

from typing import Any, Dict, Optional

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

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_						,					A_						,					A_=0.0						,					A_ = None						,					A_ = "geglu"						,					A_ = None						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = True						,					A_ = "layer_norm"						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		int 		=    only_cross_attention

										_UpperCAmelCase       :		str 		=    (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
										_UpperCAmelCase       :		List[Any] 		=    (num_embeds_ada_norm is not None) and norm_type == "ada_norm"

										if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
														raise ValueError(
														    f'`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'
														    f' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'	)

										# Define 3 blocks. Each block has its own normalization layer.
										# 1. Self-Attn
										if self.use_ada_layer_norm:
														_UpperCAmelCase       :		Tuple 		=    AdaLayerNorm(A_						,					A_	)
										elif self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		List[str] 		=    AdaLayerNormZero(A_						,					A_	)
										else:
														_UpperCAmelCase       :		int 		=    nn.LayerNorm(A_						,					elementwise_affine=A_	)
										_UpperCAmelCase       :		Tuple 		=    Attention(
										    query_dim=A_						,					heads=A_						,					dim_head=A_						,					dropout=A_						,					bias=A_						,					cross_attention_dim=cross_attention_dim if only_cross_attention else None						,					upcast_attention=A_						,					)

										# 2. Cross-Attn
										if cross_attention_dim is not None or double_self_attention:
														# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
														# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
														# the second cross attention block.
														_UpperCAmelCase       :		Optional[Any] 		=    (
														    AdaLayerNorm(A_						,					A_	)
														    if self.use_ada_layer_norm
														    else nn.LayerNorm(A_						,					elementwise_affine=A_	)
														)
														_UpperCAmelCase       :		Tuple 		=    Attention(
														    query_dim=A_						,					cross_attention_dim=cross_attention_dim if not double_self_attention else None						,					heads=A_						,					dim_head=A_						,					dropout=A_						,					bias=A_						,					upcast_attention=A_						,					)  # is self-attn if encoder_hidden_states is none
										else:
														_UpperCAmelCase       :		Dict 		=    None
														_UpperCAmelCase       :		Tuple 		=    None

										# 3. Feed-forward
										_UpperCAmelCase       :		Tuple 		=    nn.LayerNorm(A_						,					elementwise_affine=A_	)
										_UpperCAmelCase       :		List[Any] 		=    FeedForward(A_						,					dropout=A_						,					activation_fn=A_						,					final_dropout=A_	)

										# let chunk size default to None
										_UpperCAmelCase       :		str 		=    None
										_UpperCAmelCase       :		Union[str, Any] 		=    0


						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    chunk_size
										_UpperCAmelCase       :		int 		=    dim





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					):







										'''simple docstring'''


										if self.use_ada_layer_norm:
														_UpperCAmelCase       :		Optional[Any] 		=    self.norma(A_						,					A_	)
										elif self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		int 		=    self.norma(
														    A_						,					A_						,					A_						,					hidden_dtype=hidden_states.dtype	)
										else:
														_UpperCAmelCase       :		str 		=    self.norma(A_	)

										_UpperCAmelCase       :		Any 		=    cross_attention_kwargs if cross_attention_kwargs is not None else {}

										_UpperCAmelCase       :		Union[str, Any] 		=    self.attna(
										    A_						,					encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None						,					attention_mask=A_						,					**A_						,					)
										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		Optional[int] 		=    gate_msa.unsqueeze(1	) * attn_output
										_UpperCAmelCase       :		Tuple 		=    attn_output + hidden_states

										# 2. Cross-Attention
										if self.attna is not None:
														_UpperCAmelCase       :		int 		=    (
														    self.norma(A_						,					A_	) if self.use_ada_layer_norm else self.norma(A_	)
														)

														_UpperCAmelCase       :		List[str] 		=    self.attna(
														    A_						,					encoder_hidden_states=A_						,					attention_mask=A_						,					**A_						,					)
														_UpperCAmelCase       :		List[Any] 		=    attn_output + hidden_states

										# 3. Feed-forward
										_UpperCAmelCase       :		List[Any] 		=    self.norma(A_	)

										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		List[Any] 		=    norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

										if self._chunk_size is not None:
														# "feed_forward_chunk_size" can be used to save memory
														if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																		raise ValueError(
																		    f'`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'	)

														_UpperCAmelCase       :		Optional[Any] 		=    norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
														_UpperCAmelCase       :		Optional[Any] 		=    torch.cat(
														    [self.ff(A_	) for hid_slice in norm_hidden_states.chunk(A_						,					dim=self._chunk_dim	)]						,					dim=self._chunk_dim						,					)
										else:
														_UpperCAmelCase       :		Dict 		=    self.ff(A_	)

										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		List[str] 		=    gate_mlp.unsqueeze(1	) * ff_output

										_UpperCAmelCase       :		Optional[int] 		=    ff_output + hidden_states

										return hidden_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_ = None						,					A_ = 4						,					A_ = 0.0						,					A_ = "geglu"						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		str 		=    int(dim * mult	)
										_UpperCAmelCase       :		Union[str, Any] 		=    dim_out if dim_out is not None else dim

										if activation_fn == "gelu":
														_UpperCAmelCase       :		List[Any] 		=    GELU(A_						,					A_	)
										if activation_fn == "gelu-approximate":
														_UpperCAmelCase       :		Tuple 		=    GELU(A_						,					A_						,					approximate="tanh"	)
										elif activation_fn == "geglu":
														_UpperCAmelCase       :		List[str] 		=    GEGLU(A_						,					A_	)
										elif activation_fn == "geglu-approximate":
														_UpperCAmelCase       :		Tuple 		=    ApproximateGELU(A_						,					A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    nn.ModuleList([]	)
										# project in
										self.net.append(A_	)
										# project dropout
										self.net.append(nn.Dropout(A_	)	)
										# project out
										self.net.append(nn.Linear(A_						,					A_	)	)
										# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
										if final_dropout:
														self.net.append(nn.Dropout(A_	)	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										for module in self.net:
														_UpperCAmelCase       :		Dict 		=    module(A_	)
										return hidden_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_						,					A_ = "none"	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Optional[int] 		=    nn.Linear(A_						,					A_	)
										_UpperCAmelCase       :		List[str] 		=    approximate


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if gate.device.type != "mps":
														return F.gelu(A_						,					approximate=self.approximate	)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa	)						,					approximate=self.approximate	).to(dtype=gate.dtype	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.proj(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.gelu(A_	)
										return hidden_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Dict 		=    nn.Linear(A_						,					dim_out * 2	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if gate.device.type != "mps":
														return F.gelu(A_	)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa	)	).to(dtype=gate.dtype	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.proj(A_	).chunk(2						,					dim=-1	)
										return hidden_states * self.gelu(A_	)







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		str 		=    nn.Linear(A_						,					A_	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.proj(A_	)
										return x * torch.sigmoid(1.7_02 * x	)







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		List[Any] 		=    nn.Embedding(A_						,					A_	)
										_UpperCAmelCase       :		List[str] 		=    nn.SiLU()
										_UpperCAmelCase       :		List[Any] 		=    nn.Linear(A_						,					embedding_dim * 2	)
										_UpperCAmelCase       :		Optional[int] 		=    nn.LayerNorm(A_						,					elementwise_affine=A_	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.linear(self.silu(self.emb(A_	)	)	)
										_UpperCAmelCase       :		List[str] 		=    torch.chunk(A_						,					2	)
										_UpperCAmelCase       :		Dict 		=    self.norm(A_	) * (1 + scale) + shift
										return x







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase       :		Tuple 		=    CombinedTimestepLabelEmbeddings(A_						,					A_	)

										_UpperCAmelCase       :		List[Any] 		=    nn.SiLU()
										_UpperCAmelCase       :		Tuple 		=    nn.Linear(A_						,					6 * embedding_dim						,					bias=A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    nn.LayerNorm(A_						,					elementwise_affine=A_						,					eps=1e-6	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.linear(self.silu(self.emb(A_						,					A_						,					hidden_dtype=A_	)	)	)
										_UpperCAmelCase       :		Optional[int] 		=    emb.chunk(6						,					dim=1	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.norm(A_	) * (1 + scale_msa[:, None]) + shift_msa[:, None]
										return x, gate_msa, shift_mlp, scale_mlp, gate_mlp







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_						,					A_						,					A_ = None						,					A_ = 1e-5	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		List[Any] 		=    num_groups
										_UpperCAmelCase       :		Optional[Any] 		=    eps

										if act_fn is None:
														_UpperCAmelCase       :		Union[str, Any] 		=    None
										else:
														_UpperCAmelCase       :		int 		=    get_activation(A_	)

										_UpperCAmelCase       :		str 		=    nn.Linear(A_						,					out_dim * 2	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										if self.act:
														_UpperCAmelCase       :		Tuple 		=    self.act(A_	)
										_UpperCAmelCase       :		Any 		=    self.linear(A_	)
										_UpperCAmelCase       :		Tuple 		=    emb[:, :, None, None]
										_UpperCAmelCase       :		Tuple 		=    emb.chunk(2						,					dim=1	)

										_UpperCAmelCase       :		List[str] 		=    F.group_norm(A_						,					self.num_groups						,					eps=self.eps	)
										_UpperCAmelCase       :		Union[str, Any] 		=    x * (1 + scale) + shift
										return x





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[Any] 		=    os.path.join(args.tf_model_dir  ,      "parameters.json" )
				_UpperCAmelCase       :		List[str] 		=    json.loads(open(lowerCAmelCase ).read() )
				if not params:
								raise ValueError(
								    F'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.' )
				if not args.output.endswith(".pt" ):
								_UpperCAmelCase       :		Union[str, Any] 		=    args.output + ".pt"
				_UpperCAmelCase       :		Dict 		=    OrderedDict()
				with tf.device("/CPU:0" ):
								_UpperCAmelCase       :		int 		=    tf.train.load_checkpoint(args.tf_model_dir )
								_UpperCAmelCase       :		int 		=    reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_UpperCAmelCase       :		Dict 		=    reader.get_tensor(lowerCAmelCase ).astype(np.floataa )
												if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
																continue
												if key_name.startswith("pasts/" ):
																if key_name.startswith("pasts/mlp" ):
																				_UpperCAmelCase       :		int 		=    int(key_name[9] )
																elif key_name.startswith("pasts/out" ):
																				_UpperCAmelCase       :		Any 		=    8
																_UpperCAmelCase       :		str 		=    "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/moe" ):
																_UpperCAmelCase       :		List[str] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/switch_gating/kernel" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/softmlp/kernel" ):
																				_UpperCAmelCase       :		List[str] 		=    "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
																				_UpperCAmelCase       :		str 		=    key_name[-9:-7]
																				for i in range(16 ):
																								_UpperCAmelCase       :		Optional[int] 		=    "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_UpperCAmelCase       :		Optional[int] 		=    (
																								    vnp[i].transpose([1, 0] ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/mlp" ):
																_UpperCAmelCase       :		Tuple 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/p1/kernel" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p1/bias" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_UpperCAmelCase       :		Tuple 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/kernel" ):
																				_UpperCAmelCase       :		str 		=    "model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/bias" ):
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/ln" ):
																_UpperCAmelCase       :		Union[str, Any] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.norm.bias" % player
																				_UpperCAmelCase       :		int 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.norm.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/att" ):
																_UpperCAmelCase       :		Optional[Any] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/qkv/kernel" ):
																				_UpperCAmelCase       :		List[Any] 		=    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_UpperCAmelCase       :		Dict 		=    state[:, 0, :, :]
																				_UpperCAmelCase       :		List[Any] 		=    state[:, 1, :, :]
																				_UpperCAmelCase       :		Tuple 		=    state[:, 2, :, :]
																				_UpperCAmelCase       :		List[str] 		=    (
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		int 		=    (
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    (
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		List[Any] 		=    "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/o/kernel" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_UpperCAmelCase       :		Tuple 		=    (
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/an" ):
																_UpperCAmelCase       :		Optional[int] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.self_attn.norm.bias" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.norm.weight" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
												elif (
												    key_name.startswith("model/wte" )
												    or key_name.startswith("model/wpe" )
												    or key_name.startswith("model/ete" )
												):
																_UpperCAmelCase       :		Optional[Any] 		=    {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_UpperCAmelCase       :		Dict 		=    "model.%s.weight" % nlayer
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																if key_name.startswith("model/wte" ):
																				_UpperCAmelCase       :		List[str] 		=    "lm_head.weight"
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same in embedded
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/wob" ):
																_UpperCAmelCase       :		Dict 		=    "final_logits_bias"
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		str 		=    state.reshape((1, -1) )
																_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense/kernel":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.weight"
																_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense_1/bias":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.bias"
																_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
				torch.save(lowerCAmelCase  ,      args.output )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser(
					    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
					)
					parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
					parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_tf_gptsan_to_pt(args)


