






'''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      UpperCamelCase (				nn.Module ):







		"""simple docstring"""



		snake_case    =   4_2
		snake_case    =   4_2
		snake_case    =   0.0
		snake_case    =   1
		snake_case    =   1
		snake_case    =   True
		snake_case    =   False
		snake_case    =   False
		snake_case    =   False
		snake_case    =   jnp.floataa
		def       A(  self		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      []
						A	 =      []

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

										A	 =      FlaxResnetBlockaD(
										    in_channels=UpperCAmelCase__						,out_channels=self.out_channels						,dropout_prob=self.dropout						,dtype=self.dtype						,)
										resnets.append(UpperCAmelCase__		)

										A	 =      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(UpperCAmelCase__		)

						A	 =      resnets
						A	 =      attentions

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







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


						'''simple docstring'''
						A	 =      ()

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

						if self.add_downsample:
										A	 =      self.downsamplers_a(UpperCAmelCase__		)
										output_states += (hidden_states,)

						return hidden_states, output_states


class      UpperCamelCase (				nn.Module ):







		"""simple docstring"""



		snake_case    =   4_2
		snake_case    =   4_2
		snake_case    =   0.0
		snake_case    =   1
		snake_case    =   True
		snake_case    =   jnp.floataa
		def       A(  self		:       Dict		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      []

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

										A	 =      FlaxResnetBlockaD(
										    in_channels=UpperCAmelCase__						,out_channels=self.out_channels						,dropout_prob=self.dropout						,dtype=self.dtype						,)
										resnets.append(UpperCAmelCase__		)
						A	 =      resnets

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







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


						'''simple docstring'''
						A	 =      ()

						for resnet in self.resnets:
										A	 =      resnet(UpperCAmelCase__						,UpperCAmelCase__						,deterministic=UpperCAmelCase__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										A	 =      self.downsamplers_a(UpperCAmelCase__		)
										output_states += (hidden_states,)

						return hidden_states, output_states


class      UpperCamelCase (				nn.Module ):







		"""simple docstring"""



		snake_case    =   4_2
		snake_case    =   4_2
		snake_case    =   4_2
		snake_case    =   0.0
		snake_case    =   1
		snake_case    =   1
		snake_case    =   True
		snake_case    =   False
		snake_case    =   False
		snake_case    =   False
		snake_case    =   jnp.floataa
		def       A(  self		:       Dict		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      []
						A	 =      []

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

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

										A	 =      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(UpperCAmelCase__		)

						A	 =      resnets
						A	 =      attentions

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







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


						'''simple docstring'''
						for resnet, attn in zip(self.resnets						,self.attentions		):
										# pop res hidden states
										A	 =      res_hidden_states_tuple[-1]
										A	 =      res_hidden_states_tuple[:-1]
										A	 =      jnp.concatenate((hidden_states, res_hidden_states)						,axis=-1		)

										A	 =      resnet(UpperCAmelCase__						,UpperCAmelCase__						,deterministic=UpperCAmelCase__		)
										A	 =      attn(UpperCAmelCase__						,UpperCAmelCase__						,deterministic=UpperCAmelCase__		)

						if self.add_upsample:
										A	 =      self.upsamplers_a(UpperCAmelCase__		)

						return hidden_states


class      UpperCamelCase (				nn.Module ):







		"""simple docstring"""



		snake_case    =   4_2
		snake_case    =   4_2
		snake_case    =   4_2
		snake_case    =   0.0
		snake_case    =   1
		snake_case    =   True
		snake_case    =   jnp.floataa
		def       A(  self		:       Any		)    ->   Dict:


						'''simple docstring'''
						A	 =      []

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

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

						A	 =      resnets

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







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


						'''simple docstring'''
						for resnet in self.resnets:
										# pop res hidden states
										A	 =      res_hidden_states_tuple[-1]
										A	 =      res_hidden_states_tuple[:-1]
										A	 =      jnp.concatenate((hidden_states, res_hidden_states)						,axis=-1		)

										A	 =      resnet(UpperCAmelCase__						,UpperCAmelCase__						,deterministic=UpperCAmelCase__		)

						if self.add_upsample:
										A	 =      self.upsamplers_a(UpperCAmelCase__		)

						return hidden_states


class      UpperCamelCase (				nn.Module ):







		"""simple docstring"""



		snake_case    =   4_2
		snake_case    =   0.0
		snake_case    =   1
		snake_case    =   1
		snake_case    =   False
		snake_case    =   False
		snake_case    =   jnp.floataa
		def       A(  self		:       str		)    ->   int:


						'''simple docstring'''
						A	 =      [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels						,out_channels=self.in_channels						,dropout_prob=self.dropout						,dtype=self.dtype						,)
						]

						A	 =      []

						for _ in range(self.num_layers		):
										A	 =      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(UpperCAmelCase__		)

										A	 =      FlaxResnetBlockaD(
										    in_channels=self.in_channels						,out_channels=self.in_channels						,dropout_prob=self.dropout						,dtype=self.dtype						,)
										resnets.append(UpperCAmelCase__		)

						A	 =      resnets
						A	 =      attentions







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


						'''simple docstring'''
						A	 =      self.resnets[0](UpperCAmelCase__						,UpperCAmelCase__		)
						for attn, resnet in zip(self.attentions						,self.resnets[1:]		):
										A	 =      attn(UpperCAmelCase__						,UpperCAmelCase__						,deterministic=UpperCAmelCase__		)
										A	 =      resnet(UpperCAmelCase__						,UpperCAmelCase__						,deterministic=UpperCAmelCase__		)

						return hidden_states













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





