






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

import os
from pathlib import Path



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "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]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## 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   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

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 wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### 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=lowerCAmelCase__					,     exist_ok=lowerCAmelCase__	)
		__A    =							os.path.join(lowerCAmelCase__					,     "README.md"	)
		print(F"""Generating {path}"""	)
		with open(lowerCAmelCase__					,     "w"					,     encoding="utf-8"	) as f:
				f.write(lowerCAmelCase__	)


# make sure we are under the root of the project
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

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







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase_    =logging.get_logger(__name__)


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=21							,			UpperCAmelCase_=[]							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		model
											lowerCAmelCase =		trace_batch_nums
											lowerCAmelCase =		abort_after_batch_num

											# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
											lowerCAmelCase =		collections.deque([]							,			UpperCAmelCase_    )
											lowerCAmelCase =		[]
											lowerCAmelCase =		0
											lowerCAmelCase =		0
											lowerCAmelCase =		False
											lowerCAmelCase =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()



				def    __snake_case		(			self							,			UpperCAmelCase_=None    ):
											if frame is not None:
																		self.expand_frame(UpperCAmelCase_    )
											self.frames.append('''\n'''.join(self.frame    )    )
											lowerCAmelCase =		[]  # start a new frame



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											self.frame.append(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											print('''\n'''.join(self.frames    )    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""    )
											print(F"""Last {len(self.frames    )} forward frames:"""    )
											print(F"""{"abs min":8} {"abs max":8} metadata"""    )
											print('''\n'''.join(self.frames    )    )
											print('''\n\n'''    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											# extract the fully qualified module names, to be able to report at run time. e.g.:
											# encoder.block.2.layer.0.SelfAttention.o
											#
											# for shared weights only the first shared module name will be registered
											lowerCAmelCase =		{m: name for name, m in self.model.named_modules()}
											# self.longest_module_name = max(len(v) for v in self.module_names.values())



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if torch.is_tensor(UpperCAmelCase_    ):
																		self.expand_frame(get_abs_min_max(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		if detect_overflow(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									lowerCAmelCase =		True
											elif var is None:
																		self.expand_frame(F"""{"None":>17} {ctx}"""    )
											else:
																		self.expand_frame(F"""{"not a tensor":>17} {ctx}"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""    )
											self.expand_frame(F"""{"abs min":8} {"abs max":8} metadata"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

											# params
											for name, p in module.named_parameters(recurse=UpperCAmelCase_    ):
																		self.analyse_variable(UpperCAmelCase_							,			UpperCAmelCase_    )

											# inputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									self.analyse_variable(UpperCAmelCase_							,			F"""input[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''input'''    )

											# outputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									# possibly a tuple of tuples
																									if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																																for j, y in enumerate(UpperCAmelCase_    ):
																																							self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}][{j}]"""    )
																									else:
																																self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''output'''    )

											self.save_frame()



				def    __snake_case		(			self    ):
											self.model.apply(self._register_forward_hook    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											module.register_forward_hook(self.forward_hook    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowerCAmelCase =		False

											lowerCAmelCase =		True if self.batch_number in self.trace_batch_nums else False
											if trace_mode:
																		self.reset_saved_frames()

											if self.total_calls == 0:
																		self.batch_start_frame()
											self.total_calls += 1

											# count batch numbers - the very first forward hook of the batch will be called when the
											# batch completes - i.e. it gets called very last - we know this batch has finished
											if module == self.model:
																		self.batch_number += 1
																		lowerCAmelCase =		True

											self.create_frame(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

											# if last_frame_of_batch:
											#     self.batch_end_frame()

											if trace_mode:
																		self.trace_frames()

											if last_frame_of_batch:
																		self.batch_start_frame()

											if self.detected_overflow and not trace_mode:
																		self.dump_saved_frames()

																		# now we can abort, as it's pointless to continue running
																		raise ValueError(
																		    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																		    '''Please scroll up above this traceback to see the activation values prior to this event.'''    )

											# abort after certain batch if requested to do so
											if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																		raise ValueError(
																		    F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
																		    F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""    )




def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		var.abs()
							return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		False
							if torch.isnan(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has nans"""				)
							if torch.isinf(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has infs"""				)

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			100				)]
														if naaa.numel() > 0:
																					print(F"""{ctx}:  n100={naaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			1000				)]
														if naaaa.numel() > 0:
																					print(F"""{ctx}: n1000={naaaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			10000				)]
														if naaaaa.numel() > 0:
																					print(F"""{ctx}: n10000={naaaaa.numel()}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"""				)

							return detected


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""underflow_overflow"""
				__a				:			Dict					    ="""tpu_metrics_debug"""



