


'''simple docstring'''
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
					lowerCAmelCase   : Optional[Any]             =				"""platform"""
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class        _UpperCamelCase	:


				'''simple docstring'''


				_snake_case						       =				PegasusConfig
				_snake_case						       =				{}
				_snake_case						       =				'''gelu'''

				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=False							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_=0.1							,      a_=0.1							,      a_=2_0							,      a_=2							,      a_=1							,      a_=0							,      )    ->  Optional[int]:
					lowercase						:		List[Any]   					=		parent
					lowercase						:		Tuple   					=		batch_size
					lowercase						:		Tuple   					=		seq_length
					lowercase						:		Tuple   					=		is_training
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		str   					=		vocab_size
					lowercase						:		Tuple   					=		hidden_size
					lowercase						:		Optional[Any]   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Any   					=		intermediate_size

					lowercase						:		Optional[Any]   					=		hidden_dropout_prob
					lowercase						:		Optional[int]   					=		attention_probs_dropout_prob
					lowercase						:		str   					=		max_position_embeddings
					lowercase						:		int   					=		eos_token_id
					lowercase						:		List[Any]   					=		pad_token_id
					lowercase						:		Optional[Any]   					=		bos_token_id

				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[Any]   					=		ids_tensor([self.batch_size, self.seq_length - 1]							,      self.vocab_size							).clip(3							,      self.vocab_size							)
					lowercase						:		Tuple   					=		np.expand_dims(np.array([self.eos_token_id] * self.batch_size							)							,      1							)
					lowercase						:		Optional[int]   					=		np.concatenate([input_ids, eos_tensor]							,      axis=1							)

					lowercase						:		Optional[int]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		List[Any]   					=		self.config_cls(
					    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_ids=[2]							,      bos_token_id=self.bos_token_id							,      pad_token_id=self.pad_token_id							,      decoder_start_token_id=self.pad_token_id							,      **self.config_updates							,      )
					lowercase						:		Union[str, Any]   					=		prepare_pegasus_inputs_dict(a_							,      a_							,      a_							)
					return config, inputs_dict

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  int:
					lowercase						:		Any   					=		2_0
					lowercase						:		Union[str, Any]   					=		model_class_name(a_							)

					lowercase						:		Dict   					=		model.encode(inputs_dict["input_ids"]							)

					lowercase					,		lowercase						:		Tuple   					=		(
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

					lowercase						:		Tuple   					=		model.init_cache(decoder_input_ids.shape[0]							,      a_							,      a_							)
					lowercase						:		Tuple   					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,      dtype="i4"							)

					lowercase						:		List[str]   					=		jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]							,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,      )
					lowercase						:		List[Any]   					=		model.decode(
					    decoder_input_ids[:, :-1]							,      a_							,      decoder_attention_mask=a_							,      past_key_values=a_							,      decoder_position_ids=a_							,      )

					lowercase						:		Dict   					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,      dtype="i4"							)
					lowercase						:		Any   					=		model.decode(
					    decoder_input_ids[:, -1:]							,      a_							,      decoder_attention_mask=a_							,      past_key_values=outputs_cache.past_key_values							,      decoder_position_ids=a_							,      )

					lowercase						:		Any   					=		model.decode(a_							,      a_							)

					lowercase						:		Dict   					=		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 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Dict:
					lowercase						:		Dict   					=		2_0
					lowercase						:		Optional[int]   					=		model_class_name(a_							)

					lowercase						:		int   					=		model.encode(inputs_dict["input_ids"]							)

					lowercase					,		lowercase						:		List[Any]   					=		(
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

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

					lowercase						:		Tuple   					=		model.init_cache(decoder_input_ids.shape[0]							,      a_							,      a_							)
					lowercase						:		Tuple   					=		jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]							,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,      )

					lowercase						:		List[Any]   					=		model.decode(
					    decoder_input_ids[:, :-1]							,      a_							,      decoder_attention_mask=a_							,      past_key_values=a_							,      decoder_position_ids=a_							,      )
					lowercase						:		Optional[int]   					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,      dtype="i4"							)
					lowercase						:		Union[str, Any]   					=		model.decode(
					    decoder_input_ids[:, -1:]							,      a_							,      past_key_values=outputs_cache.past_key_values							,      decoder_attention_mask=a_							,      decoder_position_ids=a_							,      )

					lowercase						:		Any   					=		model.decode(a_							,      a_							,      decoder_attention_mask=a_							)

					lowercase						:		str   					=		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 	_A  (       A							,A							,A							,A=None							,A=None							,)  ->				List[Any]:
	if attention_mask is None:
		lowercase						:		int   					=		np.not_equal(A							,config.pad_token_id							).astype(np.inta							)
	if decoder_attention_mask is None:
		lowercase						:		Any   					=		np.concatenate(
		    [
		        np.ones(decoder_input_ids[:, :1].shape							,dtype=np.inta							),
		        np.not_equal(decoder_input_ids[:, 1:]							,config.pad_token_id							).astype(np.inta							),
		    ]							,axis=-1							,)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	}






@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				_snake_case						       =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				_snake_case						       =				True
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Any   					=		FlaxPegasusModelTester(self							)
					lowercase						:		List[Any]   					=		ConfigTester(self							,      config_class=a_							)

				def 	a__		(     self							)    ->  Optional[int]:
					self.config_tester.run_common_tests()

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase					,		lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
						self.model_tester.check_use_cache_forward(a_							,      a_							,      a_							)

				def 	a__		(     self							)    ->  Dict:
					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
						self.model_tester.check_use_cache_forward_with_attn_mask(a_							,      a_							,      a_							)

				def 	a__		(     self							)    ->  List[Any]:
					lowercase					,		lowercase						:		Dict   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__							):
							lowercase						:		Union[str, Any]   					=		self._prepare_for_class(a_							,      a_							)
							lowercase						:		Dict   					=		model_class(a_							)

							@jax.jit
							def encode_jitted(a_							,      a_=None							,      **a_							):
								return model.encode(input_ids=a_							,      attention_mask=a_							)

							with self.subTest("JIT Enabled"							):
								lowercase						:		Union[str, Any]   					=		encode_jitted(**a_							).to_tuple()

							with self.subTest("JIT Disabled"							):
								with jax.disable_jit():
									lowercase						:		List[str]   					=		encode_jitted(**a_							).to_tuple()

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

				def 	a__		(     self							)    ->  Tuple:
					lowercase					,		lowercase						:		int   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__							):
							lowercase						:		Optional[Any]   					=		model_class(a_							)
							lowercase						:		Any   					=		model.encode(inputs_dict["input_ids"]							,      inputs_dict["attention_mask"]							)

							lowercase						:		List[str]   					=		{
							    "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(a_							,      a_							,      a_							):
								return model.decode(
								    decoder_input_ids=a_							,      decoder_attention_mask=a_							,      encoder_outputs=a_							,      )

							with self.subTest("JIT Enabled"							):
								lowercase						:		List[Any]   					=		decode_jitted(**a_							).to_tuple()

							with self.subTest("JIT Disabled"							):
								with jax.disable_jit():
									lowercase						:		List[str]   					=		decode_jitted(**a_							).to_tuple()

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

				@slow
				def 	a__		(     self							)    ->  int:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("google/pegasus-large"							,      from_pt=a_							)
						lowercase						:		List[str]   					=		np.ones((1, 1)							)
						lowercase						:		Dict   					=		model(a_							)
						self.assertIsNotNone(a_							)


				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"							)
					lowercase						:		List[Any]   					=		PegasusTokenizer.from_pretrained("google/pegasus-xsum"							)

					lowercase						:		Dict   					=		[
					    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
					    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
					]

					lowercase						:		Union[str, Any]   					=		[
					    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
					    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
					]

					lowercase						:		List[Any]   					=		tokenizer(a_							,      return_tensors="np"							,      truncation=a_							,      max_length=5_1_2							,      padding=a_							)
					lowercase						:		Tuple   					=		model.generate(**a_							,      num_beams=2							).sequences
					lowercase						:		Dict   					=		tokenizer.batch_decode(a_							,      skip_special_tokens=a_							)
					assert tgt_text == decoded






'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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						:		Optional[int]   					=		{
	    "wmt16-en-de-dist-12-1": [28.3, 27.52],
	    "wmt16-en-de-dist-6-1": [27.4, 27.11],
	    "wmt16-en-de-12-1": [26.9, 25.75],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

'''
	model_card_dir.mkdir(parents=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


