





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor

class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	def __init__(	self       :    Optional[Any]				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    str=7				,			__lowerCamelCase       :    str=3				,			__lowerCamelCase       :    Tuple=18				,			__lowerCamelCase       :    Tuple=30				,			__lowerCamelCase       :    Any=400				,			__lowerCamelCase       :    Any=True				,			__lowerCamelCase       :    Optional[int]=None				,			__lowerCamelCase       :    str=True				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    Any=True				,			__lowerCamelCase       :    int=[0.48145466, 0.4578275, 0.40821073]				,			__lowerCamelCase       :    int=[0.26862954, 0.26130258, 0.27577711]				,			__lowerCamelCase       :    Any=True				,			)					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    parent
			__lowercase					=    batch_size
			__lowercase					=    num_channels
			__lowercase					=    image_size
			__lowercase					=    min_resolution
			__lowercase					=    max_resolution
			__lowercase					=    do_resize
			__lowercase					=    size if size is not None else {'height': 18, 'width': 18}
			__lowercase					=    do_center_crop
			__lowercase					=    crop_size if crop_size is not None else {'height': 18, 'width': 18}
			__lowercase					=    do_normalize
			__lowercase					=    image_mean
			__lowercase					=    image_std
			__lowercase					=    do_convert_rgb






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

			'''simple docstring'''




			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_convert_rgb": self.do_convert_rgb,
			}





@require_torch
@require_vision
class 		snake_case_      (       UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		OwlViTImageProcessor if is_vision_available() else None




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

			'''simple docstring'''




			__lowercase					=    OwlViTImageProcessingTester(self      )




	@property
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()




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

			'''simple docstring'''




			__lowercase					=    self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'do_resize'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'size'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'do_center_crop'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'center_crop'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'do_normalize'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'image_mean'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'image_std'      )      )
			self.assertTrue(hasattr(__lowerCamelCase				,			'do_convert_rgb'      )      )




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

			'''simple docstring'''




			__lowercase					=    self.image_processing_class.from_dict(self.image_processor_dict      )
			self.assertEqual(image_processor.size				,			{'height': 18, 'width': 18}      )
			self.assertEqual(image_processor.crop_size				,			{'height': 18, 'width': 18}      )

			__lowercase					=    self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84      )
			self.assertEqual(image_processor.size				,			{'height': 42, 'width': 42}      )
			self.assertEqual(image_processor.crop_size				,			{'height': 84, 'width': 84}      )




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			__lowercase					=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__lowerCamelCase      )

			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase				,			Image.Image      )

			# Test not batched input
			__lowercase					=    image_processing(image_inputs[0]				,			return_tensors='pt'      ).pixel_values
			self.assertEqual(
			    encoded_images.shape				,			(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )				,			)

			# Test batched
			__lowercase					=    image_processing(__lowerCamelCase				,			return_tensors='pt'      ).pixel_values
			self.assertEqual(
			    encoded_images.shape				,			(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )				,			)




	def       UpperCAmelCase (	self       :    Dict      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			__lowercase					=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__lowerCamelCase				,			numpify=__lowerCamelCase      )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase				,			np.ndarray      )

			# Test not batched input
			__lowercase					=    image_processing(image_inputs[0]				,			return_tensors='pt'      ).pixel_values
			self.assertEqual(
			    encoded_images.shape				,			(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )				,			)

			# Test batched
			__lowercase					=    image_processing(__lowerCamelCase				,			return_tensors='pt'      ).pixel_values
			self.assertEqual(
			    encoded_images.shape				,			(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )				,			)






	def       UpperCAmelCase (	self       :    int      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			__lowercase					=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__lowerCamelCase				,			torchify=__lowerCamelCase      )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase				,			torch.Tensor      )

			# Test not batched input
			__lowercase					=    image_processing(image_inputs[0]				,			return_tensors='pt'      ).pixel_values
			self.assertEqual(
			    encoded_images.shape				,			(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )				,			)

			# Test batched
			__lowercase					=    image_processing(__lowerCamelCase				,			return_tensors='pt'      ).pixel_values
			self.assertEqual(
			    encoded_images.shape				,			(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )				,			)











# 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)




