



import torch
from torch import nn

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



class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







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

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

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

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



					def    A__		(			self    ,	*, lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									UpperCAmelCase_						=				image_embeddings.shape[0]
									UpperCAmelCase_						=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0     )
									UpperCAmelCase_						=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase    ,	-1     )
									UpperCAmelCase_						=				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_						=				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_						=				self.embedding_proj(lowerCAmelCase     )
							UpperCAmelCase_						=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase     )
							UpperCAmelCase_						=				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_						=				self.clip_extra_context_tokens_proj(lowerCAmelCase     )
							UpperCAmelCase_						=				clip_extra_context_tokens.reshape(lowerCAmelCase    ,	-1    ,	self.clip_extra_context_tokens     )
							UpperCAmelCase_						=				clip_extra_context_tokens.permute(0    ,	2    ,	1     )

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

							return text_encoder_hidden_states, additive_clip_time_embeddings






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

