


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

import os
from pathlib import Path






def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       List[str]     ):
							a__    =  {
							    '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]
							a__    =  {
							    'wmt16-en-de-dist-12-1': [28.3, 27.52],
							    'wmt16-en-de-dist-6-1': [27.4, 27.11],
							    'wmt16-en-de-12-1': [26.9, 25.75],
							}
							a__    =  F'{src_lang}-{tgt_lang}'

							a__    =  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=__lowerCAmelCase      ,					exist_ok=__lowerCAmelCase     )
							a__    =  os.path.join(__lowerCAmelCase      ,					'README.md'     )
							print(F'Generating {path}'     )
							with open(__lowerCAmelCase      ,					'w'      ,					encoding='utf-8'     ) as f:
														f.write(__lowerCAmelCase     )


# make sure we are under the root of the project
snake_case							: Any      =   Path(__file__).resolve().parent.parent.parent
snake_case							: int      =   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"]:
						snake_case							: List[str]      =   model_cards_dir / '''allenai''' / model_name
						write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)








from manim import *







class  snake_case_     (lowerCamelCase_   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[int]:
												a__    =  Rectangle(height=0.5    ,width=0.5   )
												a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0   )

												a__    =  Rectangle(height=0.25    ,width=0.25   )

												a__    =  [mem.copy() for i in range(6   )]
												a__    =  [mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('CPU'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												cpu.move_to([-2.5, -0.5, 0]   )
												self.add(__snake_case   )

												a__    =  [mem.copy() for i in range(4   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('GPU'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												gpu.move_to([-1, -1, 0]   )
												self.add(__snake_case   )

												a__    =  [mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )

												a__    =  Text('Model'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												model.move_to([3, -1.0, 0]   )
												self.add(__snake_case   )

												a__    =  []
												a__    =  []

												for i, rect in enumerate(__snake_case   ):
																			a__    =  fill.copy().set_fill(__snake_case    ,opacity=0.8   )
																			target.move_to(__snake_case   )
																			model_arr.append(__snake_case   )

																			a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0.0   ).set_fill(__snake_case    ,opacity=0.8   )
																			cpu_target.move_to(cpu_left_col_base[i]   )
																			model_cpu_arr.append(__snake_case   )

												self.add(*__snake_case    ,*__snake_case   )

												a__    =  [meta_mem.copy() for i in range(6   )]
												a__    =  [meta_mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('Disk'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												disk.move_to([-4, -1.25, 0]   )
												self.add(__snake_case    ,__snake_case   )

												a__    =  Square(side_length=2.2   )
												key.move_to([-5, 2, 0]   )

												a__    =  MarkupText(
												    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'    ,font_size=18    ,)

												key_text.move_to([-5, 2.4, 0]   )

												self.add(__snake_case    ,__snake_case   )

												a__    =  MarkupText(
												    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'    ,font_size=18    ,)

												blue_text.next_to(__snake_case    ,DOWN * 2.4    ,aligned_edge=key_text.get_left()   )
												self.add(__snake_case   )

												a__    =  MarkupText(
												    F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case   )   )

												a__    =  Square(0.3   )
												input.set_fill(__snake_case    ,opacity=1.0   )
												input.set_stroke(width=0.0   )
												input.next_to(model_base[0]    ,__snake_case    ,buff=0.5   )

												self.play(Write(__snake_case   )   )

												input.generate_target()
												input.target.next_to(model_arr[0]    ,direction=__snake_case    ,buff=0.02   )
												self.play(MoveToTarget(__snake_case   )   )

												self.play(FadeOut(__snake_case   )   )

												a__    =  Arrow(start=__snake_case    ,end=__snake_case    ,color=__snake_case    ,buff=0.5   )
												a.next_to(model_arr[0].get_left()    ,__snake_case    ,buff=0.2   )

												model_cpu_arr[0].generate_target()
												model_cpu_arr[0].target.move_to(gpu_rect[0]   )

												a__    =  MarkupText(
												    F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )   )

												a__    =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

												self.play(
												    Write(__snake_case   )    ,Circumscribe(model_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_cpu_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)
												self.play(MoveToTarget(model_cpu_arr[0]   )   )

												a__    =  a.copy()
												for i in range(6   ):
																			a_c.next_to(model_arr[i].get_right() + 0.02    ,__snake_case    ,buff=0.2   )

																			input.generate_target()
																			input.target.move_to(model_arr[i].get_right() + 0.02   )

																			a__    =  AnimationGroup(
																			    FadeOut(__snake_case    ,run_time=0.5   )    ,MoveToTarget(__snake_case    ,run_time=0.5   )    ,FadeIn(__snake_case    ,run_time=0.5   )    ,lag_ratio=0.2   )

																			self.play(__snake_case   )

																			model_cpu_arr[i].generate_target()
																			model_cpu_arr[i].target.move_to(cpu_left_col_base[i]   )

																			if i < 5:
																										model_cpu_arr[i + 1].generate_target()
																										model_cpu_arr[i + 1].target.move_to(gpu_rect[0]   )
																										if i >= 1:
																																	a__    =  0.7

																										self.play(
																										    Circumscribe(model_arr[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_arr[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,)
																										if i < 1:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]   )    ,MoveToTarget(model_cpu_arr[i + 1]   )    ,)
																										else:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]    ,run_time=0.7   )    ,MoveToTarget(model_cpu_arr[i + 1]    ,run_time=0.7   )    ,)
																			else:
																										model_cpu_arr[i].generate_target()
																										model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]   )
																										input.generate_target()
																										input.target.next_to(model_arr[-1].get_right()    ,RIGHT + 0.02    ,buff=0.2   )

																										self.play(
																										    Circumscribe(model_arr[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)

																										self.play(MoveToTarget(model_cpu_arr[i]   )   )

																			a__    =  a_c
																			a__    =  a_c.copy()

												input.generate_target()
												input.target.next_to(model_base[-1]    ,RIGHT + 0.02    ,buff=0.5   )
												self.play(
												    FadeOut(__snake_case   )    ,FadeOut(__snake_case    ,run_time=0.5   )    ,)

												a__    =  MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.'    ,font_size=24   )
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )    ,MoveToTarget(__snake_case   )   )

												self.wait()




