





'''simple docstring'''
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def 				lowercase__   (   ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
       __UpperCamelCase       =  Image.open(requests.get(__lowercase				,  stream=__lowercase     ).raw     ).convert('RGB'     )

       return image
def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  []
       # fmt: off

       # vision encoder
       rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')     )
       rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')     )
       rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')     )
       rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')     )
       rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')     )
       rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')     )

       for i in range(config.vision_config.num_hidden_layers     ):
              rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')     )
              rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')     )

       # QFormer
       rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')     )
       rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')     )

       # fmt: on
       return rename_keys
def 				lowercase__   (   __lowercase						:  List[Any]				,  __lowercase						:  Any				,  __lowercase						:  Any     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  dct.pop(__lowercase     )
       __UpperCamelCase       =  val
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  Tuple     ) ->      Union[str, Any]:



       """simple docstring"""






       for i in range(config.vision_config.num_hidden_layers     ):
              # read in original q and v biases
              __UpperCamelCase       =  state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''     )
              __UpperCamelCase       =  state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''     )

              # next, set bias in the state dict
              __UpperCamelCase       =  torch.cat((q_bias, torch.zeros_like(__lowercase				,  requires_grad=__lowercase     ), v_bias)     )
              __UpperCamelCase       =  qkv_bias
def 				lowercase__   (   __lowercase						:  int     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  364 if 'coco' in model_name else 224
       __UpperCamelCase       =  InstructBlipVisionConfig(image_size=__lowercase     ).to_dict()

       # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
       # seems like flan-T5 models don't have bos_token_id properly set?
       if "t5-xl" in model_name:
              __UpperCamelCase       =  TaConfig.from_pretrained('google/flan-t5-xl'				,  dense_act_fn='gelu'				,  bos_token_id=1     ).to_dict()
       elif "t5-xxl" in model_name:
              __UpperCamelCase       =  TaConfig.from_pretrained('google/flan-t5-xxl'				,  dense_act_fn='gelu'				,  bos_token_id=1     ).to_dict()
       elif "vicuna-7b" in model_name:
              __UpperCamelCase       =  LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'				,  vocab_size=32001     ).to_dict()
       elif "vicuna-13b" in model_name:
              __UpperCamelCase       =  LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'				,  vocab_size=32001     ).to_dict()
       else:
              raise ValueError('Model name not supported'     )

       # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
       __UpperCamelCase       =  InstructBlipQFormerConfig(vocab_size=30523     ).to_dict()
       __UpperCamelCase       =  InstructBlipConfig(vision_config=__lowercase				,  text_config=__lowercase				,  qformer_config=__lowercase     )

       return config, image_size
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict=None				,  __lowercase						:  Dict=False     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  AutoTokenizer.from_pretrained('bert-base-uncased'				,  truncation_side='left'     )
       qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}     )

       if "t5" in model_name:
              __UpperCamelCase       =  TaTokenizerFast.from_pretrained('google/flan-t5-xl'				,  truncation_side='left'     )
       elif "vicuna" in model_name:
              # the following was used in the original implementation:
              # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
              # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
              # tokenizer.add_special_tokens({"bos_token": "</s>"})
              # tokenizer.add_special_tokens({"eos_token": "</s>"})
              # tokenizer.add_special_tokens({"unk_token": "</s>"})
              __UpperCamelCase       =  LlamaTokenizerFast.from_pretrained(
                  'huggyllama/llama-7b'				,  truncation_side='left'				,  bos_token='</s>'				,  unk_token='</s>'     )
              tokenizer.add_special_tokens({'pad_token': '[PAD]'}     )

       __UpperCamelCase       ,						__UpperCamelCase       =  get_blipa_config(__lowercase     )
       __UpperCamelCase       =  InstructBlipForConditionalGeneration(__lowercase     ).eval()

       __UpperCamelCase       =  {
           'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
           'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
           'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
           'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
       }

       __UpperCamelCase       ,						__UpperCamelCase       =  model_name_to_original[model_name]

       # load original model
       print('Loading original model...'     )
       __UpperCamelCase       =  'cuda:1' if torch.cuda.is_available() else 'cpu'
       __UpperCamelCase       =  'cuda:2' if torch.cuda.is_available() else 'cpu'
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  load_model_and_preprocess(
           name=__lowercase				,  model_type=__lowercase				,  is_eval=__lowercase				,  device=__lowercase     )
       original_model.eval()
       print('Done!'     )

       # update state dict keys
       __UpperCamelCase       =  original_model.state_dict()
       __UpperCamelCase       =  create_rename_keys(__lowercase     )
       for src, dest in rename_keys:
              rename_key(__lowercase				,  __lowercase				,  __lowercase     )

       # some keys can be renamed efficiently
       for key, val in state_dict.copy().items():
              __UpperCamelCase       =  state_dict.pop(__lowercase     )
              if key.startswith('Qformer.bert'     ):
                     __UpperCamelCase       =  key.replace('Qformer.bert'				,  'qformer'     )
              if "attention.self" in key:
                     __UpperCamelCase       =  key.replace('self'				,  'attention'     )
              if "llm_proj" in key:
                     __UpperCamelCase       =  key.replace('llm_proj'				,  'language_projection'     )
              if "t5_proj" in key:
                     __UpperCamelCase       =  key.replace('t5_proj'				,  'language_projection'     )
              if key.startswith('llm_model'     ):
                     __UpperCamelCase       =  key.replace('llm_model'				,  'language_model'     )
              if key.startswith('t5'     ):
                     __UpperCamelCase       =  key.replace('t5'				,  'language'     )
              __UpperCamelCase       =  val

       # read in qv biases
       read_in_q_v_bias(__lowercase				,  __lowercase     )

       # note: weights get loaded in torch.float32 by default
       hf_model.load_state_dict(__lowercase				,  strict=__lowercase     )

       __UpperCamelCase       =  load_demo_image()
       __UpperCamelCase       =  'What is unusual about this image?'

       # create processor
       __UpperCamelCase       =  BlipImageProcessor(
           size={'height': image_size, 'width': image_size}				,  image_mean=__lowercase				,  image_std=__lowercase     )
       __UpperCamelCase       =  InstructBlipProcessor(
           image_processor=__lowercase				,  tokenizer=__lowercase				,  qformer_tokenizer=__lowercase				,  )
       __UpperCamelCase       =  processor(images=__lowercase				,  text=__lowercase				,  return_tensors='pt'     ).to(__lowercase     )

       # make sure processor creates exact same pixel values
       __UpperCamelCase       =  vis_processors['eval'](__lowercase     ).unsqueeze(0     ).to(__lowercase     )
       __UpperCamelCase       =  inputs.pixel_values
       assert torch.allclose(original_pixel_values.to(pixel_values.device     )				,  __lowercase     )

       original_model.to(__lowercase     )
       hf_model.to(__lowercase     )
       with torch.no_grad():
              if "vicuna" in model_name:
                     __UpperCamelCase       =  original_model({'image': original_pixel_values, 'text_input': [prompt]}     ).logits
                     __UpperCamelCase       =  hf_model(**__lowercase     ).logits
              else:
                     __UpperCamelCase       =  original_model(
                         {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}     ).logits
                     __UpperCamelCase       =  tokenizer('\n'				,  return_tensors='pt'     ).input_ids.to(__lowercase     )
                     __UpperCamelCase       =  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id				,  -100     )
                     __UpperCamelCase       =  hf_model(**__lowercase				,  labels=__lowercase     ).logits

       print('First values of original logits:'				,  original_logits[0, :3, :3]     )
       print('First values of HF logits:'				,  logits[0, :3, :3]     )

       # assert values
       assert original_logits.shape == logits.shape
       __UpperCamelCase       =  1e-4 if 'vicuna' in model_name else 1e-5
       assert torch.allclose(original_logits.to(logits.device     )				,  __lowercase				,  atol=__lowercase     )
       print('Looks ok!'     )

       print('Generating with original model...'     )
       __UpperCamelCase       =  original_model.generate({'image': original_pixel_values, 'prompt': prompt}				,  num_beams=5     )

       # important: we need to cast the weights of the HF model to the appropriate type
       print('Generating with HF model...'     )
       __UpperCamelCase       =  hf_model.generate(
           **__lowercase				,  do_sample=__lowercase				,  num_beams=5				,  max_length=256				,  min_length=1				,  top_p=0.9				,  repetition_penalty=1.5				,  length_penalty=1.0				,  temperature=1				,  )
       if "vicuna" in model_name:
              # convert output id 0 to 2 (eos_token_id)
              # TODO add this in the generate method?
              __UpperCamelCase       =  2
       print('Original generation:'				,  __lowercase     )
       __UpperCamelCase       =  processor.batch_decode(__lowercase				,  skip_special_tokens=__lowercase     )
       __UpperCamelCase       =  [text.strip() for text in output_text]
       print('HF generation:'				,  __lowercase     )

       if pytorch_dump_folder_path is not None:
              processor.save_pretrained(__lowercase     )
              hf_model.save_pretrained(__lowercase     )

       if push_to_hub:
              processor.push_to_hub(F'''Salesforce/{model_name}'''     )
              hf_model.push_to_hub(F'''Salesforce/{model_name}'''     )


if __name__ == "__main__":
    a__							:							str =argparse.ArgumentParser()
    a__							:							List[str] =[
        '''instructblip-vicuna-7b''',
        '''instructblip-vicuna-13b''',
        '''instructblip-flan-t5-xl''',
        '''instructblip-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''instructblip-flan-t5-xl''',
        choices=choices,
        type=str,
        help='''Path to hf config.json of model to convert''',
    )
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether to push the model and processor to the hub after converting''',
    )

    a__							:							Any =parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      Tuple:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  List[str]="attention"     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  k_tmp.reshape(k_tmp.shape[0]				,  k_tmp.shape[1] * k_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,  o_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  q_tmp.reshape(q_tmp.shape[0]				,  q_tmp.shape[1] * q_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  v_tmp.reshape(v_tmp.shape[0]				,  v_tmp.shape[1] * v_tmp.shape[2]     )
       return k, o, q, v
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict				,  __lowercase						:  int				,  __lowercase						:  List[Any]=False     ) ->      Optional[Any]:



       """simple docstring"""






       if split_mlp_wi:
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
              __UpperCamelCase       =  (wi_a, wi_a)
       else:
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

       __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
       return wi, wo
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[Any]				,  __lowercase						:  List[str]				,  __lowercase						:  Optional[int]     ) ->      str:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def 				lowercase__   (   __lowercase						:  dict				,  *, __lowercase						:  int				,  __lowercase						:  bool				,  __lowercase						:  bool = False     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  traverse_util.flatten_dict(variables['target']     )
       __UpperCamelCase       =  {'/'.join(__lowercase     ): v for k, v in old.items()}

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

       __UpperCamelCase       =  collections.OrderedDict()

       # Shared embeddings.
       __UpperCamelCase       =  old['token_embedder/embedding']

       # Encoder.
       for i in range(__lowercase     ):
              # Block i, layer 0 (Self Attention).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_attention_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'attention'     )
              __UpperCamelCase       =  layer_norm
              __UpperCamelCase       =  k.T
              __UpperCamelCase       =  o.T
              __UpperCamelCase       =  q.T
              __UpperCamelCase       =  v.T

              # Block i, layer 1 (MLP).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_mlp_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'encoder'				,  __lowercase     )
              __UpperCamelCase       =  layer_norm
              if split_mlp_wi:
                     __UpperCamelCase       =  wi[0].T
                     __UpperCamelCase       =  wi[1].T
              else:
                     __UpperCamelCase       =  wi.T
              __UpperCamelCase       =  wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     __UpperCamelCase       =  tax_relpos_bias_lookup(
                         __lowercase				,  __lowercase				,  'encoder'     ).T

       __UpperCamelCase       =  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'encoder'     ).T
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'decoder'     ).T

       if not is_encoder_only:
              # Decoder.
              for i in range(__lowercase     ):
                     # Block i, layer 0 (Self Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_self_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'self_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 1 (Cross Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_cross_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'encoder_decoder_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 2 (MLP).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_mlp_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'decoder'				,  __lowercase     )
                     __UpperCamelCase       =  layer_norm
                     if split_mlp_wi:
                            __UpperCamelCase       =  wi[0].T
                            __UpperCamelCase       =  wi[1].T
                     else:
                            __UpperCamelCase       =  wi.T
                     __UpperCamelCase       =  wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            __UpperCamelCase       =  tax_relpos_bias_lookup(__lowercase				,  __lowercase				,  'decoder'     ).T

              __UpperCamelCase       =  old['decoder/decoder_norm/scale']

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

       return new
def 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  bool     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  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:
              __UpperCamelCase       =  state_dict['shared.weight']

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

       return state_dict
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Dict				,  __lowercase						:  str				,  __lowercase						:  int				,  __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  checkpoints.load_tax_checkpoint(__lowercase     )
       __UpperCamelCase       =  convert_tax_to_pytorch(
           __lowercase				,  num_layers=config.num_layers				,  is_encoder_only=__lowercase				,  scalable_attention=__lowercase     )
       __UpperCamelCase       =  make_state_dict(__lowercase				,  __lowercase     )
       model.load_state_dict(__lowercase				,  strict=__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  MTaConfig.from_json_file(__lowercase     )
       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:
              __UpperCamelCase       =  UMTaEncoderModel(__lowercase     )
       else:
              __UpperCamelCase       =  UMTaForConditionalGeneration(__lowercase     )

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

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

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


if __name__ == "__main__":
    a__							:							List[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
    )
    parser.add_argument(
        '''--scalable_attention''',
        action='''store_true''',
        help='''Whether the model uses scaled attention (umt5 model)''',
        default=False,
    )
    a__							:							List[str] =parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )
