






'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)
logging.set_verbosity_info()







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      if "xprophetnet" in prophetnet_checkpoint_path:
            UpperCamelCase       						=      XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCamelCase					)
            UpperCamelCase						,UpperCamelCase       						=      XLMProphetNetForConditionalGeneration.from_pretrained(
                __UpperCamelCase	,		output_loading_info=__UpperCamelCase					)
      else:
            UpperCamelCase       						=      ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCamelCase					)
            UpperCamelCase						,UpperCamelCase       						=      ProphetNetForConditionalGeneration.from_pretrained(
                __UpperCamelCase	,		output_loading_info=__UpperCamelCase					)

      UpperCamelCase       						=      ["""key_proj""", """value_proj""", """query_proj"""]

      UpperCamelCase       						=      {
          """self_attn""": """ngram_self_attn""",
          """cross_attn""": """encoder_attn""",
          """cross_attn_layer_norm""": """encoder_attn_layer_norm""",
          """feed_forward_layer_norm""": """final_layer_norm""",
          """feed_forward""": """""",
          """intermediate""": """fc1""",
          """output""": """fc2""",
          """key_proj""": """k_proj""",
          """query_proj""": """q_proj""",
          """value_proj""": """v_proj""",
          """word_embeddings""": """embed_tokens""",
          """embeddings_layer_norm""": """emb_layer_norm""",
          """relative_pos_embeddings""": """relative_linear""",
          """ngram_embeddings""": """ngram_input_embed""",
          """position_embeddings""": """embed_positions""",
      }

      for key in loading_info["missing_keys"]:
            UpperCamelCase       						=      key.split("""."""					)

            if attributes[0] == "lm_head":
                  UpperCamelCase       						=      prophet
                  UpperCamelCase       						=      prophet_old
            else:
                  UpperCamelCase       						=      prophet.prophetnet
                  UpperCamelCase       						=      prophet_old.model

            UpperCamelCase       						=      False
            for attribute in attributes:
                  if attribute in mapping:
                        UpperCamelCase       						=      mapping[attribute]
                        if not hasattr(__UpperCamelCase	,		__UpperCamelCase					) and len(__UpperCamelCase					) > 0:
                              UpperCamelCase       						=      attribute
                  elif hasattr(__UpperCamelCase	,		__UpperCamelCase					):
                        UpperCamelCase       						=      attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        UpperCamelCase       						=      old_model.weight
                        logger.info(F"{attribute} is initialized."					)
                        UpperCamelCase       						=      True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        UpperCamelCase       						=      old_model.bias
                        logger.info(F"{attribute} is initialized"					)
                        UpperCamelCase       						=      True
                        break
                  elif attribute in special_keys and hasattr(__UpperCamelCase	,		"""in_proj_weight"""					):
                        UpperCamelCase       						=      old_model.in_proj_weight.shape[0] // 3
                        UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					)
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              UpperCamelCase       						=      nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
                              UpperCamelCase       						=      nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

                        elif attribute == "key_proj":
                              UpperCamelCase       						=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
                              UpperCamelCase       						=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
                        elif attribute == "value_proj":
                              UpperCamelCase       						=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
                              UpperCamelCase       						=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
                        UpperCamelCase       						=      True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                        UpperCamelCase       						=      nn.Parameter(old_model.embed_positions.weight[:512, :]					)
                        UpperCamelCase       						=      True
                        break

                  if attribute.isdigit():
                        UpperCamelCase       						=      model[int(__UpperCamelCase					)]
                        UpperCamelCase       						=      old_model[int(__UpperCamelCase					)]
                  else:
                        UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					)

                        if old_attribute == "":
                              UpperCamelCase       						=      old_model
                        else:
                              if not hasattr(__UpperCamelCase	,		__UpperCamelCase					):
                                    raise ValueError(F"{old_model} does not have {old_attribute}"					)
                              UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					)

            if not is_key_init:
                  raise ValueError(F"{key} was not correctly initialized!"					)

      print(F"Saving model to {pytorch_dump_folder_path}"					)
      prophet.save_pretrained(__UpperCamelCase					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
