


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  __lowerCAmelCase					()->						str:




      """simple docstring"""

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

      return image






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   []
      # 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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   dct.pop(SCREAMING_SNAKE_CASE      )
      snake_case_ =   val






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Union[str, Any]:




      """simple docstring"""

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

            # next, set bias in the state dict
            snake_case_ =   torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE      ,      requires_grad=SCREAMING_SNAKE_CASE      ), v_bias)      )
            snake_case_ =   qkv_bias






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[str]:




      """simple docstring"""

      snake_case_ =   364 if '''coco''' in model_name else 224
      snake_case_ =   InstructBlipVisionConfig(image_size=SCREAMING_SNAKE_CASE      ).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:
            snake_case_ =   TaConfig.from_pretrained('''google/flan-t5-xl'''      ,      dense_act_fn='''gelu'''      ,      bos_token_id=1      ).to_dict()
      elif "t5-xxl" in model_name:
            snake_case_ =   TaConfig.from_pretrained('''google/flan-t5-xxl'''      ,      dense_act_fn='''gelu'''      ,      bos_token_id=1      ).to_dict()
      elif "vicuna-7b" in model_name:
            snake_case_ =   LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''      ,      vocab_size=3_2001      ).to_dict()
      elif "vicuna-13b" in model_name:
            snake_case_ =   LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''      ,      vocab_size=3_2001      ).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
      snake_case_ =   InstructBlipQFormerConfig(vocab_size=3_0523      ).to_dict()
      snake_case_ =   InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE      ,      text_config=SCREAMING_SNAKE_CASE      ,      qformer_config=SCREAMING_SNAKE_CASE      )

      return config, image_size






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=False      )->						List[Any]:




      """simple docstring"""

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

      if "t5" in model_name:
            snake_case_ =   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>"})
            snake_case_ =   LlamaTokenizerFast.from_pretrained(
                '''huggyllama/llama-7b'''      ,      truncation_side='''left'''      ,      bos_token='''</s>'''      ,      unk_token='''</s>'''      )
            tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}      )

      snake_case_ ,					snake_case_ =   get_blipa_config(SCREAMING_SNAKE_CASE      )
      snake_case_ =   InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE      ).eval()

      snake_case_ =   {
          '''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'''),
      }

      snake_case_ ,					snake_case_ =   model_name_to_original[model_name]

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

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

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

      # read in qv biases
      read_in_q_v_bias(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

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

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

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

      original_model.to(SCREAMING_SNAKE_CASE      )
      hf_model.to(SCREAMING_SNAKE_CASE      )
      with torch.no_grad():
            if "vicuna" in model_name:
                  snake_case_ =   original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}      ).logits
                  snake_case_ =   hf_model(**SCREAMING_SNAKE_CASE      ).logits
            else:
                  snake_case_ =   original_model(
                      {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}      ).logits
                  snake_case_ =   tokenizer('''\n'''      ,      return_tensors='''pt'''      ).input_ids.to(SCREAMING_SNAKE_CASE      )
                  snake_case_ =   label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,      -100      )
                  snake_case_ =   hf_model(**SCREAMING_SNAKE_CASE      ,      labels=SCREAMING_SNAKE_CASE      ).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
      snake_case_ =   1E-4 if '''vicuna''' in model_name else 1E-5
      assert torch.allclose(original_logits.to(logits.device      )      ,      SCREAMING_SNAKE_CASE      ,      atol=SCREAMING_SNAKE_CASE      )
      print('''Looks ok!'''      )

      print('''Generating with original model...'''      )
      snake_case_ =   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...'''      )
      snake_case_ =   hf_model.generate(
          **SCREAMING_SNAKE_CASE      ,      do_sample=SCREAMING_SNAKE_CASE      ,      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?
            snake_case_ =   2
      print('''Original generation:'''      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   processor.batch_decode(SCREAMING_SNAKE_CASE      ,      skip_special_tokens=SCREAMING_SNAKE_CASE      )
      snake_case_ =   [text.strip() for text in output_text]
      print('''HF generation:'''      ,      SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
 UpperCAmelCase    =					argparse.ArgumentParser()
 UpperCAmelCase    =					[
     """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""",
 )

 UpperCAmelCase    =					parser.parse_args()

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



import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




      """simple docstring"""

      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )

      set_param(
          torch_layer.self_attention.query_key      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.value      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).view(-1      ,      SCREAMING_SNAKE_CASE      ).contiguous().transpose(0      ,      1      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )
      snake_case_ =   np.asarray(weights[3]      )

      set_param(
          torch_layer.self_attention.query      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.key      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.self_attention.value      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(1      ,      2      ).contiguous().view(-1      ,      SCREAMING_SNAKE_CASE      )      ,      )
      set_param(
          torch_layer.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).view(-1      ,      SCREAMING_SNAKE_CASE      ).contiguous().transpose(0      ,      1      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   weights[0][0][0]
      snake_case_ =   np.asarray(layer_norm_a[0]      )
      snake_case_ =   np.asarray(layer_norm_a[1]      )
      set_param(
          torch_block.attention.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # lsh weights + output
      snake_case_ =   weights[0][1]
      if len(SCREAMING_SNAKE_CASE      ) < 4:
            set_layer_weights_in_torch_lsh(SCREAMING_SNAKE_CASE      ,      torch_block.attention      ,      SCREAMING_SNAKE_CASE      )
      else:
            set_layer_weights_in_torch_local(SCREAMING_SNAKE_CASE      ,      torch_block.attention      ,      SCREAMING_SNAKE_CASE      )

      # intermediate weighs
      snake_case_ =   weights[2][0][1][2]

      # Chunked Feed Forward
      if len(SCREAMING_SNAKE_CASE      ) == 4:
            snake_case_ =   intermediate_weights[2]

      # layernorm 2
      snake_case_ =   np.asarray(intermediate_weights[0][0]      )
      snake_case_ =   np.asarray(intermediate_weights[0][1]      )
      set_param(
          torch_block.feed_forward.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate dense
      snake_case_ =   np.asarray(intermediate_weights[1][0]      )
      snake_case_ =   np.asarray(intermediate_weights[1][1]      )
      set_param(
          torch_block.feed_forward.dense.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # intermediate out
      snake_case_ =   np.asarray(intermediate_weights[4][0]      )
      snake_case_ =   np.asarray(intermediate_weights[4][1]      )
      set_param(
          torch_block.feed_forward.output.dense      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   torch_model.reformer

      # word embeds
      snake_case_ =   np.asarray(weights[1]      )
      set_param(
          torch_model_reformer.embeddings.word_embeddings      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      if isinstance(weights[3]      ,      SCREAMING_SNAKE_CASE      ):
            snake_case_ =   torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights      )      ):
                  snake_case_ =   np.asarray(weights[3][emb_idx][0]      )
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), f'''{position_embeddings[emb_idx]} emb does not match'''
                  snake_case_ =   nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE      )      )

      snake_case_ =   weights[5]
      assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
          SCREAMING_SNAKE_CASE      ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers      ):
            snake_case_ =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      # output layer norm
      snake_case_ =   np.asarray(weights[7][0]      )
      snake_case_ =   np.asarray(weights[7][1]      )
      set_param(
          torch_model_reformer.encoder.layer_norm      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )

      # output embeddings
      snake_case_ =   np.asarray(weights[9][0]      )
      snake_case_ =   np.asarray(weights[9][1]      )
      set_param(
          torch_model.lm_head.decoder      ,      torch.tensor(SCREAMING_SNAKE_CASE      ).transpose(0      ,      1      ).contiguous()      ,      torch.tensor(SCREAMING_SNAKE_CASE      )      ,      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE      )
      print(f'''Building PyTorch model from configuration: {config}'''      )
      snake_case_ =   ReformerModelWithLMHead(SCREAMING_SNAKE_CASE      )

      with open(SCREAMING_SNAKE_CASE      ,      '''rb'''      ) as f:
            snake_case_ =   pickle.load(SCREAMING_SNAKE_CASE      )['''weights''']

      set_model_weights_in_torch(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config.hidden_size      )

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''      )
      torch.save(model.state_dict()      ,      SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained Reformer model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase    =					parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)