"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm





def 	_lowerCamelCase			(			UpperCAmelCase__="ro",UpperCAmelCase__="en",UpperCAmelCase__="wmt16",UpperCAmelCase__=None       )				->  List[str]:


   '''simple docstring'''


   try:
      import datasets
   except (ModuleNotFoundError, ImportError):
      raise ImportError('run pip install datasets'       )
   a__  					=		f'''{src_lang}-{tgt_lang}'''
   print(f'''Converting {dataset}-{pair}'''       )
   a__  					=		datasets.load_dataset(UpperCAmelCase__,UpperCAmelCase__       )
   if save_dir is None:
      a__  					=		f'''{dataset}-{pair}'''
   a__  					=		Path(UpperCAmelCase__       )
   save_dir.mkdir(exist_ok=UpperCAmelCase__       )

   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
      a__  					=		"val" if split == "validation" else split
      a__  					=		save_dir.joinpath(f'''{fn}.source'''       )
      a__  					=		save_dir.joinpath(f'''{fn}.target'''       )
      a__  					=		src_path.open('w+'       )
      a__  					=		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]       ):
         a__  					=		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 __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= 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]

						UpperCAmelCase__:	str									= 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, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= 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"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings