
"""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__							:							Optional[Any],						lowerCamelCase__							:							Any,						lowerCamelCase__							:							List[Any]				)  ->    Any:
  return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





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





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							Optional[Any]=False				)  ->    int:
  if split_mlp_wi:
    _SCREAMING_SNAKE_CASE   :				List[str]							    = params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    _SCREAMING_SNAKE_CASE   :				Any							    = (wi_a, wi_a)
  else:
    _SCREAMING_SNAKE_CASE   :				str							    = params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						*, lowerCamelCase__							:							Any,						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Dict = False				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = traverse_util.flatten_dict(variables["target"]				)
  _SCREAMING_SNAKE_CASE   :				Optional[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
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = """encoder/encoder/mlp/wi_0/kernel""" in old
  print("Split MLP:",						lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Tuple							    = collections.OrderedDict()

  # Shared embeddings.
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = old["""token_embedder/embedding"""]

  # Encoder.
  for i in range(lowerCamelCase__				):
    # Block i, layer 0 (Self Attention).
    _SCREAMING_SNAKE_CASE   :				int							    = tax_layer_norm_lookup(lowerCamelCase__,						lowerCamelCase__,						"encoder",						"pre_attention_layer_norm"				)
    _SCREAMING_SNAKE_CASE   :				Dict							    = tax_attention_lookup(lowerCamelCase__,						lowerCamelCase__,						"encoder",						"attention"				)
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = layer_norm
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = k.T
    _SCREAMING_SNAKE_CASE   :				Tuple							    = o.T
    _SCREAMING_SNAKE_CASE   :				Any							    = q.T
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = v.T

    # Block i, layer 1 (MLP).
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = tax_layer_norm_lookup(lowerCamelCase__,						lowerCamelCase__,						"encoder",						"pre_mlp_layer_norm"				)
    _SCREAMING_SNAKE_CASE   :				Tuple							    = tax_mlp_lookup(lowerCamelCase__,						lowerCamelCase__,						"encoder",						lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				List[str]							    = layer_norm
    if split_mlp_wi:
      _SCREAMING_SNAKE_CASE   :				Dict							    = wi[0].T
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = wi[1].T
    else:
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = wi.T
    _SCREAMING_SNAKE_CASE   :				Tuple							    = wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      _SCREAMING_SNAKE_CASE   :				List[str]							    = tax_relpos_bias_lookup(
          lowerCamelCase__,						lowerCamelCase__,						"encoder"				).T

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = old["""encoder/encoder_norm/scale"""]

  if not scalable_attention:
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = tax_relpos_bias_lookup(
        lowerCamelCase__,						0,						"encoder"				).T
    _SCREAMING_SNAKE_CASE   :				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).
      _SCREAMING_SNAKE_CASE   :				int							    = tax_layer_norm_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder",						"pre_self_attention_layer_norm"				)
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tax_attention_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder",						"self_attention"				)
      _SCREAMING_SNAKE_CASE   :				Dict							    = layer_norm
      _SCREAMING_SNAKE_CASE   :				str							    = k.T
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = o.T
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = q.T
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = v.T

      # Block i, layer 1 (Cross Attention).
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tax_layer_norm_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder",						"pre_cross_attention_layer_norm"				)
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = tax_attention_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder",						"encoder_decoder_attention"				)
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = layer_norm
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = k.T
      _SCREAMING_SNAKE_CASE   :				Tuple							    = o.T
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = q.T
      _SCREAMING_SNAKE_CASE   :				Dict							    = v.T

      # Block i, layer 2 (MLP).
      _SCREAMING_SNAKE_CASE   :				int							    = tax_layer_norm_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder",						"pre_mlp_layer_norm"				)
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tax_mlp_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder",						lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = layer_norm
      if split_mlp_wi:
        _SCREAMING_SNAKE_CASE   :				int							    = wi[0].T
        _SCREAMING_SNAKE_CASE   :				Dict							    = wi[1].T
      else:
        _SCREAMING_SNAKE_CASE   :				List[Any]							    = wi.T
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        _SCREAMING_SNAKE_CASE   :				Any							    = tax_relpos_bias_lookup(lowerCamelCase__,						lowerCamelCase__,						"decoder"				).T

    _SCREAMING_SNAKE_CASE   :				str							    = 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:
      _SCREAMING_SNAKE_CASE   :				Dict							    = old["""decoder/logits_dense/kernel"""].T

  return new





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[Any]				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				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:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = state_dict["""shared.weight"""]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      _SCREAMING_SNAKE_CASE   :				int							    = 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."				)
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = state_dict["""shared.weight"""]

  return state_dict





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							List[Any]				)  ->    int:
  _SCREAMING_SNAKE_CASE   :				str							    = checkpoints.load_tax_checkpoint(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = convert_tax_to_pytorch(
      lowerCamelCase__,						num_layers=config.num_layers,						is_encoder_only=lowerCamelCase__,						scalable_attention=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = make_state_dict(lowerCamelCase__,						lowerCamelCase__				)
  model.load_state_dict(lowerCamelCase__,						strict=lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							Optional[Any] = False,						lowerCamelCase__							:							Optional[Any] = False,						)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = 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:
    _SCREAMING_SNAKE_CASE   :				Dict							    = UMTaEncoderModel(lowerCamelCase__				)
  else:
    _SCREAMING_SNAKE_CASE   :				Dict							    = 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__":
      lowercase_       :						str     					=	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,
      )
      lowercase_       :						Union[str, Any]     					=	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"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



