
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					int=1					):


				'''simple docstring'''





				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('''.'''					)[n_shave_prefix_segments:]					)
				else:
								return ".".join(path.split('''.'''					)[:n_shave_prefix_segments]					)




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					str=0					):


				'''simple docstring'''
				lowercase_       =					[]
				for old_item in old_list:
								lowercase_       =					old_item.replace('''in_layers.0'''						,   '''norm1'''					)
								lowercase_       =					new_item.replace('''in_layers.2'''						,   '''conv1'''					)

								lowercase_       =					new_item.replace('''out_layers.0'''						,   '''norm2'''					)
								lowercase_       =					new_item.replace('''out_layers.3'''						,   '''conv2'''					)

								lowercase_       =					new_item.replace('''emb_layers.1'''						,   '''time_emb_proj'''					)
								lowercase_       =					new_item.replace('''skip_connection'''						,   '''conv_shortcut'''					)

								lowercase_       =					shave_segments(UpperCAmelCase_						,   n_shave_prefix_segments=UpperCAmelCase_					)

								mapping.append({'''old''': old_item, '''new''': new_item}					)

				return mapping




def 				a						(							snake_case__:					Tuple						,   snake_case__:					Optional[int]=0					):


				'''simple docstring'''
				lowercase_       =					[]
				for old_item in old_list:
								lowercase_       =					old_item

								lowercase_       =					new_item.replace('''norm.weight'''						,   '''group_norm.weight'''					)
								lowercase_       =					new_item.replace('''norm.bias'''						,   '''group_norm.bias'''					)

								lowercase_       =					new_item.replace('''proj_out.weight'''						,   '''proj_attn.weight'''					)
								lowercase_       =					new_item.replace('''proj_out.bias'''						,   '''proj_attn.bias'''					)

								lowercase_       =					shave_segments(UpperCAmelCase_						,   n_shave_prefix_segments=UpperCAmelCase_					)

								mapping.append({'''old''': old_item, '''new''': new_item}					)

				return mapping




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Any						,   snake_case__:					Tuple						,   snake_case__:					Dict=None						,   snake_case__:					List[Any]=None						,   snake_case__:					Optional[Any]=None					):


				'''simple docstring'''
				assert isinstance(UpperCAmelCase_						,   UpperCAmelCase_					), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												lowercase_       =					old_checkpoint[path]
												lowercase_       =					old_tensor.shape[0] // 3

												lowercase_       =					(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

												lowercase_       =					old_tensor.shape[0] // config['''num_head_channels'''] // 3

												lowercase_       =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
												lowercase_       =					old_tensor.split(channels // num_heads						,   dim=1					)

												lowercase_       =					query.reshape(UpperCAmelCase_					)
												lowercase_       =					key.reshape(UpperCAmelCase_					)
												lowercase_       =					value.reshape(UpperCAmelCase_					)

				for path in paths:
								lowercase_       =					path['''new''']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								lowercase_       =					new_path.replace('''middle_block.0'''						,   '''mid_block.resnets.0'''					)
								lowercase_       =					new_path.replace('''middle_block.1'''						,   '''mid_block.attentions.0'''					)
								lowercase_       =					new_path.replace('''middle_block.2'''						,   '''mid_block.resnets.1'''					)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowercase_       =					new_path.replace(replacement['''old''']						,   replacement['''new''']					)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												lowercase_       =					old_checkpoint[path['''old''']][:, :, 0]
								else:
												lowercase_       =					old_checkpoint[path['''old''']]




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					Dict					):


				'''simple docstring'''
				lowercase_       =					{}

				lowercase_       =					checkpoint['''time_embed.0.weight''']
				lowercase_       =					checkpoint['''time_embed.0.bias''']
				lowercase_       =					checkpoint['''time_embed.2.weight''']
				lowercase_       =					checkpoint['''time_embed.2.bias''']

				lowercase_       =					checkpoint['''input_blocks.0.0.weight''']
				lowercase_       =					checkpoint['''input_blocks.0.0.bias''']

				lowercase_       =					checkpoint['''out.0.weight''']
				lowercase_       =					checkpoint['''out.0.bias''']
				lowercase_       =					checkpoint['''out.2.weight''']
				lowercase_       =					checkpoint['''out.2.bias''']

				# Retrieves the keys for the input blocks only
				lowercase_       =					len({'''.'''.join(layer.split('''.'''					)[:2]					) for layer in checkpoint if '''input_blocks''' in layer}					)
				lowercase_       =					{
				    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(UpperCAmelCase_					)
				}

				# Retrieves the keys for the middle blocks only
				lowercase_       =					len({'''.'''.join(layer.split('''.'''					)[:2]					) for layer in checkpoint if '''middle_block''' in layer}					)
				lowercase_       =					{
				    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
				    for layer_id in range(UpperCAmelCase_					)
				}

				# Retrieves the keys for the output blocks only
				lowercase_       =					len({'''.'''.join(layer.split('''.'''					)[:2]					) for layer in checkpoint if '''output_blocks''' in layer}					)
				lowercase_       =					{
				    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(UpperCAmelCase_					)
				}

				for i in range(1						,   UpperCAmelCase_					):
								lowercase_       =					(i - 1) // (config['''num_res_blocks'''] + 1)
								lowercase_       =					(i - 1) % (config['''num_res_blocks'''] + 1)

								lowercase_       =					[key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
								lowercase_       =					[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

								if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
												lowercase_       =					checkpoint[
												    F'''input_blocks.{i}.0.op.weight'''
												]
												lowercase_       =					checkpoint[
												    F'''input_blocks.{i}.0.op.bias'''
												]
												continue

								lowercase_       =					renew_resnet_paths(UpperCAmelCase_					)
								lowercase_       =					{'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
								lowercase_       =					{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
								assign_to_checkpoint(
								    UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   additional_replacements=[meta_path, resnet_op]						,   config=UpperCAmelCase_					)

								if len(UpperCAmelCase_					):
												lowercase_       =					renew_attention_paths(UpperCAmelCase_					)
												lowercase_       =					{
												    '''old''': F'''input_blocks.{i}.1''',
												    '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												lowercase_       =					{
												    F'''input_blocks.{i}.1.qkv.bias''': {
												        '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    F'''input_blocks.{i}.1.qkv.weight''': {
												        '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   additional_replacements=[meta_path]						,   attention_paths_to_split=UpperCAmelCase_						,   config=UpperCAmelCase_						,   )

				lowercase_       =					middle_blocks[0]
				lowercase_       =					middle_blocks[1]
				lowercase_       =					middle_blocks[2]

				lowercase_       =					renew_resnet_paths(UpperCAmelCase_					)
				assign_to_checkpoint(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   config=UpperCAmelCase_					)

				lowercase_       =					renew_resnet_paths(UpperCAmelCase_					)
				assign_to_checkpoint(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   config=UpperCAmelCase_					)

				lowercase_       =					renew_attention_paths(UpperCAmelCase_					)
				lowercase_       =					{
				    '''middle_block.1.qkv.bias''': {
				        '''key''': '''mid_block.attentions.0.key.bias''',
				        '''query''': '''mid_block.attentions.0.query.bias''',
				        '''value''': '''mid_block.attentions.0.value.bias''',
				    },
				    '''middle_block.1.qkv.weight''': {
				        '''key''': '''mid_block.attentions.0.key.weight''',
				        '''query''': '''mid_block.attentions.0.query.weight''',
				        '''value''': '''mid_block.attentions.0.value.weight''',
				    },
				}
				assign_to_checkpoint(
				    UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   attention_paths_to_split=UpperCAmelCase_						,   config=UpperCAmelCase_					)

				for i in range(UpperCAmelCase_					):
								lowercase_       =					i // (config['''num_res_blocks'''] + 1)
								lowercase_       =					i % (config['''num_res_blocks'''] + 1)
								lowercase_       =					[shave_segments(UpperCAmelCase_						,   2					) for name in output_blocks[i]]
								lowercase_       =					{}

								for layer in output_block_layers:
												lowercase_       =					layer.split('''.'''					)[0], shave_segments(UpperCAmelCase_						,   1					)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(UpperCAmelCase_					)
												else:
																lowercase_       =					[layer_name]

								if len(UpperCAmelCase_					) > 1:
												lowercase_       =					[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
												lowercase_       =					[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

												lowercase_       =					renew_resnet_paths(UpperCAmelCase_					)
												lowercase_       =					renew_resnet_paths(UpperCAmelCase_					)

												lowercase_       =					{'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   additional_replacements=[meta_path]						,   config=UpperCAmelCase_					)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																lowercase_       =					list(output_block_list.values()					).index(['''conv.weight''', '''conv.bias''']					)
																lowercase_       =					checkpoint[
																    F'''output_blocks.{i}.{index}.conv.weight'''
																]
																lowercase_       =					checkpoint[
																    F'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(UpperCAmelCase_					) == 2:
																				lowercase_       =					[]

												if len(UpperCAmelCase_					):
																lowercase_       =					renew_attention_paths(UpperCAmelCase_					)
																lowercase_       =					{
																    '''old''': F'''output_blocks.{i}.1''',
																    '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																lowercase_       =					{
																    F'''output_blocks.{i}.1.qkv.bias''': {
																        '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																        '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																        '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																    },
																    F'''output_blocks.{i}.1.qkv.weight''': {
																        '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																        '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																        '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																    },
																}
																assign_to_checkpoint(
																    UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   additional_replacements=[meta_path]						,   attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions					) else None						,   config=UpperCAmelCase_						,   )
								else:
												lowercase_       =					renew_resnet_paths(UpperCAmelCase_						,   n_shave_prefix_segments=1					)
												for path in resnet_0_paths:
																lowercase_       =					'''.'''.join(['''output_blocks''', str(UpperCAmelCase_					), path['''old''']]					)
																lowercase_       =					'''.'''.join(['''up_blocks''', str(UpperCAmelCase_					), '''resnets''', str(UpperCAmelCase_					), path['''new''']]					)

																lowercase_       =					checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()

						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
						)

						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The config json file corresponding to the architecture.',
						)

						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

						__a  			=					parser.parse_args()

						__a  			=					torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												__a  			=					json.loads(f.read())

						__a  			=					convert_ldm_checkpoint(checkpoint, config)

						if "ldm" in config:
												del config["ldm"]

						__a  			=					UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

						try:
												__a  			=					DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
												__a  			=					VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

												__a  			=					LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
												pipe.save_pretrained(args.dump_path)
						except:  # noqa: E722
												model.save_pretrained(args.dump_path)








import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      if split_mlp_wi:
            a						:int 							=       params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            a						:Dict 							=       (wi_a, wi_a)
      else:
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.join(UpperCAmelCase_						): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      a						:Any 							=       '''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' ,  UpperCAmelCase_						)

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_						):
            # Block i, layer 0 (Self Attention).
            a						:Optional[Any] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_attention_layer_norm'''						)
            a							,				a							,				a							,				a						:Optional[int] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''attention'''						)
            a						:List[Any] 							=       layer_norm
            a						:str 							=       k.T
            a						:Dict 							=       o.T
            a						:int 							=       q.T
            a						:Optional[Any] 							=       v.T

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_						):
                  # Block i, layer 0 (Self Attention).
                  a						:List[str] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_self_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:List[Any] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''self_attention'''						)
                  a						:List[Any] 							=       layer_norm
                  a						:Tuple 							=       k.T
                  a						:int 							=       o.T
                  a						:Any 							=       q.T
                  a						:Optional[int] 							=       v.T

                  # Block i, layer 1 (Cross Attention).
                  a						:str 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_cross_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:Any 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''encoder_decoder_attention'''						)
                  a						:str 							=       layer_norm
                  a						:Optional[Any] 							=       k.T
                  a						:Any 							=       o.T
                  a						:Dict 							=       q.T
                  a						:Optional[Any] 							=       v.T

                  # Block i, layer 2 (MLP).
                  a						:Optional[int] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_mlp_layer_norm'''						)
                  a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  UpperCAmelCase_						)
                  a						:Optional[int] 							=       layer_norm
                  if split_mlp_wi:
                        a						:int 							=       wi[0].T
                        a						:Tuple 							=       wi[1].T
                  else:
                        a						:str 							=       wi.T
                  a						:Dict 							=       wo.T

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  a						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[Any] 							=       collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            a						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''						)
                  a						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.from_json_file(UpperCAmelCase_						)
      print(F'''Building PyTorch model from configuration: {config}'''						)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            a						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
      model.save_pretrained(UpperCAmelCase_						)

      # Verify that we can load the checkpoint.
      model.from_pretrained(UpperCAmelCase_						)
      print('''Done'''						)


if __name__ == "__main__":
     snake_case				:    Any       				=     argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





