
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__a  			=					{
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

__a  			=					{
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

__a  			=					{
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

__a  			=					{
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

__a  			=					{
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

__a  			=					{
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}




def 				a						(							snake_case__:					str					):


				'''simple docstring'''





				if isinstance(UpperCAmelCase_						,   UpperCAmelCase_					):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('''boolean value expected'''					)




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					Any						,   snake_case__:					Union[str, Any]=False					):


				'''simple docstring'''
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.0.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.0.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.2.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.2.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.0.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.0.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.3.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.3.bias''']

				if has_skip:
								lowercase_       =					checkpoint[F'''{old_prefix}.skip_connection.weight''']
								lowercase_       =					checkpoint[F'''{old_prefix}.skip_connection.bias''']

				return new_checkpoint




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


				'''simple docstring'''
				lowercase_       =					checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3						,   dim=0					)
				lowercase_       =					checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3						,   dim=0					)

				lowercase_       =					checkpoint[F'''{old_prefix}.norm.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.norm.bias''']

				lowercase_       =					weight_q.squeeze(-1					).squeeze(-1					)
				lowercase_       =					bias_q.squeeze(-1					).squeeze(-1					)
				lowercase_       =					weight_k.squeeze(-1					).squeeze(-1					)
				lowercase_       =					bias_k.squeeze(-1					).squeeze(-1					)
				lowercase_       =					weight_v.squeeze(-1					).squeeze(-1					)
				lowercase_       =					bias_v.squeeze(-1					).squeeze(-1					)

				lowercase_       =					(
				    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1					).squeeze(-1					)
				)
				lowercase_       =					checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1					).squeeze(-1					)

				return new_checkpoint




def 				a						(							snake_case__:					str						,   snake_case__:					Tuple					):


				'''simple docstring'''
				lowercase_       =					torch.load(UpperCAmelCase_						,   map_location='''cpu'''					)
				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''']

				if unet_config["num_class_embeds"] is not None:
								lowercase_       =					checkpoint['''label_emb.weight''']

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

				lowercase_       =					unet_config['''down_block_types''']
				lowercase_       =					unet_config['''layers_per_block''']
				lowercase_       =					unet_config['''attention_head_dim''']
				lowercase_       =					unet_config['''block_out_channels''']
				lowercase_       =					1
				lowercase_       =					channels_list[0]

				for i, layer_type in enumerate(UpperCAmelCase_					):
								lowercase_       =					channels_list[i]
								lowercase_       =					current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(UpperCAmelCase_					):
																lowercase_       =					F'''down_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''input_blocks.{current_layer}.0'''
																lowercase_       =					True if j == 0 and downsample_block_has_skip else False
																lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   has_skip=UpperCAmelCase_					)
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(UpperCAmelCase_					):
																lowercase_       =					F'''down_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''input_blocks.{current_layer}.0'''
																lowercase_       =					True if j == 0 and downsample_block_has_skip else False
																lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   has_skip=UpperCAmelCase_					)
																lowercase_       =					F'''down_blocks.{i}.attentions.{j}'''
																lowercase_       =					F'''input_blocks.{current_layer}.1'''
																lowercase_       =					convert_attention(
																    UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)
																current_layer += 1

								if i != len(UpperCAmelCase_					) - 1:
												lowercase_       =					F'''down_blocks.{i}.downsamplers.0'''
												lowercase_       =					F'''input_blocks.{current_layer}.0'''
												lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)
												current_layer += 1

								lowercase_       =					current_channels

				# hardcoded the mid-block for now
				lowercase_       =					'''mid_block.resnets.0'''
				lowercase_       =					'''middle_block.0'''
				lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)
				lowercase_       =					'''mid_block.attentions.0'''
				lowercase_       =					'''middle_block.1'''
				lowercase_       =					convert_attention(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)
				lowercase_       =					'''mid_block.resnets.1'''
				lowercase_       =					'''middle_block.2'''
				lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)

				lowercase_       =					0
				lowercase_       =					unet_config['''up_block_types''']

				for i, layer_type in enumerate(UpperCAmelCase_					):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1					):
																lowercase_       =					F'''up_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''output_blocks.{current_layer}.0'''
																lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   has_skip=UpperCAmelCase_					)
																current_layer += 1

												if i != len(UpperCAmelCase_					) - 1:
																lowercase_       =					F'''up_blocks.{i}.upsamplers.0'''
																lowercase_       =					F'''output_blocks.{current_layer-1}.1'''
																lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1					):
																lowercase_       =					F'''up_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''output_blocks.{current_layer}.0'''
																lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   has_skip=UpperCAmelCase_					)
																lowercase_       =					F'''up_blocks.{i}.attentions.{j}'''
																lowercase_       =					F'''output_blocks.{current_layer}.1'''
																lowercase_       =					convert_attention(
																    UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)
																current_layer += 1

												if i != len(UpperCAmelCase_					) - 1:
																lowercase_       =					F'''up_blocks.{i}.upsamplers.0'''
																lowercase_       =					F'''output_blocks.{current_layer-1}.2'''
																lowercase_       =					convert_resnet(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_					)

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

				return new_checkpoint


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

						parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
						parser.add_argument(
						    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
						)
						parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

						__a  			=					parser.parse_args()
						__a  			=					strabool(args.class_cond)

						__a  			=					os.path.basename(args.unet_path)
						print(f"Checkpoint: {ckpt_name}")

						# Get U-Net config
						if "imagenet64" in ckpt_name:
												__a  			=					IMAGENET_64_UNET_CONFIG
						elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												__a  			=					LSUN_256_UNET_CONFIG
						elif "test" in ckpt_name:
												__a  			=					TEST_UNET_CONFIG
						else:
												raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

						if not args.class_cond:
												__a  			=					None

						__a  			=					con_pt_to_diffuser(args.unet_path, unet_config)

						__a  			=					UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												__a  			=					CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												__a  			=					CT_IMAGENET_64_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												__a  			=					CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

						__a  			=					CMStochasticIterativeScheduler(**scheduler_config)

						__a  			=					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_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
     )





