from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin


@dataclass
class     lowercase     (  snake_case_  ):
							lowercase__    :							torch.FloatTensor



class     lowercase     (  snake_case_					,							snake_case_  ):




							@register_to_config
							def __init__(    self :       Union[str, Any]						,				_UpperCamelCase :       Union[str, Any] = 32						,				_UpperCamelCase :       Dict = 64						,				_UpperCamelCase :       Any = 20						,				_UpperCamelCase :       int = 768						,				_UpperCamelCase :       Dict=77						,				_UpperCamelCase :       Tuple=4						,				_UpperCamelCase :       Optional[int] = 0.0						,				_UpperCamelCase :       int = "silu"						,				_UpperCamelCase :       List[str] = None						,				_UpperCamelCase :       Union[str, Any] = None						,				_UpperCamelCase :       List[Any] = "linear"						,				_UpperCamelCase :       List[Any] = "prd"						,				_UpperCamelCase :       str = None						,				_UpperCamelCase :       List[str] = None						,				_UpperCamelCase :       Any = None						,				)       ->					Optional[Any]:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =							num_attention_heads
												SCREAMING_SNAKE_CASE           =							attention_head_dim
												SCREAMING_SNAKE_CASE           =							num_attention_heads * attention_head_dim
												SCREAMING_SNAKE_CASE           =							additional_embeddings

												SCREAMING_SNAKE_CASE           =							time_embed_dim or inner_dim
												SCREAMING_SNAKE_CASE           =							embedding_proj_dim or embedding_dim
												SCREAMING_SNAKE_CASE           =							clip_embed_dim or embedding_dim

												SCREAMING_SNAKE_CASE           =							Timesteps(UpperCamelCase__						,				UpperCamelCase__						,				0   )
												SCREAMING_SNAKE_CASE           =							TimestepEmbedding(UpperCamelCase__						,				UpperCamelCase__						,				out_dim=UpperCamelCase__						,				act_fn=UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							nn.Linear(UpperCamelCase__						,				UpperCamelCase__   )

												if embedding_proj_norm_type is None:
																	SCREAMING_SNAKE_CASE           =							None
												elif embedding_proj_norm_type == "layer":
																	SCREAMING_SNAKE_CASE           =							nn.LayerNorm(UpperCamelCase__   )
												else:
																	raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"   )

												SCREAMING_SNAKE_CASE           =							nn.Linear(UpperCamelCase__						,				UpperCamelCase__   )

												if encoder_hid_proj_type is None:
																	SCREAMING_SNAKE_CASE           =							None
												elif encoder_hid_proj_type == "linear":
																	SCREAMING_SNAKE_CASE           =							nn.Linear(UpperCamelCase__						,				UpperCamelCase__   )
												else:
																	raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"   )

												SCREAMING_SNAKE_CASE           =							nn.Parameter(torch.zeros(1						,				num_embeddings + additional_embeddings						,				UpperCamelCase__   )   )

												if added_emb_type == "prd":
																	SCREAMING_SNAKE_CASE           =							nn.Parameter(torch.zeros(1						,				1						,				UpperCamelCase__   )   )
												elif added_emb_type is None:
																	SCREAMING_SNAKE_CASE           =							None
												else:
																	raise ValueError(
																	    F"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`."   )

												SCREAMING_SNAKE_CASE           =							nn.ModuleList(
												    [
												        BasicTransformerBlock(
												            UpperCamelCase__						,				UpperCamelCase__						,				UpperCamelCase__						,				dropout=UpperCamelCase__						,				activation_fn="gelu"						,				attention_bias=UpperCamelCase__						,				)
												        for d in range(UpperCamelCase__   )
												    ]   )

												if norm_in_type == "layer":
																	SCREAMING_SNAKE_CASE           =							nn.LayerNorm(UpperCamelCase__   )
												elif norm_in_type is None:
																	SCREAMING_SNAKE_CASE           =							None
												else:
																	raise ValueError(F"Unsupported norm_in_type: {norm_in_type}."   )

												SCREAMING_SNAKE_CASE           =							nn.LayerNorm(UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							nn.Linear(UpperCamelCase__						,				UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							torch.full(
												    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,				-1_0_0_0_0.0   )
												causal_attention_mask.triu_(1   )
												SCREAMING_SNAKE_CASE           =							causal_attention_mask[None, ...]
												self.register_buffer("causal_attention_mask"						,				UpperCamelCase__						,				persistent=UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							nn.Parameter(torch.zeros(1						,				UpperCamelCase__   )   )
												SCREAMING_SNAKE_CASE           =							nn.Parameter(torch.zeros(1						,				UpperCamelCase__   )   )




							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def 						__snake_case(    self :       Optional[Any]   )       ->					Dict[str, AttentionProcessor]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							{}

												def fn_recursive_add_processors(_UpperCamelCase :       Optional[Any]						,				_UpperCamelCase :       int						,				_UpperCamelCase :       Optional[Any]   ):
																	if hasattr(UpperCamelCase__						,				"set_processor"   ):
																						SCREAMING_SNAKE_CASE           =							module.processor

																	for sub_name, child in module.named_children():
																						fn_recursive_add_processors(F"{name}.{sub_name}"						,				UpperCamelCase__						,				UpperCamelCase__   )

																	return processors

												for name, module in self.named_children():
																	fn_recursive_add_processors(UpperCamelCase__						,				UpperCamelCase__						,				UpperCamelCase__   )

												return processors




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       str   )       ->					Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							len(self.attn_processors.keys()   )

												if isinstance(UpperCamelCase__						,				UpperCamelCase__   ) and len(UpperCamelCase__   ) != count:
																	raise ValueError(
																	    F"A dict of processors was passed, but the number of processors {len(UpperCamelCase__   )} does not match the"
																	    F" number of attention layers: {count}. Please make sure to pass {count} processor classes."   )

												def fn_recursive_attn_processor(_UpperCamelCase :       Dict						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       Dict   ):
																	if hasattr(UpperCamelCase__						,				"set_processor"   ):
																						if not isinstance(UpperCamelCase__						,				UpperCamelCase__   ):
																											module.set_processor(UpperCamelCase__   )
																						else:
																											module.set_processor(processor.pop(F"{name}.processor"   )   )

																	for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(F"{name}.{sub_name}"						,				UpperCamelCase__						,				UpperCamelCase__   )

												for name, module in self.named_children():
																	fn_recursive_attn_processor(UpperCamelCase__						,				UpperCamelCase__						,				UpperCamelCase__   )




							def 						__snake_case(    self :       Dict   )       ->					List[Any]:



												'''simple docstring'''

												self.set_attn_processor(AttnProcessor()   )




							def 						__snake_case(    self :       int						,				_UpperCamelCase :       Optional[int]						,				_UpperCamelCase :       Union[str, Any]						,				_UpperCamelCase :       Union[str, Any]						,				_UpperCamelCase :       Any = None						,				_UpperCamelCase :       str = None						,				_UpperCamelCase :       Optional[int] = True						,				)       ->					Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							hidden_states.shape[0]

												SCREAMING_SNAKE_CASE           =							timestep
												if not torch.is_tensor(UpperCamelCase__   ):
																	SCREAMING_SNAKE_CASE           =							torch.tensor([timesteps]						,				dtype=torch.long						,				device=hidden_states.device   )
												elif torch.is_tensor(UpperCamelCase__   ) and len(timesteps.shape   ) == 0:
																	SCREAMING_SNAKE_CASE           =							timesteps[None].to(hidden_states.device   )

												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												SCREAMING_SNAKE_CASE           =							timesteps * torch.ones(UpperCamelCase__						,				dtype=timesteps.dtype						,				device=timesteps.device   )

												SCREAMING_SNAKE_CASE           =							self.time_proj(UpperCamelCase__   )

												# timesteps does not contain any weights and will always return f32 tensors
												# but time_embedding might be fp16, so we need to cast here.
												SCREAMING_SNAKE_CASE           =							timesteps_projected.to(dtype=self.dtype   )
												SCREAMING_SNAKE_CASE           =							self.time_embedding(UpperCamelCase__   )

												if self.embedding_proj_norm is not None:
																	SCREAMING_SNAKE_CASE           =							self.embedding_proj_norm(UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							self.embedding_proj(UpperCamelCase__   )
												if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																	SCREAMING_SNAKE_CASE           =							self.encoder_hidden_states_proj(UpperCamelCase__   )
												elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																	raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"   )

												SCREAMING_SNAKE_CASE           =							self.proj_in(UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							self.positional_embedding.to(hidden_states.dtype   )

												SCREAMING_SNAKE_CASE           =							[]
												SCREAMING_SNAKE_CASE           =							0

												if encoder_hidden_states is not None:
																	additional_embeds.append(UpperCamelCase__   )
																	additional_embeddings_len += encoder_hidden_states.shape[1]

												if len(proj_embeddings.shape   ) == 2:
																	SCREAMING_SNAKE_CASE           =							proj_embeddings[:, None, :]

												if len(hidden_states.shape   ) == 2:
																	SCREAMING_SNAKE_CASE           =							hidden_states[:, None, :]

												SCREAMING_SNAKE_CASE           =							additional_embeds + [
												    proj_embeddings,
												    time_embeddings[:, None, :],
												    hidden_states,
												]

												if self.prd_embedding is not None:
																	SCREAMING_SNAKE_CASE           =							self.prd_embedding.to(hidden_states.dtype   ).expand(UpperCamelCase__						,				-1						,				-1   )
																	additional_embeds.append(UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							torch.cat(
												    UpperCamelCase__						,				dim=1						,				)

												# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
												SCREAMING_SNAKE_CASE           =							additional_embeddings_len + proj_embeddings.shape[1] + 1
												if positional_embeddings.shape[1] < hidden_states.shape[1]:
																	SCREAMING_SNAKE_CASE           =							F.pad(
																	    UpperCamelCase__						,				(
																	        0,
																	        0,
																	        additional_embeddings_len,
																	        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																	    )						,				value=0.0						,				)

												SCREAMING_SNAKE_CASE           =							hidden_states + positional_embeddings

												if attention_mask is not None:
																	SCREAMING_SNAKE_CASE           =							(1 - attention_mask.to(hidden_states.dtype   )) * -1_0_0_0_0.0
																	SCREAMING_SNAKE_CASE           =							F.pad(UpperCamelCase__						,				(0, self.additional_embeddings)						,				value=0.0   )
																	SCREAMING_SNAKE_CASE           =							(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
																	SCREAMING_SNAKE_CASE           =							attention_mask.repeat_interleave(self.config.num_attention_heads						,				dim=0   )

												if self.norm_in is not None:
																	SCREAMING_SNAKE_CASE           =							self.norm_in(UpperCamelCase__   )

												for block in self.transformer_blocks:
																	SCREAMING_SNAKE_CASE           =							block(UpperCamelCase__						,				attention_mask=UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							self.norm_out(UpperCamelCase__   )

												if self.prd_embedding is not None:
																	SCREAMING_SNAKE_CASE           =							hidden_states[:, -1]
												else:
																	SCREAMING_SNAKE_CASE           =							hidden_states[:, additional_embeddings_len:]

												SCREAMING_SNAKE_CASE           =							self.proj_to_clip_embeddings(UpperCamelCase__   )

												if not return_dict:
																	return (predicted_image_embedding,)

												return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase__   )





							def 						__snake_case(    self :       int						,				_UpperCamelCase :       Dict   )       ->					Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							(prior_latents * self.clip_std) + self.clip_mean
												return prior_latents








"""simple docstring"""



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(lowercase		,				lowercase		,				lowercase					)

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


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





