



'''simple docstring'''

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_="attention"  )      ->				int:
		_a					:				Dict				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]  )
		_a					:				Dict				=							k_tmp.reshape(k_tmp.shape[0]						,      k_tmp.shape[1] * k_tmp.shape[2]  )
		_a					:				str				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]  )
		_a					:				Union[str, Any]				=							o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,      o_tmp.shape[2]  )
		_a					:				Optional[int]				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]  )
		_a					:				List[str]				=							q_tmp.reshape(q_tmp.shape[0]						,      q_tmp.shape[1] * q_tmp.shape[2]  )
		_a					:				Any				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]  )
		_a					:				List[Any]				=							v_tmp.reshape(v_tmp.shape[0]						,      v_tmp.shape[1] * v_tmp.shape[2]  )
		return k, o, q, v



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				List[Any]:
		if split_mlp_wi:
				_a					:				Union[str, Any]				=							params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_a					:				Optional[int]				=							params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_a					:				List[str]				=							(wi_a, wi_a)
		else:
				_a					:				Any				=							params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_a					:				List[str]				=							params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[Any]:
		return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 					__lowerCamelCase					(				lowerCAmelCase_						,      *, lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = False  )      ->				Tuple:
		_a					:				Optional[Any]				=							traverse_util.flatten_dict(variables['target']  )
		_a					:				int				=							{'/'.join(lowerCAmelCase_  ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_a					:				Union[str, Any]				=							'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'						,      lowerCAmelCase_  )

		_a					:				str				=							collections.OrderedDict()

		# Shared embeddings.
		_a					:				str				=							old['token_embedder/embedding']

		# Encoder.
		for i in range(lowerCAmelCase_  ):
				# Block i, layer 0 (Self Attention).
				_a					:				List[str]				=							tax_layer_norm_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'encoder'						,      'pre_attention_layer_norm'  )
				_a	, _a	, _a	, _a					:				Tuple				=							tax_attention_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'encoder'						,      'attention'  )
				_a					:				int				=							layer_norm
				_a					:				Optional[Any]				=							k.T
				_a					:				List[Any]				=							o.T
				_a					:				Dict				=							q.T
				_a					:				Tuple				=							v.T

				# Block i, layer 1 (MLP).
				_a					:				List[Any]				=							tax_layer_norm_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'encoder'						,      'pre_mlp_layer_norm'  )
				_a	, _a					:				List[Any]				=							tax_mlp_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'encoder'						,      lowerCAmelCase_  )
				_a					:				int				=							layer_norm
				if split_mlp_wi:
						_a					:				Union[str, Any]				=							wi[0].T
						_a					:				Tuple				=							wi[1].T
				else:
						_a					:				int				=							wi.T
				_a					:				Union[str, Any]				=							wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_a					:				Dict				=							tax_relpos_bias_lookup(
						    lowerCAmelCase_						,      lowerCAmelCase_						,      'encoder'  ).T

		_a					:				Union[str, Any]				=							old['encoder/encoder_norm/scale']

		if not scalable_attention:
				_a					:				int				=							tax_relpos_bias_lookup(
				    lowerCAmelCase_						,      0						,      'encoder'  ).T
				_a					:				List[Any]				=							tax_relpos_bias_lookup(
				    lowerCAmelCase_						,      0						,      'decoder'  ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase_  ):
						# Block i, layer 0 (Self Attention).
						_a					:				Optional[Any]				=							tax_layer_norm_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'						,      'pre_self_attention_layer_norm'  )
						_a	, _a	, _a	, _a					:				Union[str, Any]				=							tax_attention_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'						,      'self_attention'  )
						_a					:				Any				=							layer_norm
						_a					:				List[Any]				=							k.T
						_a					:				int				=							o.T
						_a					:				List[str]				=							q.T
						_a					:				List[str]				=							v.T

						# Block i, layer 1 (Cross Attention).
						_a					:				Optional[Any]				=							tax_layer_norm_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'						,      'pre_cross_attention_layer_norm'  )
						_a	, _a	, _a	, _a					:				List[str]				=							tax_attention_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'						,      'encoder_decoder_attention'  )
						_a					:				Any				=							layer_norm
						_a					:				Optional[int]				=							k.T
						_a					:				Union[str, Any]				=							o.T
						_a					:				List[str]				=							q.T
						_a					:				Any				=							v.T

						# Block i, layer 2 (MLP).
						_a					:				Any				=							tax_layer_norm_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'						,      'pre_mlp_layer_norm'  )
						_a	, _a					:				Any				=							tax_mlp_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'						,      lowerCAmelCase_  )
						_a					:				Tuple				=							layer_norm
						if split_mlp_wi:
								_a					:				Optional[Any]				=							wi[0].T
								_a					:				str				=							wi[1].T
						else:
								_a					:				Any				=							wi.T
						_a					:				Dict				=							wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_a					:				Tuple				=							tax_relpos_bias_lookup(lowerCAmelCase_						,      lowerCAmelCase_						,      'decoder'  ).T

				_a					:				int				=							old['decoder/decoder_norm/scale']

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						_a					:				Dict				=							old['decoder/logits_dense/kernel'].T

		return new



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		_a					:				List[str]				=							collections.OrderedDict([(k, torch.from_numpy(v.copy()  )) for (k, v) in converted_params.items()]  )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				_a					:				str				=							state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_a					:				Optional[Any]				=							state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'  )
						_a					:				Tuple				=							state_dict['shared.weight']

		return state_dict



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:
		_a					:				Dict				=							checkpoints.load_tax_checkpoint(lowerCAmelCase_  )
		_a					:				List[Any]				=							convert_tax_to_pytorch(
		    lowerCAmelCase_						,      num_layers=config.num_layers						,      is_encoder_only=lowerCAmelCase_						,      scalable_attention=lowerCAmelCase_  )
		_a					:				int				=							make_state_dict(lowerCAmelCase_						,      lowerCAmelCase_  )
		model.load_state_dict(lowerCAmelCase_						,      strict=lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = False						,      lowerCAmelCase_ = False						,      )      ->				List[str]:
		_a					:				List[Any]				=							MTaConfig.from_json_file(lowerCAmelCase_  )
		print(f"""Building PyTorch model from configuration: {config}"""  )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				_a					:				Any				=							UMTaEncoderModel(lowerCAmelCase_  )
		else:
				_a					:				List[str]				=							UMTaForConditionalGeneration(lowerCAmelCase_  )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
		model.save_pretrained(lowerCAmelCase_  )

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCAmelCase_  )
		print('Done'  )


if __name__ == "__main__":
							__lowerCAmelCase						      =       argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
							)
							parser.add_argument(
							    '''--scalable_attention''',
							    action='''store_true''',
							    help='''Whether the model uses scaled attention (umt5 model)''',
							    default=False,
							)
							__lowerCAmelCase						      =       parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)









'''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




