
"""simple docstring"""







from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings




@maybe_allow_in_graph
class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = "geglu"	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = "layer_norm"	,		_SCREAMING_SNAKE_CASE = False	,		):
			super().__init__()
			__lowerCAmelCase      :						List[str]						      =				only_cross_attention

			__lowerCAmelCase      :						Optional[Any]						      =				(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
			__lowerCAmelCase      :						Union[str, Any]						      =				(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

			if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
				raise ValueError(
				    f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
				    f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."			)

			# Define 3 blocks. Each block has its own normalization layer.
			# 1. Self-Attn
			if self.use_ada_layer_norm:
				__lowerCAmelCase      :						List[Any]						      =				AdaLayerNorm(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			elif self.use_ada_layer_norm_zero:
				__lowerCAmelCase      :						int						      =				AdaLayerNormZero(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Any						      =				nn.LayerNorm(_SCREAMING_SNAKE_CASE	,		elementwise_affine=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				Attention(
			    query_dim=_SCREAMING_SNAKE_CASE	,		heads=_SCREAMING_SNAKE_CASE	,		dim_head=_SCREAMING_SNAKE_CASE	,		dropout=_SCREAMING_SNAKE_CASE	,		bias=_SCREAMING_SNAKE_CASE	,		cross_attention_dim=cross_attention_dim if only_cross_attention else None	,		upcast_attention=_SCREAMING_SNAKE_CASE	,		)

			# 2. Cross-Attn
			if cross_attention_dim is not None or double_self_attention:
				# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
				# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
				# the second cross attention block.
				__lowerCAmelCase      :						List[str]						      =				(
				    AdaLayerNorm(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				    if self.use_ada_layer_norm
				    else nn.LayerNorm(_SCREAMING_SNAKE_CASE	,		elementwise_affine=_SCREAMING_SNAKE_CASE			)
				)
				__lowerCAmelCase      :						Tuple						      =				Attention(
				    query_dim=_SCREAMING_SNAKE_CASE	,		cross_attention_dim=cross_attention_dim if not double_self_attention else None	,		heads=_SCREAMING_SNAKE_CASE	,		dim_head=_SCREAMING_SNAKE_CASE	,		dropout=_SCREAMING_SNAKE_CASE	,		bias=_SCREAMING_SNAKE_CASE	,		upcast_attention=_SCREAMING_SNAKE_CASE	,		)  # is self-attn if encoder_hidden_states is none
			else:
				__lowerCAmelCase      :						Dict						      =				None
				__lowerCAmelCase      :						Optional[int]						      =				None

			# 3. Feed-forward
			__lowerCAmelCase      :						int						      =				nn.LayerNorm(_SCREAMING_SNAKE_CASE	,		elementwise_affine=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				FeedForward(_SCREAMING_SNAKE_CASE	,		dropout=_SCREAMING_SNAKE_CASE	,		activation_fn=_SCREAMING_SNAKE_CASE	,		final_dropout=_SCREAMING_SNAKE_CASE			)

			# let chunk size default to None
			__lowerCAmelCase      :						List[Any]						      =				None
			__lowerCAmelCase      :						List[str]						      =				0

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			# Sets chunk feed-forward
			__lowerCAmelCase      :						str						      =				chunk_size
			__lowerCAmelCase      :						Tuple						      =				dim







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		):
			# Notice that normalization is always applied before the real computation in the following blocks.
			# 1. Self-Attention
			if self.use_ada_layer_norm:
				__lowerCAmelCase      :						Dict						      =				self.norma(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			elif self.use_ada_layer_norm_zero:
				__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				self.norma(
				    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		hidden_dtype=hidden_states.dtype			)
			else:
				__lowerCAmelCase      :						Dict						      =				self.norma(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				cross_attention_kwargs if cross_attention_kwargs is not None else {}

			__lowerCAmelCase      :						List[str]						      =				self.attna(
			    _SCREAMING_SNAKE_CASE	,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None	,		attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
			if self.use_ada_layer_norm_zero:
				__lowerCAmelCase      :						Any						      =				gate_msa.unsqueeze(1			) * attn_output
			__lowerCAmelCase      :						str						      =				attn_output + hidden_states

			# 2. Cross-Attention
			if self.attna is not None:
				__lowerCAmelCase      :						List[str]						      =				(
				    self.norma(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) if self.use_ada_layer_norm else self.norma(_SCREAMING_SNAKE_CASE			)
				)

				__lowerCAmelCase      :						Dict						      =				self.attna(
				    _SCREAMING_SNAKE_CASE	,		encoder_hidden_states=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
				__lowerCAmelCase      :						List[str]						      =				attn_output + hidden_states

			# 3. Feed-forward
			__lowerCAmelCase      :						List[Any]						      =				self.norma(_SCREAMING_SNAKE_CASE			)

			if self.use_ada_layer_norm_zero:
				__lowerCAmelCase      :						Any						      =				norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

			if self._chunk_size is not None:
				# "feed_forward_chunk_size" can be used to save memory
				if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
					raise ValueError(
					    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."			)

				__lowerCAmelCase      :						Dict						      =				norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
				__lowerCAmelCase      :						List[Any]						      =				torch.cat(
				    [self.ff(_SCREAMING_SNAKE_CASE			) for hid_slice in norm_hidden_states.chunk(_SCREAMING_SNAKE_CASE	,		dim=self._chunk_dim			)]	,		dim=self._chunk_dim	,		)
			else:
				__lowerCAmelCase      :						int						      =				self.ff(_SCREAMING_SNAKE_CASE			)

			if self.use_ada_layer_norm_zero:
				__lowerCAmelCase      :						str						      =				gate_mlp.unsqueeze(1			) * ff_output

			__lowerCAmelCase      :						Optional[Any]						      =				ff_output + hidden_states

			return hidden_states




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 4	,		_SCREAMING_SNAKE_CASE = 0.0	,		_SCREAMING_SNAKE_CASE = "geglu"	,		_SCREAMING_SNAKE_CASE = False	,		):
			super().__init__()
			__lowerCAmelCase      :						str						      =				int(dim * mult			)
			__lowerCAmelCase      :						str						      =				dim_out if dim_out is not None else dim

			if activation_fn == "gelu":
				__lowerCAmelCase      :						int						      =				GELU(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			if activation_fn == "gelu-approximate":
				__lowerCAmelCase      :						Optional[Any]						      =				GELU(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		approximate='tanh'			)
			elif activation_fn == "geglu":
				__lowerCAmelCase      :						str						      =				GEGLU(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			elif activation_fn == "geglu-approximate":
				__lowerCAmelCase      :						List[str]						      =				ApproximateGELU(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				nn.ModuleList([]			)
			# project in
			self.net.append(_SCREAMING_SNAKE_CASE			)
			# project dropout
			self.net.append(nn.Dropout(_SCREAMING_SNAKE_CASE			)			)
			# project out
			self.net.append(nn.Linear(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)			)
			# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
			if final_dropout:
				self.net.append(nn.Dropout(_SCREAMING_SNAKE_CASE			)			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			for module in self.net:
				__lowerCAmelCase      :						Dict						      =				module(_SCREAMING_SNAKE_CASE			)
			return hidden_states




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = "none"			):
			super().__init__()
			__lowerCAmelCase      :						List[Any]						      =				nn.Linear(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				approximate

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if gate.device.type != "mps":
				return F.gelu(_SCREAMING_SNAKE_CASE	,		approximate=self.approximate			)
			# mps: gelu is not implemented for float16
			return F.gelu(gate.to(dtype=torch.floataa			)	,		approximate=self.approximate			).to(dtype=gate.dtype			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Union[str, Any]						      =				self.proj(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				self.gelu(_SCREAMING_SNAKE_CASE			)
			return hidden_states




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			super().__init__()
			__lowerCAmelCase      :						str						      =				nn.Linear(_SCREAMING_SNAKE_CASE	,		dim_out * 2			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if gate.device.type != "mps":
				return F.gelu(_SCREAMING_SNAKE_CASE			)
			# mps: gelu is not implemented for float16
			return F.gelu(gate.to(dtype=torch.floataa			)			).to(dtype=gate.dtype			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				self.proj(_SCREAMING_SNAKE_CASE			).chunk(2	,		dim=-1			)
			return hidden_states * self.gelu(_SCREAMING_SNAKE_CASE			)




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			super().__init__()
			__lowerCAmelCase      :						List[Any]						      =				nn.Linear(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						int						      =				self.proj(_SCREAMING_SNAKE_CASE			)
			return x * torch.sigmoid(1.702 * x			)




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			super().__init__()
			__lowerCAmelCase      :						Optional[Any]						      =				nn.Embedding(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				nn.SiLU()
			__lowerCAmelCase      :						Any						      =				nn.Linear(_SCREAMING_SNAKE_CASE	,		embedding_dim * 2			)
			__lowerCAmelCase      :						List[str]						      =				nn.LayerNorm(_SCREAMING_SNAKE_CASE	,		elementwise_affine=_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						str						      =				self.linear(self.silu(self.emb(_SCREAMING_SNAKE_CASE			)			)			)
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[Any]						      =				torch.chunk(_SCREAMING_SNAKE_CASE	,		2			)
			__lowerCAmelCase      :						List[Any]						      =				self.norm(_SCREAMING_SNAKE_CASE			) * (1 + scale) + shift
			return x




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			super().__init__()

			__lowerCAmelCase      :						List[str]						      =				CombinedTimestepLabelEmbeddings(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				nn.SiLU()
			__lowerCAmelCase      :						Optional[Any]						      =				nn.Linear(_SCREAMING_SNAKE_CASE	,		6 * embedding_dim	,		bias=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				nn.LayerNorm(_SCREAMING_SNAKE_CASE	,		elementwise_affine=_SCREAMING_SNAKE_CASE	,		eps=1E-6			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None			):
			__lowerCAmelCase      :						List[str]						      =				self.linear(self.silu(self.emb(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		hidden_dtype=_SCREAMING_SNAKE_CASE			)			)			)
			__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Dict						      =				emb.chunk(6	,		dim=1			)
			__lowerCAmelCase      :						Any						      =				self.norm(_SCREAMING_SNAKE_CASE			) * (1 + scale_msa[:, None]) + shift_msa[:, None]
			return x, gate_msa, shift_mlp, scale_mlp, gate_mlp




class 			A__	(     nn.Module):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 1E-5			):
			super().__init__()
			__lowerCAmelCase      :						Any						      =				num_groups
			__lowerCAmelCase      :						Optional[int]						      =				eps

			if act_fn is None:
				__lowerCAmelCase      :						Dict						      =				None
			else:
				__lowerCAmelCase      :						Optional[Any]						      =				get_activation(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				nn.Linear(_SCREAMING_SNAKE_CASE	,		out_dim * 2			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			if self.act:
				__lowerCAmelCase      :						str						      =				self.act(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				self.linear(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				emb[:, :, None, None]
			__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				emb.chunk(2	,		dim=1			)

			__lowerCAmelCase      :						Tuple						      =				F.group_norm(_SCREAMING_SNAKE_CASE	,		self.num_groups	,		eps=self.eps			)
			__lowerCAmelCase      :						int						      =				x * (1 + scale) + shift
			return x

"""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)