





"""simple docstring"""







import torch
from torch import nn

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






class       SCREAMING_SNAKE_CASE__					(					_a						,				_a					):


						@register_to_config
						def __init__(							self				:			int						,						*,
						    lowerCAmelCase				:			int = 4						,						lowerCAmelCase				:			int = 768						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						):
										super().__init__()

										lowerCAmelCase  =				nn.Parameter(torch.zeros(lowerCAmelCase					)					)

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

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




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

										lowerCAmelCase  =				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, ...
										lowerCAmelCase  =				self.embedding_proj(lowerCAmelCase					)
										lowerCAmelCase  =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase					)
										lowerCAmelCase  =				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"
										lowerCAmelCase  =				self.clip_extra_context_tokens_proj(lowerCAmelCase					)
										lowerCAmelCase  =				clip_extra_context_tokens.reshape(lowerCAmelCase						,						-1						,						self.clip_extra_context_tokens					)
										lowerCAmelCase  =				clip_extra_context_tokens.permute(0						,						2						,						1					)

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

										return text_encoder_hidden_states, additive_clip_time_embeddings











"""simple docstring"""







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Union[str, Any] )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )

				lowerCAmelCase  =				chkpt["""model"""]

				# We have the base model one level deeper than the original XLM repository
				lowerCAmelCase  =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												lowerCAmelCase  =				v
								else:
												lowerCAmelCase  =				v

				lowerCAmelCase  =				chkpt["""params"""]
				lowerCAmelCase  =				{n: v for n, v in config.items() if not isinstance(snake_case__      ,       (torch.FloatTensor, numpy.ndarray) )}

				lowerCAmelCase  =				chkpt["""dico_word2id"""]
				lowerCAmelCase  =				{s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@"""      ,       """""" ): i for s, i in vocab.items()}

				# Save pytorch-model
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + CONFIG_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

				print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
				torch.save(snake_case__      ,       snake_case__ )

				print(f'''Save configuration file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )

				print(f'''Save vocab file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		a      =							parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





