



import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case       :	int            =logging.get_logger(__name__)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  os.path.abspath(lowerCamelCase_)
				logger.info(f"""Converting TensorFlow checkpoint from {tf_path}""")
				# Load weights from TF model
				lowerCAmelCase__  :       Dict				=  tf.train.list_variables(lowerCamelCase_)
				lowerCAmelCase__  :       List[str]				=  []
				lowerCAmelCase__  :       Union[str, Any]				=  []
				lowerCAmelCase__  :       Optional[Any]				=  []
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								lowerCAmelCase__  :       Optional[Any]				=  full_name.split('''/''')
								if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
												logger.info(f"""Skipping non-model layer {full_name}""")
												continue
								if "optimizer" in full_name:
												logger.info(f"""Skipping optimization layer {full_name}""")
												continue
								if name[0] == "model":
												# ignore initial 'model'
												lowerCAmelCase__  :       str				=  name[1:]
								# figure out how many levels deep the name is
								lowerCAmelCase__  :       Dict				=  0
								for _name in name:
												if _name.startswith('''layer_with_weights'''):
																depth += 1
												else:
																break
								layer_depth.append(lowerCamelCase_)
								# read data
								lowerCAmelCase__  :       Any				=  tf.train.load_variable(lowerCamelCase_ ,lowerCamelCase_)
								names.append('''/'''.join(lowerCamelCase_))
								arrays.append(lowerCamelCase_)
				logger.info(f"""Read a total of {len(lowerCamelCase_):,} layers""")

				# Sanity check
				if len(set(lowerCamelCase_)) != 1:
								raise ValueError(f"""Found layer names with different depths (layer depth {list(set(lowerCamelCase_))})""")
				lowerCAmelCase__  :       List[str]				=  list(set(lowerCamelCase_))[0]
				if layer_depth != 1:
								raise ValueError(
								    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
								    ''' heads.''')

				# convert layers
				logger.info('''Converting weights...''')
				for full_name, array in zip(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       int				=  full_name.split('''/''')
								lowerCAmelCase__  :       Tuple				=  model
								lowerCAmelCase__  :       int				=  []
								for i, m_name in enumerate(lowerCamelCase_):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith('''layer_with_weights'''):
																lowerCAmelCase__  :       Optional[int]				=  int(m_name.split('''-''')[-1])
																if layer_num <= 2:
																				# embedding layers
																				# layer_num 0: word_embeddings
																				# layer_num 1: position_embeddings
																				# layer_num 2: token_type_embeddings
																				continue
																elif layer_num == 3:
																				# embedding LayerNorm
																				trace.extend(['''embeddings''', '''LayerNorm'''])
																				lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''embeddings''')
																				lowerCAmelCase__  :       Optional[int]				=  getattr(lowerCamelCase_ ,'''LayerNorm''')
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(['''encoder''', '''layer''', str(layer_num - 4)])
																				lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''encoder''')
																				lowerCAmelCase__  :       Any				=  getattr(lowerCamelCase_ ,'''layer''')
																				lowerCAmelCase__  :       List[Any]				=  pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(['''pooler''', '''dense'''])
																				lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''pooler''')
																				lowerCAmelCase__  :       Optional[Any]				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "embeddings":
																trace.append('''embeddings''')
																lowerCAmelCase__  :       Any				=  getattr(lowerCamelCase_ ,'''embeddings''')
																if layer_num == 0:
																				trace.append('''word_embeddings''')
																				lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''word_embeddings''')
																elif layer_num == 1:
																				trace.append('''position_embeddings''')
																				lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''position_embeddings''')
																elif layer_num == 2:
																				trace.append('''token_type_embeddings''')
																				lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''token_type_embeddings''')
																else:
																				raise ValueError(f"""Unknown embedding layer with name {full_name}""")
																trace.append('''weight''')
																lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''weight''')
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(['''attention''', '''self'''])
																lowerCAmelCase__  :       Optional[Any]				=  getattr(lowerCamelCase_ ,'''attention''')
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''self''')
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(['''attention''', '''output''', '''LayerNorm'''])
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''attention''')
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       Any				=  getattr(lowerCamelCase_ ,'''LayerNorm''')
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(['''attention''', '''output''', '''dense'''])
																lowerCAmelCase__  :       int				=  getattr(lowerCamelCase_ ,'''attention''')
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "_output_dense":
																# output dense
																trace.extend(['''output''', '''dense'''])
																lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(['''output''', '''LayerNorm'''])
																lowerCAmelCase__  :       Optional[int]				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''LayerNorm''')
												elif m_name == "_key_dense":
																# attention key
																trace.append('''key''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''key''')
												elif m_name == "_query_dense":
																# attention query
																trace.append('''query''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''query''')
												elif m_name == "_value_dense":
																# attention value
																trace.append('''value''')
																lowerCAmelCase__  :       int				=  getattr(lowerCamelCase_ ,'''value''')
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(['''intermediate''', '''dense'''])
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''intermediate''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append('''output''')
																lowerCAmelCase__  :       int				=  getattr(lowerCamelCase_ ,'''output''')
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append('''bias''')
																lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''bias''')
												elif m_name in ["kernel", "gamma"]:
																trace.append('''weight''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''weight''')
												else:
																logger.warning(f"""Ignored {m_name}""")
        # for certain layers reshape is necessary
								lowerCAmelCase__  :       str				=  '''.'''.join(lowerCamelCase_)
								if re.match(r'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)''' ,lowerCamelCase_) or re.match(
								    r'''(\S+)\.attention\.output\.dense\.weight''' ,lowerCamelCase_):
												lowerCAmelCase__  :       Tuple				=  array.reshape(pointer.data.shape)
								if "kernel" in full_name:
												lowerCAmelCase__  :       Tuple				=  array.transpose()
								if pointer.shape == array.shape:
												lowerCAmelCase__  :       Union[str, Any]				=  torch.from_numpy(lowerCamelCase_)
								else:
												raise ValueError(
												    f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
												    f""" {array.shape}""")
								logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}""")
				return model






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	str ,lowerCamelCase_	:	List[str]):





				'''simple docstring'''

				logger.info(f"""Loading model based on config from {config_path}...""")
				lowerCAmelCase__  :       Tuple				=  BertConfig.from_json_file(lowerCamelCase_)
				lowerCAmelCase__  :       Union[str, Any]				=  BertModel(lowerCamelCase_)

				# Load weights from checkpoint
				logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}...""")
				load_tfa_weights_in_bert(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				# Save pytorch-model
				logger.info(f"""Saving PyTorch model to {pytorch_dump_path}...""")
				torch.save(model.state_dict() ,lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	int            =argparse.ArgumentParser()
							parser.add_argument(
							    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
							)
							parser.add_argument(
							    '--bert_config_file',
							    type=str,
							    required=True,
							    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
							)
							parser.add_argument(
							    '--pytorch_dump_path',
							    type=str,
							    required=True,
							    help='Path to the output PyTorch model (must include filename).',
							)
							__snake_case       :	Tuple            =parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






# 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

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    '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>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =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))

							__snake_case       :	Union[str, Any]            =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,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    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,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =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

