




'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							[
							    '''encoder.version''',
							    '''decoder.version''',
							    '''model.encoder.version''',
							    '''model.decoder.version''',
							    '''_float_tensor''',
							    '''decoder.output_projection.weight''',
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,		UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    Dict ):





							'''simple docstring'''


							_a	,				_a      	=							emb.weight.shape
							_a      	=							nn.Linear(UpperCamelCase					,		UpperCamelCase					,		bias=UpperCamelCase )
							_a      	=							emb.weight.data
							return lin_layer
def 						snake_case_       (UpperCamelCase			:    Tuple					,		UpperCamelCase			:    Any="facebook/mbart-large-en-ro"					,		UpperCamelCase			:    Optional[Any]=False					,		UpperCamelCase			:    Any=False ):





							'''simple docstring'''


							_a      	=							torch.load(UpperCamelCase					,		map_location='''cpu''' )['''model''']
							remove_ignore_keys_(UpperCamelCase )
							_a      	=							state_dict['''encoder.embed_tokens.weight'''].shape[0]

							_a      	=							MBartConfig.from_pretrained(UpperCamelCase					,		vocab_size=UpperCamelCase )
							if mbart_aa and finetuned:
														_a      	=							'''relu'''

							_a      	=							state_dict['''decoder.embed_tokens.weight''']
							_a      	=							MBartForConditionalGeneration(UpperCamelCase )
							model.model.load_state_dict(UpperCamelCase )

							if finetuned:
														_a      	=							make_linear_from_emb(model.model.shared )

							return model


if __name__ == "__main__":
				_snake_case : str  				=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
				parser.add_argument(
				    '--hf_config',
				    default='facebook/mbart-large-cc25',
				    type=str,
				    help='Which huggingface architecture to use: mbart-large',
				)
				parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
				parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
				_snake_case : Union[str, Any]  				=						parser.parse_args()
				_snake_case : Optional[int]  				=						convert_fairseq_mbart_checkpoint_from_disk(
				    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
				)
				model.save_pretrained(args.pytorch_dump_folder_path)









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

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				Optional[Any]   =      []
						_A	:				str   =      []

						for i in range(self.num_layers		):
										_A	:				Union[str, Any]   =      self.in_channels if i == 0 else self.out_channels

										_A	:				str   =      FlaxResnetBlockaD(
										    in_channels=A__       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

										_A	:				List[str]   =      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__		)

						_A	:				List[str]   =      resnets
						_A	:				Any   =      attentions

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







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						_A	:				List[str]   =      ()

						for resnet, attn in zip(self.resnets       ,self.attentions		):
										_A	:				Optional[Any]   =      resnet(A__       ,A__       ,deterministic=A__		)
										_A	:				Tuple   =      attn(A__       ,A__       ,deterministic=A__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										_A	:				Optional[int]   =      self.downsamplers_a(A__		)
										output_states += (hidden_states,)

						return hidden_states, output_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				List[Any]   =      []

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

										_A	:				List[Any]   =      FlaxResnetBlockaD(
										    in_channels=A__       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)
						_A	:				Tuple   =      resnets

						if self.add_downsample:
										_A	:				Optional[Any]   =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__=True		):
						_A	:				List[Any]   =      ()

						for resnet in self.resnets:
										_A	:				Optional[int]   =      resnet(A__       ,A__       ,deterministic=A__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										_A	:				List[Any]   =      self.downsamplers_a(A__		)
										output_states += (hidden_states,)

						return hidden_states, output_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				List[str]   =      []
						_A	:				Optional[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				Dict   =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										_A	:				List[str]   =      self.prev_output_channel if i == 0 else self.out_channels

										_A	:				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(A__		)

										_A	:				Union[str, Any]   =      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__		)

						_A	:				Dict   =      resnets
						_A	:				int   =      attentions

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







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

										_A	:				str   =      resnet(A__       ,A__       ,deterministic=A__		)
										_A	:				Optional[int]   =      attn(A__       ,A__       ,deterministic=A__		)

						if self.add_upsample:
										_A	:				Union[str, Any]   =      self.upsamplers_a(A__		)

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				Optional[Any]   =      []

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

										_A	:				List[Any]   =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

						_A	:				int   =      resnets

						if self.add_upsample:
										_A	:				List[str]   =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						for resnet in self.resnets:
										# pop res hidden states
										_A	:				Tuple   =      res_hidden_states_tuple[-1]
										_A	:				Optional[int]   =      res_hidden_states_tuple[:-1]
										_A	:				Any   =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1		)

										_A	:				Optional[Any]   =      resnet(A__       ,A__       ,deterministic=A__		)

						if self.add_upsample:
										_A	:				Tuple   =      self.upsamplers_a(A__		)

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						# there is always at least one resnet
						_A	:				List[str]   =      [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
						]

						_A	:				Union[str, Any]   =      []

						for _ in range(self.num_layers		):
										_A	:				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(A__		)

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

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







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						_A	:				Optional[int]   =      self.resnets[0](A__       ,A__		)
						for attn, resnet in zip(self.attentions       ,self.resnets[1:]		):
										_A	:				Dict   =      attn(A__       ,A__       ,deterministic=A__		)
										_A	:				Any   =      resnet(A__       ,A__       ,deterministic=A__		)

						return hidden_states



