






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       42



class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				@register_to_config
				def __init__(     self						:    Any,  lowerCamelCase						:    int = 65_536,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 2,  lowerCamelCase						:    int = 2,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    str = "fourier",  lowerCamelCase						:    bool = True,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    float = 0.0,  lowerCamelCase						:    Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),  lowerCamelCase						:    Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),  lowerCamelCase						:    Tuple[str] = "UNetMidBlock1D",  lowerCamelCase						:    str = None,  lowerCamelCase						:    Tuple[int] = (32, 32, 64),  lowerCamelCase						:    str = None,  lowerCamelCase						:    int = 8,  lowerCamelCase						:    int = 1,  lowerCamelCase						:    bool = False,  ):

								'''simple docstring'''
								super().__init__()
								lowercase__    =			sample_size

								# time
								if time_embedding_type == "fourier":
												lowercase__    =			GaussianFourierProjection(
												    embedding_size=8,  set_W_to_weight=lowerCamelCase,  log=lowerCamelCase,  flip_sin_to_cos=lowerCamelCase  )
												lowercase__    =			2 * block_out_channels[0]
								elif time_embedding_type == "positional":
												lowercase__    =			Timesteps(
												    block_out_channels[0],  flip_sin_to_cos=lowerCamelCase,  downscale_freq_shift=lowerCamelCase  )
												lowercase__    =			block_out_channels[0]

								if use_timestep_embedding:
												lowercase__    =			block_out_channels[0] * 4
												lowercase__    =			TimestepEmbedding(
												    in_channels=lowerCamelCase,  time_embed_dim=lowerCamelCase,  act_fn=lowerCamelCase,  out_dim=block_out_channels[0],  )

								lowercase__    =			nn.ModuleList([]  )
								lowercase__    =			None
								lowercase__    =			nn.ModuleList([]  )
								lowercase__    =			None

								# down
								lowercase__    =			in_channels
								for i, down_block_type in enumerate(lowerCamelCase  ):
												lowercase__    =			output_channel
												lowercase__    =			block_out_channels[i]

												if i == 0:
																input_channel += extra_in_channels

												lowercase__    =			i == len(lowerCamelCase  ) - 1

												lowercase__    =			get_down_block(
												    lowerCamelCase,  num_layers=lowerCamelCase,  in_channels=lowerCamelCase,  out_channels=lowerCamelCase,  temb_channels=block_out_channels[0],  add_downsample=not is_final_block or downsample_each_block,  )
												self.down_blocks.append(lowerCamelCase  )

								# mid
								lowercase__    =			get_mid_block(
								    lowerCamelCase,  in_channels=block_out_channels[-1],  mid_channels=block_out_channels[-1],  out_channels=block_out_channels[-1],  embed_dim=block_out_channels[0],  num_layers=lowerCamelCase,  add_downsample=lowerCamelCase,  )

								# up
								lowercase__    =			list(reversed(lowerCamelCase  )  )
								lowercase__    =			reversed_block_out_channels[0]
								if out_block_type is None:
												lowercase__    =			out_channels
								else:
												lowercase__    =			block_out_channels[0]

								for i, up_block_type in enumerate(lowerCamelCase  ):
												lowercase__    =			output_channel
												lowercase__    =			(
												    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase  ) - 1 else final_upsample_channels
												)

												lowercase__    =			i == len(lowerCamelCase  ) - 1

												lowercase__    =			get_up_block(
												    lowerCamelCase,  num_layers=lowerCamelCase,  in_channels=lowerCamelCase,  out_channels=lowerCamelCase,  temb_channels=block_out_channels[0],  add_upsample=not is_final_block,  )
												self.up_blocks.append(lowerCamelCase  )
												lowercase__    =			output_channel

								# out
								lowercase__    =			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4,  32  )
								lowercase__    =			get_out_block(
								    out_block_type=lowerCamelCase,  num_groups_out=lowerCamelCase,  embed_dim=block_out_channels[0],  out_channels=lowerCamelCase,  act_fn=lowerCamelCase,  fc_dim=block_out_channels[-1] // 4,  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    torch.FloatTensor,  lowerCamelCase						:    Union[torch.Tensor, float, int],  lowerCamelCase						:    bool = True,  ):

								'''simple docstring'''
								lowercase__    =			timestep
								if not torch.is_tensor(lowerCamelCase  ):
												lowercase__    =			torch.tensor([timesteps],  dtype=torch.long,  device=sample.device  )
								elif torch.is_tensor(lowerCamelCase  ) and len(timesteps.shape  ) == 0:
												lowercase__    =			timesteps[None].to(sample.device  )

								lowercase__    =			self.time_proj(lowerCamelCase  )
								if self.config.use_timestep_embedding:
												lowercase__    =			self.time_mlp(lowerCamelCase  )
								else:
												lowercase__    =			timestep_embed[..., None]
												lowercase__    =			timestep_embed.repeat([1, 1, sample.shape[2]]  ).to(sample.dtype  )
												lowercase__    =			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])  )

								# 2. down
								lowercase__    =			()
								for downsample_block in self.down_blocks:
												lowercase__ ,						lowercase__    =			downsample_block(hidden_states=lowerCamelCase,  temb=lowerCamelCase  )
												down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
												lowercase__    =			self.mid_block(lowerCamelCase,  lowerCamelCase  )

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks  ):
												lowercase__    =			down_block_res_samples[-1:]
												lowercase__    =			down_block_res_samples[:-1]
												lowercase__    =			upsample_block(lowerCamelCase,  res_hidden_states_tuple=lowerCamelCase,  temb=lowerCamelCase  )

								# 5. post-process
								if self.out_block:
												lowercase__    =			self.out_block(lowerCamelCase,  lowerCamelCase  )

								if not return_dict:
												return (sample,)

								return UNetaDOutput(sample=lowerCamelCase  )












import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				lowercase__    =			(
				    ('''layer.''', '''layer_'''),
				    ('''word_embeddings.weight''', '''word_embeddings'''),
				    ('''position_embeddings.weight''', '''position_embeddings'''),
				    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
				    ('''.''', '''/'''),
				    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
				    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
				    ('''weight''', '''kernel'''),
				)

				if not os.path.isdir(lowerCamelCase_				):
								os.makedirs(lowerCamelCase_				)

				lowercase__    =			model.state_dict()

				def to_tf_var_name(lowerCamelCase_				):
								for patt, repl in iter(lowerCamelCase_				):
												lowercase__    =			name.replace(lowerCamelCase_		,   lowerCamelCase_				)
								return F"""bert/{name}"""

				def create_tf_var(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):
								lowercase__    =			tf.dtypes.as_dtype(tensor.dtype				)
								lowercase__    =			tf.get_variable(dtype=lowerCamelCase_		,   shape=tensor.shape		,   name=lowerCamelCase_		,   initializer=tf.zeros_initializer()				)
								session.run(tf.variables_initializer([tf_var]				)				)
								session.run(lowerCamelCase_				)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												lowercase__    =			to_tf_var_name(lowerCamelCase_				)
												lowercase__    =			state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose				):
																lowercase__    =			torch_tensor.T
												lowercase__    =			create_tf_var(tensor=lowerCamelCase_		,   name=lowerCamelCase_		,   session=lowerCamelCase_				)
												tf.keras.backend.set_value(lowerCamelCase_		,   lowerCamelCase_				)
												lowercase__    =			session.run(lowerCamelCase_				)
												print(F"""Successfully created {tf_name}: {np.allclose(lowerCamelCase_		,   lowerCamelCase_				)}"""				)

								lowercase__    =			tf.train.Saver(tf.trainable_variables()				)
								saver.save(lowerCamelCase_		,   os.path.join(lowerCamelCase_		,   model_name.replace('''-'''		,   '''_'''				) + '''.ckpt'''				)				)






def   a			(						lowerCamelCase_=None				):






				'''simple docstring'''
				lowercase__    =			argparse.ArgumentParser()
				parser.add_argument('''--model_name'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''model name e.g. bert-base-uncased'''				)
				parser.add_argument(
				    '''--cache_dir'''		,   type=lowerCamelCase_		,   default=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory containing pytorch model'''				)
				parser.add_argument('''--pytorch_model_path'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''/path/to/<pytorch-model-name>.bin'''				)
				parser.add_argument('''--tf_cache_dir'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory in which to save tensorflow model'''				)
				lowercase__    =			parser.parse_args(lowerCamelCase_				)

				lowercase__    =			BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name		,   state_dict=torch.load(args.pytorch_model_path				)		,   cache_dir=args.cache_dir		,   )

				convert_pytorch_checkpoint_to_tf(model=lowerCamelCase_		,   ckpt_dir=args.tf_cache_dir		,   model_name=args.model_name				)


if __name__ == "__main__":
							main()




