






"""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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase="attention" ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase=False ):




  """simple docstring"""







  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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return params[f'{prefix}/layers_{i}/{layer_name}/scale']



def 					lowerCAmelCase				(    __UpperCamelCase		,       *, __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase = False ):




  """simple docstring"""







  __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__":
       lowercase_				     =     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
       )
       lowercase_				     =     parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )












"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple,       _lowerCamelCase :     List[str]=13,       _lowerCamelCase :     Optional[Any]=7,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     int=True,       _lowerCamelCase :     List[str]=True,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     int=99,       _lowerCamelCase :     Optional[int]=32,       _lowerCamelCase :     Tuple=5,       _lowerCamelCase :     Tuple=4,       _lowerCamelCase :     str=37,       _lowerCamelCase :     Union[str, Any]="gelu",       _lowerCamelCase :     int=0.1,       _lowerCamelCase :     List[Any]=0.1,       _lowerCamelCase :     Dict=5_12,       _lowerCamelCase :     List[Any]=16,       _lowerCamelCase :     Any=2,       _lowerCamelCase :     Any=0.02,       _lowerCamelCase :     Dict=4,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= seq_length
     __A									= is_training
     __A									= use_attention_mask
     __A									= use_token_type_ids
     __A									= use_labels
     __A									= vocab_size
     __A									= hidden_size
     __A									= num_hidden_layers
     __A									= num_attention_heads
     __A									= intermediate_size
     __A									= hidden_act
     __A									= hidden_dropout_prob
     __A									= attention_probs_dropout_prob
     __A									= max_position_embeddings
     __A									= type_vocab_size
     __A									= type_sequence_label_size
     __A									= initializer_range
     __A									= num_choices


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= ids_tensor([self.batch_size, self.seq_length],       self.vocab_size  )

     __A									= None
     if self.use_attention_mask:
       __A									= random_attention_mask([self.batch_size, self.seq_length]  )

     __A									= None
     if self.use_token_type_ids:
       __A									= ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size  )

     __A									= RoFormerConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       is_decoder=_lowerCamelCase,       initializer_range=self.initializer_range,       )

     return config, input_ids, token_type_ids, attention_mask






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     __A			,    __A			,    __A			,    __A									= config_and_inputs
     __A									= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict





@require_flax
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Dict						 = True

   A_						:	Tuple						 = (
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= FlaxRoFormerModelTester(self  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     for model_class_name in self.all_model_classes:
       __A									= model_class_name.from_pretrained('''junnyu/roformer_chinese_small''',       from_pt=_lowerCamelCase  )
       __A									= model(np.ones((1, 1)  )  )
       self.assertIsNotNone(_lowerCamelCase  )





@require_flax
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
     __A									= jnp.array([[0, 1, 2, 3, 4, 5]]  )
     __A									= model(_lowerCamelCase  )[0]

     __A									= 5_00_00

     __A									= (1, 6, vocab_size)
     self.assertEqual(output.shape,       _lowerCamelCase  )

     __A									= jnp.array(
         [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]  )

     self.assertTrue(jnp.allclose(output[:, :3, :3],       _lowerCamelCase,       atol=1e-4  )  )




