

"""simple docstring"""





import os
from pathlib import Path





def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Dict						       =			{
	    '''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]
	lowerCAmelCase__			:		Tuple						       =			{
	    '''wmt19-de-en-6-6-base''': [0, 38.37],
	    '''wmt19-de-en-6-6-big''': [0, 39.90],
	}
	lowerCAmelCase__			:		List[Any]						       =			f'{src_lang}-{tgt_lang}'

	lowerCAmelCase__			:		int						       =			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=A_		,  exist_ok=A_				)
	lowerCAmelCase__			:		List[str]						       =			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
__UpperCamelCase						:   List[Any]							   =	Path(__file__).resolve().parent.parent.parent
__UpperCamelCase						:   Any							   =	repo_dir / '''model_cards'''

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







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase						:   List[Any]							   =	logging.get_logger(__name__)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= ["pixel_values"]







					def __init__( self    :       Tuple     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Dict[str, int]] = None     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       bool = True     ,lowercase_    :       Union[int, float] = 1 / 2_5_5     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,**lowercase_    :       Tuple     ,):
						super().__init__(**lowercase_	)
						lowerCAmelCase__			:		Any						       =			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__			:		Tuple						       =			get_size_dict(lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			do_resize
						lowerCAmelCase__			:		Tuple						       =			do_rescale
						lowerCAmelCase__			:		Union[str, Any]						       =			do_normalize
						lowerCAmelCase__			:		Optional[int]						       =			size
						lowerCAmelCase__			:		List[Any]						       =			resample
						lowerCAmelCase__			:		int						       =			rescale_factor
						lowerCAmelCase__			:		int						       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__			:		Any						       =			image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       Dict[str, int]     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       str     ,):
						lowerCAmelCase__			:		Union[str, Any]						       =			get_size_dict(lowercase_	)
						if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'	)
						return resize(
						    lowercase_     ,size=(size['''height'''], size['''width'''])     ,resample=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       float     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Tuple	):
						return rescale(lowercase_     ,scale=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       np.ndarray     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Union[str, Any]     ,):
						return normalize(lowercase_     ,mean=lowercase_     ,std=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       ImageInput     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Dict[str, int] = None     ,lowercase_    :       PILImageResampling = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[float] = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[str, TensorType]] = None     ,lowercase_    :       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,**lowercase_    :       List[Any]     ,):
						lowerCAmelCase__			:		Optional[int]						       =			do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__			:		Any						       =			do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__			:		Dict						       =			do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__			:		Dict						       =			resample if resample is not None else self.resample
						lowerCAmelCase__			:		Dict						       =			rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__			:		Optional[Any]						       =			image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__			:		str						       =			image_std if image_std is not None else self.image_std

						lowerCAmelCase__			:		Any						       =			size if size is not None else self.size
						lowerCAmelCase__			:		Dict						       =			get_size_dict(lowercase_	)

						lowerCAmelCase__			:		Union[str, Any]						       =			make_list_of_images(lowercase_	)

						if not valid_images(lowercase_	):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

						if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''	)

						if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

						# All transformations expect numpy arrays.
						lowerCAmelCase__			:		Dict						       =			[to_numpy_array(lowercase_	) for image in images]

						if do_resize:
							lowerCAmelCase__			:		Optional[Any]						       =			[self.resize(image=lowercase_     ,size=lowercase_     ,resample=lowercase_	) for image in images]

						if do_rescale:
							lowerCAmelCase__			:		List[Any]						       =			[self.rescale(image=lowercase_     ,scale=lowercase_	) for image in images]

						if do_normalize:
							lowerCAmelCase__			:		Dict						       =			[self.normalize(image=lowercase_     ,mean=lowercase_     ,std=lowercase_	) for image in images]

						lowerCAmelCase__			:		Tuple						       =			[to_channel_dimension_format(lowercase_     ,lowercase_	) for image in images]

						lowerCAmelCase__			:		int						       =			{'''pixel_values''': images}
						return BatchFeature(data=lowercase_     ,tensor_type=lowercase_	)




