





'''simple docstring'''






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder




@dataclass
class 			UpperCAmelCase					(      _a       ):
      _lowercase:      "DiagonalGaussianDistribution"




class 			UpperCAmelCase					(      _a							,		_a       ):
      _lowercase:      List[str]											=       True




      @register_to_config
      def __init__(      self      :			Optional[int]      ,      __snake_case      :			int = 3      ,      __snake_case      :			int = 3      ,      __snake_case      :			Tuple[str] = ("DownEncoderBlock2D",)      ,      __snake_case      :			Tuple[str] = ("UpDecoderBlock2D",)      ,      __snake_case      :			Tuple[int] = (64,)      ,      __snake_case      :			int = 1      ,      __snake_case      :			str = "silu"      ,      __snake_case      :			int = 4      ,      __snake_case      :			int = 32      ,      __snake_case      :			int = 32      ,      __snake_case      :			float = 0.1_82_15      ,      )    ->				List[Any]:
            super().__init__()

            # pass init params to Encoder
            _lowerCAmelCase												=							Encoder(
                in_channels=__lowerCamelCase      ,      out_channels=__lowerCamelCase      ,      down_block_types=__lowerCamelCase      ,      block_out_channels=__lowerCamelCase      ,      layers_per_block=__lowerCamelCase      ,      act_fn=__lowerCamelCase      ,      norm_num_groups=__lowerCamelCase      ,      double_z=__lowerCamelCase      ,      )

            # pass init params to Decoder
            _lowerCAmelCase												=							Decoder(
                in_channels=__lowerCamelCase      ,      out_channels=__lowerCamelCase      ,      up_block_types=__lowerCamelCase      ,      block_out_channels=__lowerCamelCase      ,      layers_per_block=__lowerCamelCase      ,      norm_num_groups=__lowerCamelCase      ,      act_fn=__lowerCamelCase      ,      )

            _lowerCAmelCase												=							nn.Convad(2 * latent_channels      ,      2 * latent_channels      ,      1  )
            _lowerCAmelCase												=							nn.Convad(__lowerCamelCase      ,      __lowerCamelCase      ,      1  )

            _lowerCAmelCase												=							False
            _lowerCAmelCase												=							False

            # only relevant if vae tiling is enabled
            _lowerCAmelCase												=							self.config.sample_size
            _lowerCAmelCase												=							(
                self.config.sample_size[0]
                if isinstance(self.config.sample_size      ,      (list, tuple)  )
                else self.config.sample_size
            )
            _lowerCAmelCase												=							int(sample_size / (2 ** (len(self.config.block_out_channels  ) - 1))  )
            _lowerCAmelCase												=							0.25




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			str      ,      __snake_case      :			int=False  )    ->				Any:
            if isinstance(__lowerCamelCase      ,      (Encoder, Decoder)  ):
                  _lowerCAmelCase												=							value




      def     lowercase__   (      self      :			str      ,      __snake_case      :			bool = True  )    ->				Optional[Any]:
            _lowerCAmelCase												=							use_tiling




      def     lowercase__   (      self      :			Optional[int]  )    ->				str:
            self.enable_tiling(__lowerCamelCase  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							True




      def     lowercase__   (      self      :			Tuple  )    ->				List[str]:
            _lowerCAmelCase												=							False




      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def     lowercase__   (      self      :			Any  )    ->				List[str]:
            _lowerCAmelCase												=							{}

            def fn_recursive_add_processors(__snake_case      :			str      ,      __snake_case      :			torch.nn.Module      ,      __snake_case      :			Dict[str, AttentionProcessor]  ):
                  if hasattr(__lowerCamelCase      ,      """set_processor"""  ):
                        _lowerCAmelCase												=							module.processor

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

                  return processors

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

            return processors




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Union[AttentionProcessor, Dict[str, AttentionProcessor]]  )    ->				Any:
            _lowerCAmelCase												=							len(self.attn_processors.keys()  )

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

            def fn_recursive_attn_processor(__snake_case      :			str      ,      __snake_case      :			torch.nn.Module      ,      __snake_case      :			Tuple  ):
                  if hasattr(__lowerCamelCase      ,      """set_processor"""  ):
                        if not isinstance(__lowerCamelCase      ,      __lowerCamelCase  ):
                              module.set_processor(__lowerCamelCase  )
                        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}"      ,      __lowerCamelCase      ,      __lowerCamelCase  )

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




      def     lowercase__   (      self      :			Dict  )    ->				Optional[int]:
            self.set_attn_processor(AttnProcessor()  )




      @apply_forward_hook
      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			bool = True  )    ->				Optional[Any]:
            if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
                  return self.tiled_encode(__lowerCamelCase      ,      return_dict=__lowerCamelCase  )

            if self.use_slicing and x.shape[0] > 1:
                  _lowerCAmelCase												=							[self.encoder(__lowerCamelCase  ) for x_slice in x.split(1  )]
                  _lowerCAmelCase												=							torch.cat(__lowerCamelCase  )
            else:
                  _lowerCAmelCase												=							self.encoder(__lowerCamelCase  )

            _lowerCAmelCase												=							self.quant_conv(__lowerCamelCase  )
            _lowerCAmelCase												=							DiagonalGaussianDistribution(__lowerCamelCase  )

            if not return_dict:
                  return (posterior,)

            return AutoencoderKLOutput(latent_dist=__lowerCamelCase  )




      def     lowercase__   (      self      :			int      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			bool = True  )    ->				Tuple:
            if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
                  return self.tiled_decode(__lowerCamelCase      ,      return_dict=__lowerCamelCase  )

            _lowerCAmelCase												=							self.post_quant_conv(__lowerCamelCase  )
            _lowerCAmelCase												=							self.decoder(__lowerCamelCase  )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=__lowerCamelCase  )




      @apply_forward_hook
      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			bool = True  )    ->				List[str]:
            if self.use_slicing and z.shape[0] > 1:
                  _lowerCAmelCase												=							[self._decode(__lowerCamelCase  ).sample for z_slice in z.split(1  )]
                  _lowerCAmelCase												=							torch.cat(__lowerCamelCase  )
            else:
                  _lowerCAmelCase												=							self._decode(__lowerCamelCase  ).sample

            if not return_dict:
                  return (decoded,)

            return DecoderOutput(sample=__lowerCamelCase  )




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[Any]  )    ->				str:
            _lowerCAmelCase												=							min(a.shape[2]      ,      b.shape[2]      ,      __lowerCamelCase  )
            for y in range(__lowerCamelCase  ):
                  _lowerCAmelCase												=							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
            return b




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[Any]  )    ->				Any:
            _lowerCAmelCase												=							min(a.shape[3]      ,      b.shape[3]      ,      __lowerCamelCase  )
            for x in range(__lowerCamelCase  ):
                  _lowerCAmelCase												=							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
            return b




      def     lowercase__   (      self      :			str      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			bool = True  )    ->				Optional[int]:
            _lowerCAmelCase												=							int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)  )
            _lowerCAmelCase												=							int(self.tile_latent_min_size * self.tile_overlap_factor  )
            _lowerCAmelCase												=							self.tile_latent_min_size - blend_extent

            # Split the image into 512x512 tiles and encode them separately.
            _lowerCAmelCase												=							[]
            for i in range(0      ,      x.shape[2]      ,      __lowerCamelCase  ):
                  _lowerCAmelCase												=							[]
                  for j in range(0      ,      x.shape[3]      ,      __lowerCamelCase  ):
                        _lowerCAmelCase												=							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                        _lowerCAmelCase												=							self.encoder(__lowerCamelCase  )
                        _lowerCAmelCase												=							self.quant_conv(__lowerCamelCase  )
                        row.append(__lowerCamelCase  )
                  rows.append(__lowerCamelCase  )
            _lowerCAmelCase												=							[]
            for i, row in enumerate(__lowerCamelCase  ):
                  _lowerCAmelCase												=							[]
                  for j, tile in enumerate(__lowerCamelCase  ):
                        # blend the above tile and the left tile
                        # to the current tile and add the current tile to the result row
                        if i > 0:
                              _lowerCAmelCase												=							self.blend_v(rows[i - 1][j]      ,      __lowerCamelCase      ,      __lowerCamelCase  )
                        if j > 0:
                              _lowerCAmelCase												=							self.blend_h(row[j - 1]      ,      __lowerCamelCase      ,      __lowerCamelCase  )
                        result_row.append(tile[:, :, :row_limit, :row_limit]  )
                  result_rows.append(torch.cat(__lowerCamelCase      ,      dim=3  )  )

            _lowerCAmelCase												=							torch.cat(__lowerCamelCase      ,      dim=2  )
            _lowerCAmelCase												=							DiagonalGaussianDistribution(__lowerCamelCase  )

            if not return_dict:
                  return (posterior,)

            return AutoencoderKLOutput(latent_dist=__lowerCamelCase  )




      def     lowercase__   (      self      :			str      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			bool = True  )    ->				int:
            _lowerCAmelCase												=							int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)  )
            _lowerCAmelCase												=							int(self.tile_sample_min_size * self.tile_overlap_factor  )
            _lowerCAmelCase												=							self.tile_sample_min_size - blend_extent

            # Split z into overlapping 64x64 tiles and decode them separately.
            # The tiles have an overlap to avoid seams between tiles.
            _lowerCAmelCase												=							[]
            for i in range(0      ,      z.shape[2]      ,      __lowerCamelCase  ):
                  _lowerCAmelCase												=							[]
                  for j in range(0      ,      z.shape[3]      ,      __lowerCamelCase  ):
                        _lowerCAmelCase												=							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                        _lowerCAmelCase												=							self.post_quant_conv(__lowerCamelCase  )
                        _lowerCAmelCase												=							self.decoder(__lowerCamelCase  )
                        row.append(__lowerCamelCase  )
                  rows.append(__lowerCamelCase  )
            _lowerCAmelCase												=							[]
            for i, row in enumerate(__lowerCamelCase  ):
                  _lowerCAmelCase												=							[]
                  for j, tile in enumerate(__lowerCamelCase  ):
                        # blend the above tile and the left tile
                        # to the current tile and add the current tile to the result row
                        if i > 0:
                              _lowerCAmelCase												=							self.blend_v(rows[i - 1][j]      ,      __lowerCamelCase      ,      __lowerCamelCase  )
                        if j > 0:
                              _lowerCAmelCase												=							self.blend_h(row[j - 1]      ,      __lowerCamelCase      ,      __lowerCamelCase  )
                        result_row.append(tile[:, :, :row_limit, :row_limit]  )
                  result_rows.append(torch.cat(__lowerCamelCase      ,      dim=3  )  )

            _lowerCAmelCase												=							torch.cat(__lowerCamelCase      ,      dim=2  )
            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=__lowerCamelCase  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			bool = False      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[torch.Generator] = None      ,      )    ->				Tuple:
            _lowerCAmelCase												=							sample
            _lowerCAmelCase												=							self.encode(__lowerCamelCase  ).latent_dist
            if sample_posterior:
                  _lowerCAmelCase												=							posterior.sample(generator=__lowerCamelCase  )
            else:
                  _lowerCAmelCase												=							posterior.mode()
            _lowerCAmelCase												=							self.decode(__lowerCamelCase  ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=__lowerCamelCase  )













'''simple docstring'''






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      def get_masked_lm_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_layer_array(lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_attention_layer_array(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )
            _lowerCAmelCase												=							array.reshape(lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      print(f"Loading model based on config from {config_path}..."  )
      _lowerCAmelCase												=							BertConfig.from_json_file(lowerCAmelCase  )
      _lowerCAmelCase												=							BertForMaskedLM(lowerCAmelCase  )

      # Layers
      for layer_index in range(0							,     config.num_hidden_layers  ):
            _lowerCAmelCase												=							model.bert.encoder.layer[layer_index]

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/kernel"""							,     self_attn.query.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/bias"""							,     self_attn.query.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/kernel"""							,     self_attn.key.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/bias"""							,     self_attn.key.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/kernel"""							,     self_attn.value.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/bias"""							,     self_attn.value.bias.data.shape  )

            # Self-attention Output
            _lowerCAmelCase												=							layer.attention.output

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/kernel"""							,     self_output.dense.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/bias"""							,     self_output.dense.bias.data.shape  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

      # Embeddings
      _lowerCAmelCase												=							get_encoder_array("""_position_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_type_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/gamma"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/beta"""  )

      # LM Head
      _lowerCAmelCase												=							model.cls.predictions.transform

      _lowerCAmelCase												=							get_masked_lm_array("""dense/kernel"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""dense/bias"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/gamma"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/beta"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

      # Integration test - should load without any errors ;)
      _lowerCAmelCase												=							BertForMaskedLM.from_pretrained(lowerCAmelCase  )
      print(new_model.eval()  )

      print("""Model conversion was done sucessfully!"""  )


if __name__ == "__main__":
      A__    :       str              =argparse.ArgumentParser()
      parser.add_argument(
          '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
      )
      parser.add_argument(
          '''--bert_config_file''',
          type=str,
          required=True,
          help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''',
          type=str,
          required=True,
          help='''Path to the output PyTorch model.''',
      )
      A__    :       Dict              =parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






