






import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


snake_case_   :	Tuple							      =logging.get_logger(__name__)

if is_vision_available():
					import PIL.Image
					from PIL import Image
					from PIL.Image import Image as ImageType
else:
					snake_case_   :	Optional[Any]							      =object

if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf





class        a__      :






		def __init__(      self							, lowercase__						)				->				Optional[Any]:
				__A    =							value






		def __str__(      self						)				->				Tuple:
				return self.to_string()






		def 					_lowerCamelCase					(      self						)				->				Dict:
				logger.error(
				    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"						)
				return self._value






		def 					_lowerCamelCase					(      self						)				->				str:
				logger.error(
				    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"						)
				return str(self._value						)





class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__		):






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				return self._value






		def 					_lowerCamelCase					(      self						)				->				str:
				return self._value





class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__		):






		def __init__(      self							, lowercase__						)				->				List[str]:
				super().__init__(lowercase__						)

				if not is_vision_available():
						raise ImportError("PIL must be installed in order to handle images."						)

				__A    =							None
				__A    =							None
				__A    =							None

				if isinstance(lowercase__							, lowercase__						):
						__A    =							value
				elif isinstance(lowercase__							, (str, pathlib.Path)						):
						__A    =							value
				elif isinstance(lowercase__							, torch.Tensor						):
						__A    =							value
				else:
						raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(lowercase__						)}"""						)






		def 					_lowerCamelCase					(      self							, lowercase__=None							, lowercase__=None						)				->				List[str]:
				from IPython.display import Image, display

				display(Image(self.to_string()						)						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				if self._raw is not None:
						return self._raw

				if self._path is not None:
						__A    =							Image.open(self._path						)
						return self._raw






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				if self._path is not None:
						return self._path

				if self._raw is not None:
						__A    =							tempfile.mkdtemp()
						__A    =							os.path.join(lowercase__							, str(uuid.uuida()						) + ".png"						)
						self._raw.save(self._path						)

						return self._path

				if self._tensor is not None:
						__A    =							self._tensor.cpu().detach().numpy()

						# There is likely simpler than load into image into save
						__A    =							Image.fromarray((array * 255).astype(np.uinta						)						)

						__A    =							tempfile.mkdtemp()
						__A    =							os.path.join(lowercase__							, str(uuid.uuida()						) + ".png"						)

						img.save(self._path						)

						return self._path





class        a__      (	lowerCAmelCase__		):






		def __init__(      self							, lowercase__							, lowercase__=16000						)				->				Tuple:
				super().__init__(lowercase__						)

				if not is_soundfile_availble():
						raise ImportError("soundfile must be installed in order to handle audio."						)

				__A    =							None
				__A    =							None

				__A    =							samplerate

				if isinstance(lowercase__							, (str, pathlib.Path)						):
						__A    =							value
				elif isinstance(lowercase__							, torch.Tensor						):
						__A    =							value
				else:
						raise ValueError(F"""Unsupported audio type: {type(lowercase__						)}"""						)






		def 					_lowerCamelCase					(      self							, lowercase__=None							, lowercase__=None						)				->				List[str]:
				from IPython.display import Audio, display

				display(Audio(self.to_string()							, rate=self.samplerate						)						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				if self._tensor is not None:
						return self._tensor

				if self._path is not None:
						__A ,   __A    =							sf.read(self._path						)
						__A    =							torch.tensor(lowercase__						)
						return self._tensor






		def 					_lowerCamelCase					(      self						)				->				Dict:
				if self._path is not None:
						return self._path

				if self._tensor is not None:
						__A    =							tempfile.mkdtemp()
						__A    =							os.path.join(lowercase__							, str(uuid.uuida()						) + ".wav"						)
						sf.write(self._path							, self._tensor							, samplerate=self.samplerate						)
						return self._path



snake_case_   :	Optional[int]							      ={'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
snake_case_   :	Union[str, Any]							      ={str: AgentText}

if is_vision_available():
					snake_case_   :	str							      =AgentImage



def      UpperCAmelCase						( *lowerCAmelCase__					,     **lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							[(arg.to_raw() if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	) else arg) for arg in args]
		__A    =							{k: (v.to_raw() if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	) else v) for k, v in kwargs.items()}
		return args, kwargs





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=None	):


		'''simple docstring'''

		if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	):
				__A    =							{}
				for i, (k, v) in enumerate(outputs.items()	):
						if output_types is not None:
								# If the class has defined outputs, we can map directly according to the class definition
								if output_types[i] in AGENT_TYPE_MAPPING:
										__A    =							AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase__	)
								else:
										__A    =							AgentType(lowerCAmelCase__	)

						else:
								# If the class does not have defined output, then we map according to the type
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(lowerCAmelCase__					,     _k	):
												__A    =							_v(lowerCAmelCase__	)
								if k not in decoded_outputs:
										__A    =							AgentType[v]

		elif isinstance(lowerCAmelCase__					,     (list, tuple)	):
				__A    =							type(lowerCAmelCase__	)()
				for i, v in enumerate(lowerCAmelCase__	):
						if output_types is not None:
								# If the class has defined outputs, we can map directly according to the class definition
								if output_types[i] in AGENT_TYPE_MAPPING:
										decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase__	)	)
								else:
										decoded_outputs.append(AgentType(lowerCAmelCase__	)	)
						else:
								# If the class does not have defined output, then we map according to the type
								__A    =							False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(lowerCAmelCase__					,     _k	):
												decoded_outputs.append(_v(lowerCAmelCase__	)	)
												__A    =							True

								if not found:
										decoded_outputs.append(AgentType(lowerCAmelCase__	)	)

		else:
				if output_types[0] in AGENT_TYPE_MAPPING:
						# If the class has defined outputs, we can map directly according to the class definition
						__A    =							AGENT_TYPE_MAPPING[output_types[0]](lowerCAmelCase__	)

				else:
						# If the class does not have defined output, then we map according to the type
						for _k, _v in INSTANCE_TYPE_MAPPING.items():
								if isinstance(lowerCAmelCase__					,     _k	):
										return _v(lowerCAmelCase__	)
						return AgentType(lowerCAmelCase__	)

		return decoded_outputs









# 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)

