



'''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


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



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



		if "xprophetnet" in prophetnet_checkpoint_path:
				_a					:				Tuple				=							XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_  )
				_a					:				Union[str, Any]				=							XLMProphetNetForConditionalGeneration.from_pretrained(
				    lowerCAmelCase_						,      output_loading_info=lowerCAmelCase_  )
		else:
				_a					:				List[str]				=							ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_  )
				_a					:				Optional[Any]				=							ProphetNetForConditionalGeneration.from_pretrained(
				    lowerCAmelCase_						,      output_loading_info=lowerCAmelCase_  )

		_a					:				Optional[int]				=							["""key_proj""", """value_proj""", """query_proj"""]

		_a					:				Dict				=							{
		    """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"]:
				_a					:				int				=							key.split('.'  )

				if attributes[0] == "lm_head":
						_a					:				Union[str, Any]				=							prophet
						_a					:				List[str]				=							prophet_old
				else:
						_a					:				List[str]				=							prophet.prophetnet
						_a					:				Tuple				=							prophet_old.model

				_a					:				Dict				=							False
				for attribute in attributes:
						if attribute in mapping:
								_a					:				str				=							mapping[attribute]
								if not hasattr(lowerCAmelCase_						,      lowerCAmelCase_  ) and len(lowerCAmelCase_  ) > 0:
										_a					:				Optional[int]				=							attribute
						elif hasattr(lowerCAmelCase_						,      lowerCAmelCase_  ):
								_a					:				str				=							attribute

						if attribute == "weight":
								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
								_a					:				Any				=							old_model.weight
								logger.info(f"""{attribute} is initialized."""  )
								_a					:				int				=							True
								break
						elif attribute == "bias":
								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
								_a					:				Dict				=							old_model.bias
								logger.info(f"""{attribute} is initialized"""  )
								_a					:				Optional[int]				=							True
								break
						elif attribute in special_keys and hasattr(lowerCAmelCase_						,      'in_proj_weight'  ):
								_a					:				Any				=							old_model.in_proj_weight.shape[0] // 3
								_a					:				Any				=							getattr(lowerCAmelCase_						,      lowerCAmelCase_  )
								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":
										_a					:				Optional[int]				=							nn.Parameter(old_model.in_proj_weight[:embed_dim, :]  )
										_a					:				Tuple				=							nn.Parameter(old_model.in_proj_bias[:embed_dim]  )

								elif attribute == "key_proj":
										_a					:				Tuple				=							nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
										_a					:				str				=							nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
								elif attribute == "value_proj":
										_a					:				Optional[Any]				=							nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
										_a					:				int				=							nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
								_a					:				Union[str, Any]				=							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."
								_a					:				Dict				=							nn.Parameter(old_model.embed_positions.weight[:512, :]  )
								_a					:				List[Any]				=							True
								break

						if attribute.isdigit():
								_a					:				Dict				=							model[int(lowerCAmelCase_  )]
								_a					:				Any				=							old_model[int(lowerCAmelCase_  )]
						else:
								_a					:				Union[str, Any]				=							getattr(lowerCAmelCase_						,      lowerCAmelCase_  )

								if old_attribute == "":
										_a					:				int				=							old_model
								else:
										if not hasattr(lowerCAmelCase_						,      lowerCAmelCase_  ):
												raise ValueError(f"""{old_model} does not have {old_attribute}"""  )
										_a					:				List[str]				=							getattr(lowerCAmelCase_						,      lowerCAmelCase_  )

				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(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase						      =       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.'''
							)
							__lowerCAmelCase						      =       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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__lowerCAmelCase						      =       '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__lowerCAmelCase						      =       [
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
__lowerCAmelCase						      =       dict(zip(vocab, range(len(vocab))))
__lowerCAmelCase						      =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase						      =       Path(tmpdirname)
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''merges_file''']
							with open(src_vocab_file, '''w''') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, '''w''') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, '''w''') as fp:
														fp.write('''\n'''.join(merges))

							__lowerCAmelCase						      =       FSMTTokenizer(
							    langs=['''en''', '''ru'''],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__lowerCAmelCase						      =       FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

__lowerCAmelCase						      =       FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

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

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

# Save
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-ru




