


import torch
from torch import nn

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



class 							A    (       __A ,  __A							):
					'''simple docstring'''






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




							"""simple docstring"""


							super().__init__()

							lowercase__     			=		nn.Parameter(torch.zeros(lowercase_		)		)

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

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


					def   lowerCamelCase__     (self    :						Optional[Any]      ,					*, _UpperCAmelCase    :						Tuple      ,					_UpperCAmelCase    :						Union[str, Any]      ,					_UpperCAmelCase    :						Union[str, Any]      ,					_UpperCAmelCase    :						str		)						->   Any:




							"""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(
									    lowercase_      ,					-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(lowercase_		)
							lowercase__     			=		self.clip_image_embeddings_project_to_time_embeddings(lowercase_		)
							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(lowercase_		)
							lowercase__     			=		clip_extra_context_tokens.reshape(lowercase_      ,					-1      ,					self.clip_extra_context_tokens		)
							lowercase__     			=		clip_extra_context_tokens.permute(0      ,					2      ,					1		)

							lowercase__     			=		self.encoder_hidden_states_proj(lowercase_		)
							lowercase__     			=		self.text_encoder_hidden_states_norm(lowercase_		)
							lowercase__     			=		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 BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   Optional[Any]    					=				BertForPreTraining(_lowerCamelCase)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}''')
    torch.save(model.state_dict()		,	_lowerCamelCase)


if __name__ == "__main__":
      UpperCamelCase  							=						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(
          '''--bert_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained BERT 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.'''
      )
      UpperCamelCase  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



