
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


SCREAMING_SNAKE_CASE_      :			str		  =      logging.get_logger(__name__)

@dataclass
class 		snake_case_      :







	'''simple docstring'''
	__UpperCamelCase        =		42
	__UpperCamelCase        =		42
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			return json.dumps(dataclasses.asdict(self      )				,			indent=2      ) + "\n"





@dataclass(frozen=A_	)
class 		snake_case_      :







	'''simple docstring'''
	__UpperCamelCase        =		42
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None




	def       UpperCAmelCase (	self       :    int      )					->   List[str]:

			'''simple docstring'''




			return json.dumps(dataclasses.asdict(self      )      ) + "\n"





class 		snake_case_      :







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    int				,			__lowerCamelCase       :    str      )					->   List[Any]:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Any      )					->   int:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    List[str]      )					->   int:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    Dict      )					->   Optional[Any]:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    str      )					->   Optional[int]:

			'''simple docstring'''




			if len(self.get_labels()      ) > 1:
					__lowercase					=    self.get_labels()[int(example.label      )]
			return example






	@classmethod
	def       UpperCAmelCase (	cls       :    List[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    List[str]=None      )					->   str:

			'''simple docstring'''




			with open(__lowerCamelCase				,			'r'				,			encoding='utf-8-sig'      ) as f:
					return list(csv.reader(__lowerCamelCase				,			delimiter='\t'				,			quotechar=__lowerCamelCase      )      )





class 		snake_case_      (       A_	):







	'''simple docstring'''




	def __init__(	self       :    List[Any]				,			__lowerCamelCase       :    Any=None				,			__lowerCamelCase       :    Optional[int]=None				,			__lowerCamelCase       :    Union[str, Any]="classification"				,			__lowerCamelCase       :    Optional[Any]=False      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    [] if labels is None else labels
			__lowercase					=    [] if examples is None else examples
			__lowercase					=    mode
			__lowercase					=    verbose




	def __len__(	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			return len(self.examples      )




	def __getitem__(	self       :    Any				,			__lowerCamelCase       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
					return SingleSentenceClassificationProcessor(labels=self.labels				,			examples=self.examples[idx]      )
			return self.examples[idx]




	@classmethod
	def       UpperCAmelCase (	cls       :    Union[str, Any]				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Tuple=""				,			__lowerCamelCase       :    List[Any]=0				,			__lowerCamelCase       :    str=1				,			__lowerCamelCase       :    Dict=None				,			__lowerCamelCase       :    str=False				,			**__lowerCamelCase       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    cls(**__lowerCamelCase      )
			processor.add_examples_from_csv(
			    __lowerCamelCase				,			split_name=__lowerCamelCase				,			column_label=__lowerCamelCase				,			column_text=__lowerCamelCase				,			column_id=__lowerCamelCase				,			skip_first_row=__lowerCamelCase				,			overwrite_labels=__lowerCamelCase				,			overwrite_examples=__lowerCamelCase				,			)
			return processor




	@classmethod
	def       UpperCAmelCase (	cls       :    List[str]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    Tuple=None				,			**__lowerCamelCase       :    Union[str, Any]      )					->   int:

			'''simple docstring'''




			__lowercase					=    cls(**__lowerCamelCase      )
			processor.add_examples(__lowerCamelCase				,			labels=__lowerCamelCase      )
			return processor




	def       UpperCAmelCase (	self       :    Optional[int]				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    Optional[Any]=""				,			__lowerCamelCase       :    List[Any]=0				,			__lowerCamelCase       :    Optional[Any]=1				,			__lowerCamelCase       :    Tuple=None				,			__lowerCamelCase       :    int=False				,			__lowerCamelCase       :    Optional[int]=False				,			__lowerCamelCase       :    List[str]=False				,			)					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    self._read_tsv(__lowerCamelCase      )
			if skip_first_row:
					__lowercase					=    lines[1:]
			__lowercase					=    []
			__lowercase					=    []
			__lowercase					=    []
			for i, line in enumerate(__lowerCamelCase      ):
					texts.append(line[column_text]      )
					labels.append(line[column_label]      )
					if column_id is not None:
							ids.append(line[column_id]      )
					else:
							__lowercase					=    F"{split_name}-{i}" if split_name else str(__lowerCamelCase      )
							ids.append(__lowerCamelCase      )

			return self.add_examples(
			    __lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			overwrite_labels=__lowerCamelCase				,			overwrite_examples=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[int]=None				,			__lowerCamelCase       :    List[str]=None				,			__lowerCamelCase       :    List[str]=False				,			__lowerCamelCase       :    Union[str, Any]=False      )					->   int:

			'''simple docstring'''




			if labels is not None and len(__lowerCamelCase      ) != len(__lowerCamelCase      ):
					raise ValueError(
					    F"Text and labels have mismatched lengths {len(__lowerCamelCase      )} and {len(__lowerCamelCase      )}"      )
			if ids is not None and len(__lowerCamelCase      ) != len(__lowerCamelCase      ):
					raise ValueError(F"Text and ids have mismatched lengths {len(__lowerCamelCase      )} and {len(__lowerCamelCase      )}"      )
			if ids is None:
					__lowercase					=    [None] * len(__lowerCamelCase      )
			if labels is None:
					__lowercase					=    [None] * len(__lowerCamelCase      )
			__lowercase					=    []
			__lowercase					=    set()
			for text_or_text_and_label, label, guid in zip(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      ):
					if isinstance(__lowerCamelCase				,			(tuple, list)      ) and label is None:
							__lowercase					=    text_or_text_and_label
					else:
							__lowercase					=    text_or_text_and_label
					added_labels.add(__lowerCamelCase      )
					examples.append(InputExample(guid=__lowerCamelCase				,			text_a=__lowerCamelCase				,			text_b=__lowerCamelCase				,			label=__lowerCamelCase      )      )

			# Update examples
			if overwrite_examples:
					__lowercase					=    examples
			else:
					self.examples.extend(__lowerCamelCase      )

			# Update labels
			if overwrite_labels:
					__lowercase					=    list(__lowerCamelCase      )
			else:
					__lowercase					=    list(set(self.labels      ).union(__lowerCamelCase      )      )

			return self.examples






	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    int=False				,			__lowerCamelCase       :    Tuple=0				,			__lowerCamelCase       :    Tuple=True				,			__lowerCamelCase       :    Union[str, Any]=None				,			)					->   int:

			'''simple docstring'''




			if max_length is None:
					__lowercase					=    tokenizer.max_len

			__lowercase					=    {label: i for i, label in enumerate(self.labels      )}

			__lowercase					=    []
			for ex_index, example in enumerate(self.examples      ):
					if ex_index % 10_000 == 0:
							logger.info(F"Tokenizing example {ex_index}"      )

					__lowercase					=    tokenizer.encode(
					    example.text_a				,			add_special_tokens=__lowerCamelCase				,			max_length=min(__lowerCamelCase				,			tokenizer.max_len      )				,			)
					all_input_ids.append(__lowerCamelCase      )

			__lowercase					=    max(len(__lowerCamelCase      ) for input_ids in all_input_ids      )

			__lowercase					=    []
			for ex_index, (input_ids, example) in enumerate(zip(__lowerCamelCase				,			self.examples      )      ):
					if ex_index % 10_000 == 0:
							logger.info(F"Writing example {ex_index}/{len(self.examples      )}"      )
					# The mask has 1 for real tokens and 0 for padding tokens. Only real
					# tokens are attended to.
					__lowercase					=    [1 if mask_padding_with_zero else 0] * len(__lowerCamelCase      )

					# Zero-pad up to the sequence length.
					__lowercase					=    batch_length - len(__lowerCamelCase      )
					if pad_on_left:
							__lowercase					=    ([pad_token] * padding_length) + input_ids
							__lowercase					=    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
					else:
							__lowercase					=    input_ids + ([pad_token] * padding_length)
							__lowercase					=    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

					if len(__lowerCamelCase      ) != batch_length:
							raise ValueError(F"Error with input length {len(__lowerCamelCase      )} vs {batch_length}"      )
					if len(__lowerCamelCase      ) != batch_length:
							raise ValueError(F"Error with input length {len(__lowerCamelCase      )} vs {batch_length}"      )

					if self.mode == "classification":
							__lowercase					=    label_map[example.label]
					elif self.mode == "regression":
							__lowercase					=    float(example.label      )
					else:
							raise ValueError(self.mode      )

					if ex_index < 5 and self.verbose:
							logger.info('*** Example ***'      )
							logger.info(F"guid: {example.guid}"      )
							logger.info(F"input_ids: {' '.join([str(__lowerCamelCase      ) for x in input_ids]      )}"      )
							logger.info(F"attention_mask: {' '.join([str(__lowerCamelCase      ) for x in attention_mask]      )}"      )
							logger.info(F"label: {example.label} (id = {label})"      )

					features.append(InputFeatures(input_ids=__lowerCamelCase				,			attention_mask=__lowerCamelCase				,			label=__lowerCamelCase      )      )

			if return_tensors is None:
					return features
			elif return_tensors == "tf":
					if not is_tf_available():
							raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'      )
					import tensorflow as tf

					def gen():
							for ex in features:
									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

					__lowercase					=    tf.data.Dataset.from_generator(
					    __lowerCamelCase				,			({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,			({'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )}, tf.TensorShape([]      ))				,			)
					return dataset
			elif return_tensors == "pt":
					if not is_torch_available():
							raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'      )
					import torch
					from torch.utils.data import TensorDataset

					__lowercase					=    torch.tensor([f.input_ids for f in features]				,			dtype=torch.long      )
					__lowercase					=    torch.tensor([f.attention_mask for f in features]				,			dtype=torch.long      )
					if self.mode == "classification":
							__lowercase					=    torch.tensor([f.label for f in features]				,			dtype=torch.long      )
					elif self.mode == "regression":
							__lowercase					=    torch.tensor([f.label for f in features]				,			dtype=torch.float      )

					__lowercase					=    TensorDataset(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      )
					return dataset
			else:
					raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )











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

import os
from pathlib import Path



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    {
		    '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					=    {
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		__lowercase					=    F"{src_lang}-{tgt_lang}"

		__lowercase					=    F"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\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-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\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\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\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=5\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 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\n```\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```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
		model_card_dir.mkdir(parents=snake_case	,      exist_ok=snake_case       )
		__lowercase					=    os.path.join(snake_case	,      'README.md'       )
		print(F"Generating {path}"       )
		with open(snake_case	,      'w'	,      encoding='utf-8'       ) as f:
				f.write(snake_case       )


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE_      :			Tuple		  =      Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_      :			Any		  =      repo_dir / '''model_cards'''

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




