



"""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     A__     (						nn.Module							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			0.0
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			True
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			jnp.floataa

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						int				= []
    __lowerCAmelCase   :						List[str]				= []

    for i in range(self.num_layers):
       __lowerCAmelCase   :						List[Any]				= self.in_channels if i == 0 else self.out_channels

       __lowerCAmelCase   :						Any				= FlaxResnetBlockaD(
           in_channels=_SCREAMING_SNAKE_CASE ,    out_channels=self.out_channels ,    dropout_prob=self.dropout ,    dtype=self.dtype ,    )
       resnets.append(_SCREAMING_SNAKE_CASE)

       __lowerCAmelCase   :						int				= 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(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						List[str]				= resnets
    __lowerCAmelCase   :						str				= attentions

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







 def __call__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	int=True)       -> List[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= ()

    for resnet, attn in zip(self.resnets ,    self.attentions):
       __lowerCAmelCase   :						Dict				= resnet(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)
       __lowerCAmelCase   :						int				= attn(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)
       output_states += (hidden_states,)

    if self.add_downsample:
       __lowerCAmelCase   :						str				= self.downsamplers_a(_SCREAMING_SNAKE_CASE)
       output_states += (hidden_states,)

    return hidden_states, output_states






class     A__     (						nn.Module							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			0.0
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			True
 SCREAMING_SNAKE_CASE					   =			jnp.floataa

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[str]:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= []

    for i in range(self.num_layers):
       __lowerCAmelCase   :						Optional[Any]				= self.in_channels if i == 0 else self.out_channels

       __lowerCAmelCase   :						int				= FlaxResnetBlockaD(
           in_channels=_SCREAMING_SNAKE_CASE ,    out_channels=self.out_channels ,    dropout_prob=self.dropout ,    dtype=self.dtype ,    )
       resnets.append(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Optional[Any]				= resnets

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







 def __call__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=True)       -> List[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						int				= ()

    for resnet in self.resnets:
       __lowerCAmelCase   :						int				= resnet(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)
       output_states += (hidden_states,)

    if self.add_downsample:
       __lowerCAmelCase   :						Tuple				= self.downsamplers_a(_SCREAMING_SNAKE_CASE)
       output_states += (hidden_states,)

    return hidden_states, output_states






class     A__     (						nn.Module							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			0.0
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			True
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			jnp.floataa

 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						List[str]				= []
    __lowerCAmelCase   :						List[str]				= []

    for i in range(self.num_layers):
       __lowerCAmelCase   :						Optional[Any]				= self.in_channels if (i == self.num_layers - 1) else self.out_channels
       __lowerCAmelCase   :						List[Any]				= self.prev_output_channel if i == 0 else self.out_channels

       __lowerCAmelCase   :						List[str]				= FlaxResnetBlockaD(
           in_channels=resnet_in_channels + res_skip_channels ,    out_channels=self.out_channels ,    dropout_prob=self.dropout ,    dtype=self.dtype ,    )
       resnets.append(_SCREAMING_SNAKE_CASE)

       __lowerCAmelCase   :						int				= 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(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						List[str]				= resnets
    __lowerCAmelCase   :						Optional[int]				= attentions

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







 def __call__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True)       -> Union[str, Any]:

    """simple docstring"""

    for resnet, attn in zip(self.resnets ,    self.attentions):
       # pop res hidden states
       __lowerCAmelCase   :						str				= res_hidden_states_tuple[-1]
       __lowerCAmelCase   :						List[Any]				= res_hidden_states_tuple[:-1]
       __lowerCAmelCase   :						Tuple				= jnp.concatenate((hidden_states, res_hidden_states) ,    axis=-1)

       __lowerCAmelCase   :						str				= resnet(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)
       __lowerCAmelCase   :						Any				= attn(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)

    if self.add_upsample:
       __lowerCAmelCase   :						List[str]				= self.upsamplers_a(_SCREAMING_SNAKE_CASE)

    return hidden_states






class     A__     (						nn.Module							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			0.0
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			True
 SCREAMING_SNAKE_CASE					   =			jnp.floataa

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= []

    for i in range(self.num_layers):
       __lowerCAmelCase   :						str				= self.in_channels if (i == self.num_layers - 1) else self.out_channels
       __lowerCAmelCase   :						Optional[int]				= self.prev_output_channel if i == 0 else self.out_channels

       __lowerCAmelCase   :						Optional[int]				= FlaxResnetBlockaD(
           in_channels=resnet_in_channels + res_skip_channels ,    out_channels=self.out_channels ,    dropout_prob=self.dropout ,    dtype=self.dtype ,    )
       resnets.append(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						List[str]				= resnets

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







 def __call__(  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Any=True)       -> int:

    """simple docstring"""

    for resnet in self.resnets:
       # pop res hidden states
       __lowerCAmelCase   :						Optional[Any]				= res_hidden_states_tuple[-1]
       __lowerCAmelCase   :						Dict				= res_hidden_states_tuple[:-1]
       __lowerCAmelCase   :						List[Any]				= jnp.concatenate((hidden_states, res_hidden_states) ,    axis=-1)

       __lowerCAmelCase   :						Any				= resnet(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)

    if self.add_upsample:
       __lowerCAmelCase   :						Tuple				= self.upsamplers_a(_SCREAMING_SNAKE_CASE)

    return hidden_states






class     A__     (						nn.Module							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			42
 SCREAMING_SNAKE_CASE					   =			0.0
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			1
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			jnp.floataa

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= [
        FlaxResnetBlockaD(
            in_channels=self.in_channels ,    out_channels=self.in_channels ,    dropout_prob=self.dropout ,    dtype=self.dtype ,    )
    ]

    __lowerCAmelCase   :						Union[str, Any]				= []

    for _ in range(self.num_layers):
       __lowerCAmelCase   :						List[Any]				= 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(_SCREAMING_SNAKE_CASE)

       __lowerCAmelCase   :						Optional[int]				= FlaxResnetBlockaD(
           in_channels=self.in_channels ,    out_channels=self.in_channels ,    dropout_prob=self.dropout ,    dtype=self.dtype ,    )
       resnets.append(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						List[str]				= resnets
    __lowerCAmelCase   :						List[str]				= attentions







 def __call__(  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Dict=True)       -> Optional[int]:

    """simple docstring"""

    __lowerCAmelCase   :						Dict				= self.resnets[0](_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
    for attn, resnet in zip(self.attentions ,    self.resnets[1:]):
       __lowerCAmelCase   :						Union[str, Any]				= attn(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)
       __lowerCAmelCase   :						Any				= resnet(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    deterministic=_SCREAMING_SNAKE_CASE)

    return hidden_states




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)