



from pathlib import Path

import fire
from tqdm import tqdm



def    snake_case_				(    _SCREAMING_SNAKE_CASE="ro"  ,	_SCREAMING_SNAKE_CASE="en"  ,	_SCREAMING_SNAKE_CASE="wmt16"  ,	_SCREAMING_SNAKE_CASE=None   ):
	try:
		import datasets
	except (ModuleNotFoundError, ImportError):
		raise ImportError("run pip install datasets"   )
	__lowercase		       =			F"""{src_lang}-{tgt_lang}"""
	print(F"""Converting {dataset}-{pair}"""   )
	__lowercase		       =			datasets.load_dataset(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if save_dir is None:
		__lowercase		       =			F"""{dataset}-{pair}"""
	__lowercase		       =			Path(_SCREAMING_SNAKE_CASE   )
	save_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )

	for split in ds.keys():
		print(F"""Splitting {split} with {ds[split].num_rows} records"""   )

		# to save to val.source, val.target like summary datasets
		__lowercase		       =			"val" if split == "validation" else split
		__lowercase		       =			save_dir.joinpath(F"""{fn}.source"""   )
		__lowercase		       =			save_dir.joinpath(F"""{fn}.target"""   )
		__lowercase		       =			src_path.open("w+"   )
		__lowercase		       =			tgt_path.open("w+"   )

		# reader is the bottleneck so writing one record at a time doesn't slow things down
		for x in tqdm(ds[split]   ):
			__lowercase		       =			x["translation"]
			src_fp.write(ex[src_lang] + "\n"   )
			tgt_fp.write(ex[tgt_lang] + "\n"   )

	print(F"""Saved {dataset} dataset to {save_dir}"""   )


if __name__ == "__main__":
	fire.Fire(download_wmt_dataset)




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

							# parameters for encoder hidden states
							__lowercase		       =			clip_extra_context_tokens
							__lowercase		       =			nn.Linear(
							    lowerCamelCase							,      self.clip_extra_context_tokens * cross_attention_dim	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.LayerNorm(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowercase		       =			image_embeddings.shape[0]
								__lowercase		       =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
								__lowercase		       =			classifier_free_guidance_embeddings.expand(
								    lowerCamelCase							,      -1	)
								__lowercase		       =			torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,      dim=0	)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase		       =			prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

							__lowercase		       =			self.encoder_hidden_states_proj(lowerCamelCase	)
							__lowercase		       =			self.text_encoder_hidden_states_norm(lowerCamelCase	)
							__lowercase		       =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,      dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings



