
"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 			A__	(     _lowerCamelCase	,					_lowerCamelCase	,					unittest.TestCase):
		A_						:	Any           =     StableDiffusionPanoramaPipeline
		A_						:	Union[str, Any]           =     TEXT_TO_IMAGE_PARAMS
		A_						:	Dict           =     TEXT_TO_IMAGE_BATCH_PARAMS
		A_						:	Optional[int]           =     TEXT_TO_IMAGE_IMAGE_PARAMS
		A_						:	Any           =     TEXT_TO_IMAGE_IMAGE_PARAMS

		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Union[str, Any]						      =				UNetaDConditionModel(
			    block_out_channels=(32, 64)	,		layers_per_block=1	,		sample_size=32	,		in_channels=4	,		out_channels=4	,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')	,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')	,		cross_attention_dim=32	,		)
			__lowerCAmelCase      :						Optional[int]						      =				DDIMScheduler()
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Union[str, Any]						      =				AutoencoderKL(
			    block_out_channels=[32, 64]	,		in_channels=3	,		out_channels=3	,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']	,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']	,		latent_channels=4	,		)
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Optional[Any]						      =				CLIPTextConfig(
			    bos_token_id=0	,		eos_token_id=2	,		hidden_size=32	,		intermediate_size=37	,		layer_norm_eps=1E-05	,		num_attention_heads=4	,		num_hidden_layers=5	,		pad_token_id=1	,		vocab_size=10_00	,		)
			__lowerCAmelCase      :						List[Any]						      =				CLIPTextModel(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

			__lowerCAmelCase      :						int						      =				{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'safety_checker': None,
			    'feature_extractor': None,
			}
			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			__lowerCAmelCase      :						Tuple						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				{
			    'prompt': 'a photo of the dolomites',
			    'generator': generator,
			    # Setting height and width to None to prevent OOMs on CPU.
			    'height': None,
			    'width': None,
			    'num_inference_steps': 1,
			    'guidance_scale': 6.0,
			    'output_type': 'numpy',
			}
			return inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'cpu'  # ensure determinism for the device-dependent torch.Generator
			__lowerCAmelCase      :						Dict						      =				self.get_dummy_components()
			__lowerCAmelCase      :						List[Any]						      =				StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				sd_pipe.to(_SCREAMING_SNAKE_CASE			)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				sd_pipe(**_SCREAMING_SNAKE_CASE			).images
			__lowerCAmelCase      :						Dict						      =				image[0, -3:, -3:, -1]
			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase      :						int						      =				np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]			)

			assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

		def 							__lowerCamelCase							(				self			):
			super().test_inference_batch_consistent(batch_sizes=[1, 2]			)

		def 							__lowerCamelCase							(				self			):
			super().test_inference_batch_single_identical(batch_size=2	,		expected_max_diff=3.25E-3			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'cpu'  # ensure determinism for the device-dependent torch.Generator
			__lowerCAmelCase      :						Union[str, Any]						      =				self.get_dummy_components()
			__lowerCAmelCase      :						List[str]						      =				StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				sd_pipe.to(_SCREAMING_SNAKE_CASE			)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						str						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				'french fries'
			__lowerCAmelCase      :						Tuple						      =				sd_pipe(**_SCREAMING_SNAKE_CASE	,		negative_prompt=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				output.images
			__lowerCAmelCase      :						List[Any]						      =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase      :						Tuple						      =				np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]			)

			assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				'cpu'  # ensure determinism for the device-dependent torch.Generator
			__lowerCAmelCase      :						Optional[Any]						      =				self.get_dummy_components()
			__lowerCAmelCase      :						int						      =				StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				sd_pipe.to(_SCREAMING_SNAKE_CASE			)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				sd_pipe(**_SCREAMING_SNAKE_CASE	,		view_batch_size=2			)
			__lowerCAmelCase      :						int						      =				output.images
			__lowerCAmelCase      :						str						      =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase      :						List[Any]						      =				np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]			)

			assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'cpu'  # ensure determinism for the device-dependent torch.Generator
			__lowerCAmelCase      :						List[str]						      =				self.get_dummy_components()
			__lowerCAmelCase      :						List[str]						      =				EulerAncestralDiscreteScheduler(
			    beta_start=0.0_0085	,		beta_end=0.012	,		beta_schedule='scaled_linear'			)
			__lowerCAmelCase      :						Optional[int]						      =				StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				sd_pipe.to(_SCREAMING_SNAKE_CASE			)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				sd_pipe(**_SCREAMING_SNAKE_CASE			).images
			__lowerCAmelCase      :						List[Any]						      =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase      :						Any						      =				np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]			)

			assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'cpu'  # ensure determinism for the device-dependent torch.Generator
			__lowerCAmelCase      :						int						      =				self.get_dummy_components()
			__lowerCAmelCase      :						Dict						      =				PNDMScheduler(
			    beta_start=0.0_0085	,		beta_end=0.012	,		beta_schedule='scaled_linear'	,		skip_prk_steps=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				sd_pipe.to(_SCREAMING_SNAKE_CASE			)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				sd_pipe(**_SCREAMING_SNAKE_CASE			).images
			__lowerCAmelCase      :						Tuple						      =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase      :						Optional[int]						      =				np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]			)

			assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




@slow
@require_torch_gpu
class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=0			):
			__lowerCAmelCase      :						Tuple						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				{
			    'prompt': 'a photo of the dolomites',
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'stabilityai/stable-diffusion-2-base'
			__lowerCAmelCase      :						Tuple						      =				DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	,		subfolder='scheduler'			)
			__lowerCAmelCase      :						Dict						      =				StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE	,		safety_checker=_SCREAMING_SNAKE_CASE			)
			pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)
			pipe.enable_attention_slicing()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.get_inputs()
			__lowerCAmelCase      :						int						      =				pipe(**_SCREAMING_SNAKE_CASE			).images
			__lowerCAmelCase      :						Optional[Any]						      =				image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 5_12, 20_48, 3)

			__lowerCAmelCase      :						Optional[int]						      =				np.array(
			    [
			        0.3696_8392,
			        0.2702_5372,
			        0.3244_6766,
			        0.2837_9387,
			        0.3636_3274,
			        0.3073_3347,
			        0.2710_0027,
			        0.2705_4125,
			        0.2553_6096,
			    ]			)

			assert np.abs(expected_slice - image_slice			).max() < 1E-2

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				StableDiffusionPanoramaPipeline.from_pretrained(
			    'stabilityai/stable-diffusion-2-base'	,		safety_checker=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
			pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)
			pipe.enable_attention_slicing()

			__lowerCAmelCase      :						Optional[int]						      =				self.get_inputs()
			__lowerCAmelCase      :						List[str]						      =				pipe(**_SCREAMING_SNAKE_CASE			).images
			__lowerCAmelCase      :						Union[str, Any]						      =				image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 5_12, 20_48, 3)

			__lowerCAmelCase      :						int						      =				np.array(
			    [
			        [
			            0.0,
			            0.0,
			            0.0,
			            0.0,
			            0.0,
			            0.0,
			            0.0,
			            0.0,
			            0.0,
			        ]
			    ]			)

			assert np.abs(expected_slice - image_slice			).max() < 1E-3

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				0

			def callback_fn(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) -> None:
				__lowerCAmelCase      :						str						      =				True
				nonlocal number_of_steps
				number_of_steps += 1
				if step == 1:
					__lowerCAmelCase      :						Optional[int]						      =				latents.detach().cpu().numpy()
					assert latents.shape == (1, 4, 64, 2_56)
					__lowerCAmelCase      :						Tuple						      =				latents[0, -3:, -3:, -1]

					__lowerCAmelCase      :						Union[str, Any]						      =				np.array(
					    [
					        0.1868_1869,
					        0.3390_7816,
					        0.536_1276,
					        0.1443_2865,
					        -0.0285_6611,
					        -0.7394_1123,
					        0.2339_7987,
					        0.4732_2682,
					        -0.3782_3164,
					    ]			)
					assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
				elif step == 2:
					__lowerCAmelCase      :						Tuple						      =				latents.detach().cpu().numpy()
					assert latents.shape == (1, 4, 64, 2_56)
					__lowerCAmelCase      :						str						      =				latents[0, -3:, -3:, -1]

					__lowerCAmelCase      :						Optional[Any]						      =				np.array(
					    [
					        0.1853_9645,
					        0.3398_7248,
					        0.537_8559,
					        0.1443_7142,
					        -0.0245_5261,
					        -0.733_8317,
					        0.2399_0755,
					        0.4735_6272,
					        -0.378_6505,
					    ]			)

					assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2

			__lowerCAmelCase      :						Any						      =				False

			__lowerCAmelCase      :						Optional[Any]						      =				'stabilityai/stable-diffusion-2-base'
			__lowerCAmelCase      :						Union[str, Any]						      =				DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	,		subfolder='scheduler'			)
			__lowerCAmelCase      :						Dict						      =				StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE	,		safety_checker=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)
			pipe.enable_attention_slicing()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.get_inputs()
			pipe(**_SCREAMING_SNAKE_CASE	,		callback=_SCREAMING_SNAKE_CASE	,		callback_steps=1			)
			assert callback_fn.has_been_called
			assert number_of_steps == 3







		def 							__lowerCamelCase							(				self			):
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			__lowerCAmelCase      :						List[str]						      =				'stabilityai/stable-diffusion-2-base'
			__lowerCAmelCase      :						int						      =				DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	,		subfolder='scheduler'			)
			__lowerCAmelCase      :						int						      =				StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE	,		safety_checker=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)
			pipe.enable_attention_slicing(1			)
			pipe.enable_sequential_cpu_offload()

			__lowerCAmelCase      :						str						      =				self.get_inputs()
			__lowerCAmelCase      :						List[Any]						      =				pipe(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						str						      =				torch.cuda.max_memory_allocated()
			# make sure that less than 5.2 GB is allocated
			assert mem_bytes < 5.5 * 10**9

"""simple docstring"""







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

import os
from pathlib import Path
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						str						      =				{
	    'en': 'Machine learning is great, isn\'t it?',
	    'ru': 'Машинное обучение - это здорово, не так ли?',
	    'de': 'Maschinelles Lernen ist großartig, oder?',
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	__lowerCAmelCase      :						Union[str, Any]						      =				{
	    'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
	    'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
	    'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
	    'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
	}
	__lowerCAmelCase      :						List[str]						      =				F"{src_lang}-{tgt_lang}"

	__lowerCAmelCase      :						Tuple						      =				F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
	os.makedirs(_UpperCamelCase	,  exist_ok=_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				os.path.join(_UpperCamelCase	,  'README.md'		)
	print(F"Generating {path}"		)
	with open(_UpperCamelCase	,  'w'	,  encoding='utf-8'		) as f:
		f.write(_UpperCamelCase		)


# make sure we are under the root of the project
lowerCamelCase__        =		Path(__file__).resolve().parent.parent.parent
lowerCamelCase__        =		repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__        =		model_name.split("""-""")
							lowerCamelCase__        =		model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)