
'''simple docstring'''







import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import BlipTextModel
    from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST







class 		snake_case_       :



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase=12			,   UpperCamelCase=7			,   UpperCamelCase=True			,   UpperCamelCase=True			,   UpperCamelCase=True			,   UpperCamelCase=99			,   UpperCamelCase=32			,   UpperCamelCase=32			,   UpperCamelCase=5			,   UpperCamelCase=4			,   UpperCamelCase=37			,   UpperCamelCase=0.1			,   UpperCamelCase=0.1			,   UpperCamelCase=5_12			,   UpperCamelCase=0.0_2			,   UpperCamelCase=0			,   UpperCamelCase=None			,   ):
          lowerCamelCase__				   =      parent
          lowerCamelCase__				   =      batch_size
          lowerCamelCase__				   =      seq_length
          lowerCamelCase__				   =      is_training
          lowerCamelCase__				   =      use_input_mask
          lowerCamelCase__				   =      use_labels
          lowerCamelCase__				   =      vocab_size
          lowerCamelCase__				   =      hidden_size
          lowerCamelCase__				   =      projection_dim
          lowerCamelCase__				   =      num_hidden_layers
          lowerCamelCase__				   =      num_attention_heads
          lowerCamelCase__				   =      intermediate_size
          lowerCamelCase__				   =      dropout
          lowerCamelCase__				   =      attention_dropout
          lowerCamelCase__				   =      max_position_embeddings
          lowerCamelCase__				   =      initializer_range
          lowerCamelCase__				   =      scope
          lowerCamelCase__				   =      bos_token_id







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size)

          lowerCamelCase__				   =      None
          if self.use_input_mask:
                 lowerCamelCase__				   =      random_attention_mask([self.batch_size, self.seq_length])

          if input_mask is not None:
                 lowerCamelCase__   ,		lowerCamelCase__				   =      input_mask.shape
                 lowerCamelCase__				   =      np.random.randint(1			,   seq_length - 1			,   size=(batch_size,))
                 for batch_idx, start_index in enumerate(A__):
                        lowerCamelCase__				   =      1
                        lowerCamelCase__				   =      0

          lowerCamelCase__				   =      self.get_config()

          return config, input_ids, input_mask







   def     __UpperCAmelCase      ( self):
          return BlipTextConfig(
              vocab_size=self.vocab_size			,   hidden_size=self.hidden_size			,   projection_dim=self.projection_dim			,   num_hidden_layers=self.num_hidden_layers			,   num_attention_heads=self.num_attention_heads			,   intermediate_size=self.intermediate_size			,   dropout=self.dropout			,   attention_dropout=self.attention_dropout			,   max_position_embeddings=self.max_position_embeddings			,   initializer_range=self.initializer_range			,   bos_token_id=self.bos_token_id			,   )







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      BlipTextModel(config=A__)
          model.to(A__)
          model.eval()
          with torch.no_grad():
                 lowerCamelCase__				   =      model(A__			,   attention_mask=A__)
                 lowerCamelCase__				   =      model(A__)
          self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(result.pooler_output.shape			,   (self.batch_size, self.hidden_size))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.prepare_config_and_inputs()
          lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      config_and_inputs
          lowerCamelCase__				   =      {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict







@require_torch
class 		snake_case_       (						_lowerCAmelCase		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							int								=(BlipTextModel,) if is_torch_available() else ()
   __lowerCAmelCase	:							Dict								=False
   __lowerCAmelCase	:							Tuple								=False
   __lowerCAmelCase	:							Any								=False







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      BlipTextModelTester(self)
          lowerCamelCase__				   =      ConfigTester(self			,   config_class=A__			,   hidden_size=37)







   def     __UpperCAmelCase      ( self):
          self.config_tester.run_common_tests()







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__)







   def     __UpperCAmelCase      ( self):
          pass







   def     __UpperCAmelCase      ( self):
          pass







   @unittest.skip(reason="Blip does not use inputs_embeds")
   def     __UpperCAmelCase      ( self):
          pass







   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
   def     __UpperCAmelCase      ( self):
          pass







   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
   def     __UpperCAmelCase      ( self):
          pass







   @slow
   def     __UpperCAmelCase      ( self):
          for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__				   =      BlipTextModel.from_pretrained(A__)
                 self.assertIsNotNone(A__)







   def     __UpperCAmelCase      ( self):
          super().test_pt_tf_model_equivalence(allow_missing_keys=A__)







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							List[Any]=0    )       ->      Dict:


      '''simple docstring'''




      if name is None:
            lowerCamelCase__   	=					None
      else:
            lowerCamelCase__   	=					'.' * max(0  ,					spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowerCamelCase__   	=					fmt.format(lowerCamelCase    )

      # Print and recurse (if needed).
      if isinstance(lowerCamelCase  ,					lowerCamelCase    ):
            if msg is not None:
                  print(lowerCamelCase    )
            for k in val.keys():
                  recursive_print(lowerCamelCase  ,					val[k]  ,					spaces + 2    )
      elif isinstance(lowerCamelCase  ,					torch.Tensor    ):
            print(lowerCamelCase  ,					':'  ,					val.size()    )
      else:
            print(lowerCamelCase  ,					':'  ,					lowerCamelCase    )

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any    )       ->      Optional[int]:


      '''simple docstring'''




      lowerCamelCase__   	=					param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCamelCase__   	=					(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      lowerCamelCase__   	=					param.transpose(0  ,					1    ).contiguous()
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      return param

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							int    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					config.n_positions
      lowerCamelCase__   	=					config.n_layer
      lowerCamelCase__   	=					config.vocab_size
      lowerCamelCase__   	=					config.n_head
      lowerCamelCase__   	=					config.n_embd // config.n_head

      lowerCamelCase__   	=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCamelCase__   	=					{
          'transformer.wte.weight': word_embeddings,
          'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
          'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
          'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
      }

      lowerCamelCase__   	=					'model.language_model.encoder.layers.'
      for i in range(lowerCamelCase    ):
            lowerCamelCase__   	=					torch.tril(torch.ones((n_positions, n_positions)  ,					dtype=torch.bool    )    )
            lowerCamelCase__   	=					causal_mask.view(1  ,					1  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					causal_mask
            lowerCamelCase__   	=					torch.tensor(-1e4  ,					dtype=torch.bfloataa    )

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala.transpose(0  ,					1    ).contiguous()

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

      # For LM head, transformers' wants the matrix to weight embeddings.
      lowerCamelCase__   	=					word_embeddings

      return sd_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					6_40_00
      lowerCamelCase__   	=					config_megatron['encoder_seq_length']
      lowerCamelCase__   	=					config_megatron['hidden_size']
      lowerCamelCase__   	=					config_megatron['num_layers']
      lowerCamelCase__   	=					config_megatron['num_attention_heads']
      lowerCamelCase__   	=					config_megatron['ffn_hidden_size']
      lowerCamelCase__   	=					'gelu'
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					config_megatron['layernorm_epsilon']  # 1e-5
      lowerCamelCase__   	=					config_megatron['init_method_std']  # 0.02
      lowerCamelCase__   	=					config_megatron['apply_query_key_layer_scaling']  # True
      lowerCamelCase__   	=					True
      lowerCamelCase__   	=					True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					0  # <unk>
      else:
            lowerCamelCase__   	=					2  # <s>
            lowerCamelCase__   	=					3  # <|endoftext|>
            lowerCamelCase__   	=					0  # <pad>

      return config_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Dict:


      '''simple docstring'''




      print(lowerCamelCase    )

      lowerCamelCase__   	=					args.checkpoint_path
      lowerCamelCase__   	=					args.save_path
      if isfile(lowerCamelCase    ):
            raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

      # Load the model.
      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )

      # Load the config.
      lowerCamelCase__   	=					checkpoint['hyper_parameters']['cfg']
      lowerCamelCase__   	=					GPTaConfig()
      lowerCamelCase__   	=					copy_config(config_hf=lowerCamelCase  ,					config_megatron=lowerCamelCase    )
      lowerCamelCase__   	=					['GPT2LMHeadModel']

      lowerCamelCase__   	=					checkpoint['state_dict']

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase    )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(lowerCamelCase  ,					lowerCamelCase    )

      lowerCamelCase__   	=					'GPTSw3Tokenizer'

      # Store the config to file.
      print('Saving config'    )
      config_hf.save_pretrained(lowerCamelCase    )

      # Store the state_dict to file.
      lowerCamelCase__   	=					os.path.join(lowerCamelCase  ,					'pytorch_model.bin'    )
      print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
      torch.save(lowerCamelCase  ,					lowerCamelCase    )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Optional[int]      =argparse.ArgumentParser()
      parser.add_argument(
          """--checkpoint_path""",
          type=str,
          required=True,
          help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
      )
      parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
      parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
      __SCREAMING_SNAKE_CASE     : Tuple      =parser.parse_args()
      main(_args)