''
	__UpperCamelCase        =		'''en'''
	__UpperCamelCase        =		['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
	__UpperCamelCase        =		['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




	@slow
	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''en'''
	__UpperCamelCase        =		'''de'''
	__UpperCamelCase        =		[
	    '''I am a small frog.''',
	    '''Now I can forget the 100 words of german that I know.''',
	    '''Tom asked his teacher for advice.''',
	    '''That\'s how I would do it.''',
	    '''Tom really admired Mary\'s courage.''',
	    '''Turn around and close your eyes.''',
	]
	__UpperCamelCase        =		[
	    '''Ich bin ein kleiner Frosch.''',
	    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
	    '''Tom bat seinen Lehrer um Rat.''',
	    '''So würde ich das machen.''',
	    '''Tom bewunderte Marias Mut wirklich.''',
	    '''Drehen Sie sich um und schließen Sie die Augen.''',
	]




	@slow
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''en'''
	__UpperCamelCase        =		'''zh'''
	__UpperCamelCase        =		['''My name is Wolfgang and I live in Berlin''']
	__UpperCamelCase        =		['''我叫沃尔夫冈 我住在柏林''']




	@slow
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Union[str, Any]:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''ru'''
	__UpperCamelCase        =		'''fr'''
	__UpperCamelCase        =		['''Он показал мне рукопись своей новой пьесы.''']
	__UpperCamelCase        =		['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




	@slow
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   int:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		'''en'''
	__UpperCamelCase        =		'''ROMANCE'''
	__UpperCamelCase        =		[
	    '''>>fr<< Don\'t spend so much time watching TV.''',
	    '''>>pt<< Your message has been sent.''',
	    '''>>es<< He\'s two years older than me.''',
	]
	__UpperCamelCase        =		[
	    '''Ne passez pas autant de temps à regarder la télé.''',
	    '''A sua mensagem foi enviada.''',
	    '''Es dos años más viejo que yo.''',
	]




	@slow
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   str:

			'''simple docstring'''




			self._assert_generated_batch_equal_expected()











# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    {
		    'en': 'Machine learning is great, isn\'t it?',
		    'ru': 'Машинное обучение - это здорово, не так ли?',
		    'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		__lowercase					=    {
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		__lowercase					=    F"{src_lang}-{tgt_lang}"

		__lowercase					=    F"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
		model_card_dir.mkdir(parents=snake_case	,      exist_ok=snake_case       )
		__lowercase					=    os.path.join(snake_case	,      'README.md'       )
		print(F"Generating {path}"       )
		with open(snake_case	,      'w'	,      encoding='utf-8'       ) as f:
				f.write(snake_case       )


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE_      :			Tuple		  =      Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_      :			Any		  =      repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		SCREAMING_SNAKE_CASE_      :			Any		  =      model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)




