





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	tempfile.mkdtemp()

				lowerCamelCase__              =	[
				    '''[UNK]''',
				    '''[CLS]''',
				    '''[SEP]''',
				    '''[PAD]''',
				    '''[MASK]''',
				    '''want''',
				    '''##want''',
				    '''##ed''',
				    '''wa''',
				    '''un''',
				    '''runn''',
				    '''##ing''',
				    ''',''',
				    '''low''',
				    '''lowest''',
				]
				lowerCamelCase__              =	os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']							)
				with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''							) as vocab_writer:
					vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

				lowerCamelCase__              =	{
				    '''do_resize''': True,
				    '''size''': 2_0,
				    '''do_center_crop''': True,
				    '''crop_size''': 1_8,
				    '''do_normalize''': True,
				    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				lowerCamelCase__              =	os.path.join(self.tmpdirname ,						__lowerCAmelCase							)
				with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''							) as fp:
					json.dump(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return BertTokenizer.from_pretrained(self.tmpdirname ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return BertTokenizerFast.from_pretrained(self.tmpdirname ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				shutil.rmtree(self.tmpdirname							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	[np.random.randint(2_5_5 ,						size=(3, 3_0, 4_0_0) ,						dtype=np.uinta							)]
				lowerCamelCase__              =	[Image.fromarray(np.moveaxis(__lowerCAmelCase ,						0 ,						-1							)							) for x in image_inputs]
				return image_inputs





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_tokenizer()
				lowerCamelCase__              =	self.get_rust_tokenizer()
				lowerCamelCase__              =	self.get_image_processor()

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)
				processor_slow.save_pretrained(self.tmpdirname							)
				lowerCamelCase__              =	AlignProcessor.from_pretrained(self.tmpdirname ,						use_fast=__lowerCAmelCase							)

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)
				processor_fast.save_pretrained(self.tmpdirname							)
				lowerCamelCase__              =	AlignProcessor.from_pretrained(self.tmpdirname							)

				self.assertEqual(processor_slow.tokenizer.get_vocab() ,						tokenizer_slow.get_vocab()							)
				self.assertEqual(processor_fast.tokenizer.get_vocab() ,						tokenizer_fast.get_vocab()							)
				self.assertEqual(tokenizer_slow.get_vocab() ,						tokenizer_fast.get_vocab()							)
				self.assertIsInstance(processor_slow.tokenizer ,						__lowerCAmelCase							)
				self.assertIsInstance(processor_fast.tokenizer ,						__lowerCAmelCase							)

				self.assertEqual(processor_slow.image_processor.to_json_string() ,						image_processor.to_json_string()							)
				self.assertEqual(processor_fast.image_processor.to_json_string() ,						image_processor.to_json_string()							)
				self.assertIsInstance(processor_slow.image_processor ,						__lowerCAmelCase							)
				self.assertIsInstance(processor_fast.image_processor ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AlignProcessor(tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()							)
				processor.save_pretrained(self.tmpdirname							)

				lowerCamelCase__              =	self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''							)
				lowerCamelCase__              =	self.get_image_processor(do_normalize=__lowerCAmelCase ,						padding_value=1.0							)

				lowerCamelCase__              =	AlignProcessor.from_pretrained(
				    self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=__lowerCAmelCase ,						padding_value=1.0							)

				self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()							)
				self.assertIsInstance(processor.tokenizer ,						__lowerCAmelCase							)

				self.assertEqual(processor.image_processor.to_json_string() ,						image_processor_add_kwargs.to_json_string()							)
				self.assertIsInstance(processor.image_processor ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	self.prepare_image_inputs()

				lowerCamelCase__              =	image_processor(__lowerCAmelCase ,						return_tensors='''np'''							)
				lowerCamelCase__              =	processor(images=__lowerCAmelCase ,						return_tensors='''np'''							)

				for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum() ,						input_processor[key].sum() ,						delta=1E-2							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	'''lower newer'''

				lowerCamelCase__              =	processor(text=__lowerCAmelCase							)

				lowerCamelCase__              =	tokenizer(__lowerCAmelCase ,						padding='''max_length''' ,						max_length=6_4							)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	'''lower newer'''
				lowerCamelCase__              =	self.prepare_image_inputs()

				lowerCamelCase__              =	processor(text=__lowerCAmelCase ,						images=__lowerCAmelCase							)

				self.assertListEqual(list(inputs.keys()							) ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']							)

				# test if it raises when no input is passed
				with pytest.raises(__lowerCAmelCase							):
					processor()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				lowerCamelCase__              =	processor.batch_decode(__lowerCAmelCase							)
				lowerCamelCase__              =	tokenizer.batch_decode(__lowerCAmelCase							)

				self.assertListEqual(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	AlignProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	'''lower newer'''
				lowerCamelCase__              =	self.prepare_image_inputs()

				lowerCamelCase__              =	processor(text=__lowerCAmelCase ,						images=__lowerCAmelCase							)

				self.assertListEqual(list(inputs.keys()							) ,						processor.model_input_names							)










# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	{
	    '''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__              =	{
	    '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
	    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
	    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
	}
	lowerCamelCase__              =	F'{src_lang}-{tgt_lang}'

	lowerCamelCase__              =	F'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 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\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\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   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -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/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\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							)
	lowerCamelCase__              =	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
_a						=   Path(__file__).resolve().parent.parent.parent
_a						=   repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			_a						=   model_cards_dir / "allenai" / model_name
			write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



