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     lowercase     (  unittest.TestCase  ):




							def __init__(    self :       List[str]						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Optional[int]=13						,				_UpperCamelCase :       Any=7						,				_UpperCamelCase :       List[Any]=True						,				_UpperCamelCase :       Tuple=True						,				_UpperCamelCase :       Dict=True						,				_UpperCamelCase :       int=True						,				_UpperCamelCase :       Dict=99						,				_UpperCamelCase :       Optional[int]=32						,				_UpperCamelCase :       Union[str, Any]=5						,				_UpperCamelCase :       Optional[int]=4						,				_UpperCamelCase :       Optional[int]=37						,				_UpperCamelCase :       Optional[int]="gelu"						,				_UpperCamelCase :       List[Any]=0.1						,				_UpperCamelCase :       Dict=0.1						,				_UpperCamelCase :       Optional[int]=512						,				_UpperCamelCase :       Tuple=16						,				_UpperCamelCase :       Tuple=2						,				_UpperCamelCase :       str=0.0_2						,				_UpperCamelCase :       Optional[Any]=4						,				)       ->					str:



												'''simple docstring'''

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




							def 						__snake_case(    self :       int   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size   )

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

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

												SCREAMING_SNAKE_CASE           =							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 						__snake_case(    self :       List[str]   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE           =							config_and_inputs
												SCREAMING_SNAKE_CASE           =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
												return config, inputs_dict



@require_flax
class     lowercase     (  snake_case_					,							unittest.TestCase  ):
							lowercase__    :							List[str]										=				True

							lowercase__    :							int										=				(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def 						__snake_case(    self :       Union[str, Any]   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							FlaxRoFormerModelTester(self   )





							@slow
							def 						__snake_case(    self :       int   )       ->					Tuple:



												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =							model_class_name.from_pretrained("junnyu/roformer_chinese_small"						,				from_pt=UpperCamelCase__   )
																	SCREAMING_SNAKE_CASE           =							model(np.ones((1, 1)   )   )
																	self.assertIsNotNone(UpperCamelCase__   )



@require_flax
class     lowercase     (  unittest.TestCase  ):




							@slow
							def 						__snake_case(    self :       Tuple   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"   )
												SCREAMING_SNAKE_CASE           =							jnp.array([[0, 1, 2, 3, 4, 5]]   )
												SCREAMING_SNAKE_CASE           =							model(UpperCamelCase__   )[0]

												SCREAMING_SNAKE_CASE           =							50_000

												SCREAMING_SNAKE_CASE           =							(1, 6, vocab_size)
												self.assertEqual(output.shape						,				UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							jnp.array(
												    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]   )

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








"""simple docstring"""



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(lowercase		,				lowercase		,				lowercase					)

    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'					)
    torch.save(model.state_dict()		,				lowercase					)


if __name__ == "__main__":
  __snake_case       =       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(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __snake_case       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





