




"""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 _lowerCamelCase    (					unittest.TestCase						):






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

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	parent
         lowerCAmelCase__      :			str   =	batch_size
         lowerCAmelCase__      :			Optional[int]   =	seq_length
         lowerCAmelCase__      :			Any   =	is_training
         lowerCAmelCase__      :			str   =	use_attention_mask
         lowerCAmelCase__      :			Any   =	use_token_type_ids
         lowerCAmelCase__      :			Dict   =	use_labels
         lowerCAmelCase__      :			List[str]   =	vocab_size
         lowerCAmelCase__      :			Tuple   =	hidden_size
         lowerCAmelCase__      :			Dict   =	num_hidden_layers
         lowerCAmelCase__      :			Optional[int]   =	num_attention_heads
         lowerCAmelCase__      :			Any   =	intermediate_size
         lowerCAmelCase__      :			List[str]   =	hidden_act
         lowerCAmelCase__      :			Optional[Any]   =	hidden_dropout_prob
         lowerCAmelCase__      :			Optional[Any]   =	attention_probs_dropout_prob
         lowerCAmelCase__      :			Optional[Any]   =	max_position_embeddings
         lowerCAmelCase__      :			Union[str, Any]   =	type_vocab_size
         lowerCAmelCase__      :			Optional[int]   =	type_sequence_label_size
         lowerCAmelCase__      :			int   =	initializer_range
         lowerCAmelCase__      :			Dict   =	num_choices






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size   )

         lowerCAmelCase__      :			Optional[Any]   =	None
         if self.use_attention_mask:
               lowerCAmelCase__      :			Optional[Any]   =	random_attention_mask([self.batch_size, self.seq_length]   )

         lowerCAmelCase__      :			Optional[Any]   =	None
         if self.use_token_type_ids:
               lowerCAmelCase__      :			str   =	ids_tensor([self.batch_size, self.seq_length]   ,     self.type_vocab_size   )

         lowerCAmelCase__      :			int   =	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=UpperCamelCase   ,     initializer_range=self.initializer_range   ,     )

         return config, input_ids, token_type_ids, attention_mask






   def 	_lowerCAmelCase   (		self       :		int   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	self.prepare_config_and_inputs()
         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	config_and_inputs
         lowerCAmelCase__      :			str   =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
         return config, inputs_dict






@require_flax
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Dict     				=     True

   _lowerCamelCase    :str     				=     (
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	FlaxRoFormerModelTester(self   )






   @slow
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Optional[Any]:

         """simple docstring"""
         for model_class_name in self.all_model_classes:
               lowerCAmelCase__      :			Union[str, Any]   =	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""   ,     from_pt=UpperCamelCase   )
               lowerCAmelCase__      :			Union[str, Any]   =	model(np.ones((1, 1)   )   )
               self.assertIsNotNone(UpperCamelCase   )






@require_flax
class _lowerCamelCase    (					unittest.TestCase						):






   @slow
   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
         lowerCAmelCase__      :			Optional[Any]   =	jnp.array([[0, 1, 2, 3, 4, 5]]   )
         lowerCAmelCase__      :			List[Any]   =	model(UpperCamelCase   )[0]

         lowerCAmelCase__      :			List[str]   =	5_00_00

         lowerCAmelCase__      :			str   =	(1, 6, vocab_size)
         self.assertEqual(output.shape   ,     UpperCamelCase   )

         lowerCAmelCase__      :			str   =	jnp.array(
             [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

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











"""simple docstring"""


import argparse

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


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

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


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  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."""
  )
  _A													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





