'''simple docstring'''

import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      int
		lowercase   :      float       =							0.0
		lowercase   :      int       =							1
		lowercase   :      int       =							1
		lowercase   :      bool       =							True
		lowercase   :      bool       =							False
		lowercase   :      bool       =							False
		lowercase   :      bool       =							False
		lowercase   :      jnp.dtype       =							jnp.floataa






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=[]

								for i in range(self.num_layers				):
														_lowerCAmelCase										=self.in_channels if i == 0 else self.out_channels

														_lowerCAmelCase										=FlaxResnetBlockaD(
														    in_channels=__A		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
														resnets.append(__A				)

														_lowerCAmelCase										=FlaxTransformeraDModel(
														    in_channels=self.out_channels		,						n_heads=self.num_attention_heads		,						d_head=self.out_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						only_cross_attention=self.only_cross_attention		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
														attentions.append(__A				)

								_lowerCAmelCase										=resnets
								_lowerCAmelCase										=attentions

								if self.add_downsample:
														_lowerCAmelCase										=FlaxDownsampleaD(self.out_channels		,						dtype=self.dtype				)






		def __call__(   self		,						__A		,						__A		,						__A		,						__A=True				) ->     Optional[Any]:
								_lowerCAmelCase										=()

								for resnet, attn in zip(self.resnets		,						self.attentions				):
														_lowerCAmelCase										=resnet(__A		,						__A		,						deterministic=__A				)
														_lowerCAmelCase										=attn(__A		,						__A		,						deterministic=__A				)
														output_states += (hidden_states,)

								if self.add_downsample:
														_lowerCAmelCase										=self.downsamplers_a(__A				)
														output_states += (hidden_states,)

								return hidden_states, output_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      int
		lowercase   :      float       =							0.0
		lowercase   :      int       =							1
		lowercase   :      bool       =							True
		lowercase   :      jnp.dtype       =							jnp.floataa






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=[]

								for i in range(self.num_layers				):
														_lowerCAmelCase										=self.in_channels if i == 0 else self.out_channels

														_lowerCAmelCase										=FlaxResnetBlockaD(
														    in_channels=__A		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
														resnets.append(__A				)
								_lowerCAmelCase										=resnets

								if self.add_downsample:
														_lowerCAmelCase										=FlaxDownsampleaD(self.out_channels		,						dtype=self.dtype				)






		def __call__(   self		,						__A		,						__A		,						__A=True				) ->     Optional[int]:
								_lowerCAmelCase										=()

								for resnet in self.resnets:
														_lowerCAmelCase										=resnet(__A		,						__A		,						deterministic=__A				)
														output_states += (hidden_states,)

								if self.add_downsample:
														_lowerCAmelCase										=self.downsamplers_a(__A				)
														output_states += (hidden_states,)

								return hidden_states, output_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      int
		lowercase   :      int
		lowercase   :      float       =							0.0
		lowercase   :      int       =							1
		lowercase   :      int       =							1
		lowercase   :      bool       =							True
		lowercase   :      bool       =							False
		lowercase   :      bool       =							False
		lowercase   :      bool       =							False
		lowercase   :      jnp.dtype       =							jnp.floataa






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=[]

								for i in range(self.num_layers				):
														_lowerCAmelCase										=self.in_channels if (i == self.num_layers - 1) else self.out_channels
														_lowerCAmelCase										=self.prev_output_channel if i == 0 else self.out_channels

														_lowerCAmelCase										=FlaxResnetBlockaD(
														    in_channels=resnet_in_channels + res_skip_channels		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
														resnets.append(__A				)

														_lowerCAmelCase										=FlaxTransformeraDModel(
														    in_channels=self.out_channels		,						n_heads=self.num_attention_heads		,						d_head=self.out_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						only_cross_attention=self.only_cross_attention		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
														attentions.append(__A				)

								_lowerCAmelCase										=resnets
								_lowerCAmelCase										=attentions

								if self.add_upsample:
														_lowerCAmelCase										=FlaxUpsampleaD(self.out_channels		,						dtype=self.dtype				)






		def __call__(   self		,						__A		,						__A		,						__A		,						__A		,						__A=True				) ->     Any:
								for resnet, attn in zip(self.resnets		,						self.attentions				):
														# pop res hidden states
														_lowerCAmelCase										=res_hidden_states_tuple[-1]
														_lowerCAmelCase										=res_hidden_states_tuple[:-1]
														_lowerCAmelCase										=jnp.concatenate((hidden_states, res_hidden_states)		,						axis=-1				)

														_lowerCAmelCase										=resnet(__A		,						__A		,						deterministic=__A				)
														_lowerCAmelCase										=attn(__A		,						__A		,						deterministic=__A				)

								if self.add_upsample:
														_lowerCAmelCase										=self.upsamplers_a(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      int
		lowercase   :      int
		lowercase   :      float       =							0.0
		lowercase   :      int       =							1
		lowercase   :      bool       =							True
		lowercase   :      jnp.dtype       =							jnp.floataa






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=[]

								for i in range(self.num_layers				):
														_lowerCAmelCase										=self.in_channels if (i == self.num_layers - 1) else self.out_channels
														_lowerCAmelCase										=self.prev_output_channel if i == 0 else self.out_channels

														_lowerCAmelCase										=FlaxResnetBlockaD(
														    in_channels=resnet_in_channels + res_skip_channels		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
														resnets.append(__A				)

								_lowerCAmelCase										=resnets

								if self.add_upsample:
														_lowerCAmelCase										=FlaxUpsampleaD(self.out_channels		,						dtype=self.dtype				)






		def __call__(   self		,						__A		,						__A		,						__A		,						__A=True				) ->     Any:
								for resnet in self.resnets:
														# pop res hidden states
														_lowerCAmelCase										=res_hidden_states_tuple[-1]
														_lowerCAmelCase										=res_hidden_states_tuple[:-1]
														_lowerCAmelCase										=jnp.concatenate((hidden_states, res_hidden_states)		,						axis=-1				)

														_lowerCAmelCase										=resnet(__A		,						__A		,						deterministic=__A				)

								if self.add_upsample:
														_lowerCAmelCase										=self.upsamplers_a(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      float       =							0.0
		lowercase   :      int       =							1
		lowercase   :      int       =							1
		lowercase   :      bool       =							False
		lowercase   :      bool       =							False
		lowercase   :      jnp.dtype       =							jnp.floataa






		def 		UpperCamelCase__       (   self				) ->     int:
								# there is always at least one resnet
								_lowerCAmelCase										=[
								    FlaxResnetBlockaD(
								        in_channels=self.in_channels		,						out_channels=self.in_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
								]

								_lowerCAmelCase										=[]

								for _ in range(self.num_layers				):
														_lowerCAmelCase										=FlaxTransformeraDModel(
														    in_channels=self.in_channels		,						n_heads=self.num_attention_heads		,						d_head=self.in_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
														attentions.append(__A				)

														_lowerCAmelCase										=FlaxResnetBlockaD(
														    in_channels=self.in_channels		,						out_channels=self.in_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
														resnets.append(__A				)

								_lowerCAmelCase										=resnets
								_lowerCAmelCase										=attentions






		def __call__(   self		,						__A		,						__A		,						__A		,						__A=True				) ->     int:
								_lowerCAmelCase										=self.resnets[0](__A		,						__A				)
								for attn, resnet in zip(self.attentions		,						self.resnets[1:]				):
														_lowerCAmelCase										=attn(__A		,						__A		,						deterministic=__A				)
														_lowerCAmelCase										=resnet(__A		,						__A		,						deterministic=__A				)

								return hidden_states






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).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'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).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'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    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
																														_lowerCAmelCase										=(
																														    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
																														_lowerCAmelCase										=(
																														    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
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= 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''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





