



"""simple docstring"""







import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=-1		)      ->	Optional[int]:
				# in NER datasets, the last column is usually reserved for NER label
				A__											=					label_idx


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[InputExample]:
				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					mode.value
				A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	f"""{mode}.txt"""		)
				A__											=					1
				A__											=					[]
				with open(SCREAMING_SNAKE_CASE__						,	encoding="utf-8"		) as f:
					A__											=					[]
					A__											=					[]
					for line in f:
						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
							if words:
								examples.append(InputExample(guid=f"""{mode}-{guid_index}"""						,	words=SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)		)
								guid_index += 1
								A__											=					[]
								A__											=					[]
						else:
							A__											=					line.split(" "		)
							words.append(splits[0]		)
							if len(SCREAMING_SNAKE_CASE__		) > 1:
								labels.append(splits[self.label_idx].replace("\n"						,	""		)		)
							else:
								# Examples could have no label for mode = "test"
								labels.append("O"		)
					if words:
						examples.append(InputExample(guid=f"""{mode}-{guid_index}"""						,	words=SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)		)
				return examples


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					0
				for line in test_input_reader:
					if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
						writer.write(SCREAMING_SNAKE_CASE__		)
						if not preds_list[example_id]:
							example_id += 1
					elif preds_list[example_id]:
						A__											=					line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
						writer.write(SCREAMING_SNAKE_CASE__		)
					else:
						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."						,	line.split()[0]		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"r"		) as f:
						A__											=					f.read().splitlines()
					if "O" not in labels:
						A__											=					["O"] + labels
					return labels
				else:
					return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self		)      ->	Tuple:
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"r"		) as f:
						A__											=					f.read().splitlines()
					if "O" not in labels:
						A__											=					["O"] + labels
					return labels
				else:
					return [
					    "O",
					    "B-ADVP",
					    "B-INTJ",
					    "B-LST",
					    "B-PRT",
					    "B-NP",
					    "B-SBAR",
					    "B-VP",
					    "B-ADJP",
					    "B-CONJP",
					    "B-PP",
					    "I-ADVP",
					    "I-INTJ",
					    "I-LST",
					    "I-PRT",
					    "I-NP",
					    "I-SBAR",
					    "I-VP",
					    "I-ADJP",
					    "I-CONJP",
					    "I-PP",
					]






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[InputExample]:
				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					mode.value
				A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	f"""{mode}.txt"""		)
				A__											=					1
				A__											=					[]

				with open(SCREAMING_SNAKE_CASE__						,	encoding="utf-8"		) as f:
					for sentence in parse_incr(SCREAMING_SNAKE_CASE__		):
						A__											=					[]
						A__											=					[]
						for token in sentence:
							words.append(token["form"]		)
							labels.append(token["upos"]		)
						assert len(SCREAMING_SNAKE_CASE__		) == len(SCREAMING_SNAKE_CASE__		)
						if words:
							examples.append(InputExample(guid=f"""{mode}-{guid_index}"""						,	words=SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)		)
							guid_index += 1
				return examples


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					0
				for sentence in parse_incr(SCREAMING_SNAKE_CASE__		):
					A__											=					preds_list[example_id]
					A__											=					""
					for token in sentence:
						out += f"""{token['form']} ({token['upos']}|{s_p.pop(0		)}) """
					out += "\n"
					writer.write(SCREAMING_SNAKE_CASE__		)
					example_id += 1







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"r"		) as f:
						return f.read().splitlines()
				else:
					return [
					    "ADJ",
					    "ADP",
					    "ADV",
					    "AUX",
					    "CCONJ",
					    "DET",
					    "INTJ",
					    "NOUN",
					    "NUM",
					    "PART",
					    "PRON",
					    "PROPN",
					    "PUNCT",
					    "SCONJ",
					    "SYM",
					    "VERB",
					    "X",
					]







'''simple docstring'''







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__="attention"					)      ->      Dict:
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      Optional[Any]:
       if split_mlp_wi:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
              A_						       =   (wi_a, wi_a)
       else:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

       A_						       =   params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def    UpperCAmelCase__     (   UpperCAmelCase__,							*, UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   traverse_util.flatten_dict(variables["""target"""]					)
       A_						       =   {"""/""".join(UpperCAmelCase__					): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       A_						       =   """encoder/layers_0/mlp/wi_0/kernel""" in old
       print("""Split MLP:""",							UpperCAmelCase__					)

       A_						       =   collections.OrderedDict()

       # Shared embeddings.
       A_						       =   old["""token_embedder/embedding"""]

       # Encoder.
       for i in range(UpperCAmelCase__					):
              # Block i, layer 0 (Self Attention).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_attention_layer_norm"""					)
              A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""attention"""					)
              A_						       =   layer_norm
              A_						       =   k.T
              A_						       =   o.T
              A_						       =   q.T
              A_						       =   v.T

              # Block i, layer 1 (MLP).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_mlp_layer_norm"""					)
              A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							UpperCAmelCase__					)
              A_						       =   layer_norm
              if split_mlp_wi:
                     A_						       =   wi[0].T
                     A_						       =   wi[1].T
              else:
                     A_						       =   wi.T
              A_						       =   wo.T

       A_						       =   old[
           """encoder/relpos_bias/rel_embedding"""
       ].T
       A_						       =   old["""encoder/encoder_norm/scale"""]

       if not is_encoder_only:
              # Decoder.
              for i in range(UpperCAmelCase__					):
                     # Block i, layer 0 (Self Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_self_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""self_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 1 (Cross Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_cross_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""encoder_decoder_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 2 (MLP).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_mlp_layer_norm"""					)
                     A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							UpperCAmelCase__					)
                     A_						       =   layer_norm
                     if split_mlp_wi:
                            A_						       =   wi[0].T
                            A_						       =   wi[1].T
                     else:
                            A_						       =   wi.T
                     A_						       =   wo.T

              A_						       =   old["""decoder/decoder_norm/scale"""]
              A_						       =   old[
                  """decoder/relpos_bias/rel_embedding"""
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     A_						       =   old["""decoder/logits_dense/kernel"""].T

       return new
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   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_						       =   state_dict["""shared.weight"""]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     A_						       =   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_						       =   state_dict["""shared.weight"""]

       return state_dict
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   checkpoints.load_tax_checkpoint(UpperCAmelCase__					)
       A_						       =   convert_tax_to_pytorch(UpperCAmelCase__,							num_layers=config.num_layers,							is_encoder_only=UpperCAmelCase__					)
       A_						       =   make_state_dict(UpperCAmelCase__,							UpperCAmelCase__					)
       model.load_state_dict(UpperCAmelCase__,							strict=UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = False					)      ->      List[Any]:
       A_						       =   TaConfig.from_json_file(UpperCAmelCase__					)
       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_						       =   TaEncoderModel(UpperCAmelCase__					)
       else:
              A_						       =   TaForConditionalGeneration(UpperCAmelCase__					)

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

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
       model.save_pretrained(UpperCAmelCase__					)

       # Verify that we can load the checkpoint.
       model.from_pretrained(UpperCAmelCase__					)
       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
      )
      __lowerCamelCase									=     parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )
