






from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
					from .data.data_collator import DataCollator
					from .modeling_utils import PreTrainedModel
					from .tokenization_utils_base import PreTrainedTokenizerBase
					from .trainer_callback import TrainerCallback
					from .trainer_utils import EvalPrediction, PredictionOutput
					from .training_args import TrainingArguments


snake_case_   :	Optional[int]							      =logging.get_logger(__name__)





class        a__      (	lowerCAmelCase__		):






		def __init__(      self							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = (None, None)							, lowercase__ = None							, )				->				List[str]:
				super().__init__(
				    model=lowercase__							, args=lowercase__							, data_collator=lowercase__							, train_dataset=lowercase__							, eval_dataset=lowercase__							, tokenizer=lowercase__							, model_init=lowercase__							, compute_metrics=lowercase__							, callbacks=lowercase__							, optimizers=lowercase__							, preprocess_logits_for_metrics=lowercase__							, )

				# Override self.model.generation_config if a GenerationConfig is specified in args.
				# Priority: args.generation_config > model.generation_config > default GenerationConfig.
				if self.args.generation_config is not None:
						__A    =							self.load_generation_config(self.args.generation_config						)
						__A    =							gen_config






		@staticmethod
		def 					_lowerCamelCase					(      lowercase__						)				->				GenerationConfig:

				# GenerationConfig provided, nothing to do
				if isinstance(lowercase__							, lowercase__						):
						return deepcopy(lowercase__						)

				# str or Path
				__A    =							Path(lowercase__						) if isinstance(lowercase__							, lowercase__						) else gen_config_arg
				__A    =							None

				# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
				# This step is required in order to determine config_file_name
				if pretrained_model_name.is_file():
						__A    =							pretrained_model_name.name
						__A    =							pretrained_model_name.parent
				# dir path
				elif pretrained_model_name.is_dir():
						pass
				# model id or URL
				else:
						__A    =							gen_config_arg

				__A    =							GenerationConfig.from_pretrained(lowercase__							, lowercase__						)
				return gen_config






		def 					_lowerCamelCase					(      self							, lowercase__ = None							, lowercase__ = None							, lowercase__ = "eval"							, **lowercase__							, )				->				Dict[str, float]:
				__A    =							gen_kwargs.copy()
				if gen_kwargs.get("max_length"						) is None and gen_kwargs.get("max_new_tokens"						) is None:
						__A    =							self.args.generation_max_length
				__A    =							(
				    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.args.generation_num_beams
				)
				__A    =							gen_kwargs

				return super().evaluate(lowercase__							, ignore_keys=lowercase__							, metric_key_prefix=lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None							, lowercase__ = "test"							, **lowercase__							, )				->				"PredictionOutput":
				__A    =							gen_kwargs.copy()
				if gen_kwargs.get("max_length"						) is None and gen_kwargs.get("max_new_tokens"						) is None:
						__A    =							self.args.generation_max_length
				__A    =							(
				    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.args.generation_num_beams
				)
				__A    =							gen_kwargs

				return super().predict(lowercase__							, ignore_keys=lowercase__							, metric_key_prefix=lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__ = None							, **lowercase__							, )				->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

				if not self.args.predict_with_generate or prediction_loss_only:
						return super().prediction_step(
						    lowercase__							, lowercase__							, prediction_loss_only=lowercase__							, ignore_keys=lowercase__						)

				__A    =							"labels" in inputs
				__A    =							self._prepare_inputs(lowercase__						)

				# XXX: adapt synced_gpus for fairscale as well
				# Priority (handled in generate):
				# gen_kwargs > model.generation_config > default GenerationConfig()

				if len(lowercase__						) == 0 and hasattr(self							, "_gen_kwargs"						):
						__A    =							self._gen_kwargs.copy()

				if gen_kwargs.get("max_length"						) is None and gen_kwargs.get("max_new_tokens"						) is None:
						__A    =							self.model.config.max_length
				__A    =							(
				    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.model.config.num_beams
				)
				__A    =							True if is_deepspeed_zeroa_enabled() else False
				__A    =							(
				    gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus"						) is not None else default_synced_gpus
				)

				# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
				# (otherwise, it would continue generating from the padded `decoder_input_ids`)
				if (
				    "labels" in inputs
				    and "decoder_input_ids" in inputs
				    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
				):
						__A    =							{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
				__A    =							self.model.generate(**lowercase__							, **lowercase__						)

				# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
				# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
				# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
				if self.model.generation_config._from_model_config:
						__A    =							False

				# Retrieves GenerationConfig from model.generation_config
				__A    =							self.model.generation_config
				# in case the batch is shorter than max length, the output should be padded
				if generated_tokens.shape[-1] < gen_config.max_length:
						__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_length						)
				elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
						__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_new_tokens + 1						)

				with torch.no_grad():
						if has_labels:
								with self.compute_loss_context_manager():
										__A    =							model(**lowercase__						)
								if self.label_smoother is not None:
										__A    =							self.label_smoother(lowercase__							, inputs["labels"]						).mean().detach()
								else:
										__A    =							(outputs["loss"] if isinstance(lowercase__							, lowercase__						) else outputs[0]).mean().detach()
						else:
								__A    =							None

				if self.args.prediction_loss_only:
						return loss, None, None

				if has_labels:
						__A    =							inputs["labels"]
						if labels.shape[-1] < gen_config.max_length:
								__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_length						)
						elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
								__A    =							self._pad_tensors_to_max_len(lowercase__							, gen_config.max_new_tokens + 1						)
				else:
						__A    =							None

				return loss, generated_tokens, labels






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				Optional[int]:
				if self.tokenizer is not None and hasattr(self.tokenizer							, "pad_token_id"						):
						# If PAD token is not defined at least EOS token has to be defined
						__A    =							(
						    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
						)
				else:
						if self.model.config.pad_token_id is not None:
								__A    =							self.model.config.pad_token_id
						else:
								raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors"						)

				__A    =							pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)							, dtype=tensor.dtype							, device=tensor.device						)
				__A    =							tensor
				return padded_tensor









# 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)

