






'''simple docstring'''

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image


A:      int  				=				["text", "image", "audio"]






def 	_UpperCAmelCase    (  a		:	List[str]							)			->			str:





						"""simple docstring"""
						lowercase_      :   int        =			[]

						for input_type in input_types:
												if input_type == "text":
																		inputs.append('Text input'							)
												elif input_type == "image":
																		inputs.append(
																		    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'							)							) / '000000039769.png'							).resize((5_1_2, 5_1_2)							)							)
												elif input_type == "audio":
																		inputs.append(torch.ones(3_0_0_0							)							)
												elif isinstance(UpperCAmelCase__						,			UpperCAmelCase__							):
																		inputs.append(create_inputs(UpperCAmelCase__							)							)
												else:
																		raise ValueError(f"Invalid type requested: {input_type}"							)

						return inputs






def 	_UpperCAmelCase    (  a		:	List							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			[]

						for output in outputs:
												if isinstance(UpperCAmelCase__						,			(str, AgentText)							):
																		output_types.append('text'							)
												elif isinstance(UpperCAmelCase__						,			(Image.Image, AgentImage)							):
																		output_types.append('image'							)
												elif isinstance(UpperCAmelCase__						,			(torch.Tensor, AgentAudio)							):
																		output_types.append('audio'							)
												else:
																		raise ValueError(f"Invalid output: {output}"							)

						return output_types






@is_tool_test
class 							__magic_name__     :







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							self.assertTrue(hasattr(self.tool  ,      'inputs'       )       )
							self.assertTrue(hasattr(self.tool  ,      'outputs'       )       )

							lowercase_      :   Any        =			self.tool.inputs
							for _input in inputs:
													if isinstance(_input  ,      __UpperCamelCase       ):
																			for __input in _input:
																									self.assertTrue(__input in authorized_types       )
													else:
																			self.assertTrue(_input in authorized_types       )

							lowercase_      :   List[str]        =			self.tool.outputs
							for _output in outputs:
													self.assertTrue(_output in authorized_types       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   Any        =			create_inputs(self.tool.inputs       )
							lowercase_      :   Optional[int]        =			self.tool(*__UpperCamelCase       )

							# There is a single output
							if len(self.tool.outputs       ) == 1:
													lowercase_      :   str        =			[outputs]

							self.assertListEqual(output_types(__UpperCamelCase       )  ,      self.tool.outputs       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							self.assertTrue(hasattr(self.tool  ,      'description'       )       )
							self.assertTrue(hasattr(self.tool  ,      'default_checkpoint'       )       )
							self.assertTrue(self.tool.description.startswith('This is a tool that'       )       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   List[Any]        =			create_inputs(self.tool.inputs       )
							lowercase_      :   Tuple        =			self.tool(*__UpperCamelCase       )

							if not isinstance(__UpperCamelCase  ,      __UpperCamelCase       ):
													lowercase_      :   Dict        =			[outputs]

							self.assertEqual(len(__UpperCamelCase       )  ,      len(self.tool.outputs       )       )

							for output, output_type in zip(__UpperCamelCase  ,      self.tool.outputs       ):
													lowercase_      :   int        =			AGENT_TYPE_MAPPING[output_type]
													self.assertTrue(isinstance(__UpperCamelCase  ,      __UpperCamelCase       )       )







	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   int        =			create_inputs(self.tool.inputs       )

							lowercase_      :   int        =			[]

							for _input, input_type in zip(__UpperCamelCase  ,      self.tool.inputs       ):
													if isinstance(__UpperCamelCase  ,      __UpperCamelCase       ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input       ) for _input_type in input_type]       )
													else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input       )       )

        # Should not raise an error
							lowercase_      :   Tuple        =			self.tool(*__UpperCamelCase       )

							if not isinstance(__UpperCamelCase  ,      __UpperCamelCase       ):
													lowercase_      :   Any        =			[outputs]

							self.assertEqual(len(__UpperCamelCase       )  ,      len(self.tool.outputs       )       )







'''simple docstring'''

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

import os
from pathlib import Path






def 	_UpperCAmelCase    (  a		:	Dict						,			a		:	Optional[int]						,			a		:	Tuple							)			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   Any        =			{
						    'en': 'Machine learning is great, isn\'t it?',
						    'ru': 'Машинное обучение - это здорово, не так ли?',
						    'de': 'Maschinelles Lernen ist großartig, oder?',
						}

						# BLUE scores as follows:
						# "pair": [fairseq, transformers]
						lowercase_      :   List[str]        =			{
						    'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
						    'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
						    'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
						    'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
						}
						lowercase_      :   Optional[Any]        =			f"{src_lang}-{tgt_lang}"

						lowercase_      :   Optional[Any]        =			f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
						os.makedirs(a						,			exist_ok=a							)
						lowercase_      :   int        =			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
A:      List[str]  				=				Path(__file__).resolve().parent.parent.parent
A:      List[str]  				=				repo_dir / "model_cards"

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
						A				,		A				,		A:      Any  				=				model_name.split("-")
						A:      int  				=				model_cards_dir / "facebook" / model_name
						write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
