
import unittest

import numpy as np

from transformers import ElectraConfig, 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():
     from transformers.models.electra.modeling_flax_electra import (
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForMultipleChoice,
         FlaxElectraForPreTraining,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForSequenceClassification,
         FlaxElectraForTokenClassification,
         FlaxElectraModel,
     )


class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=13						,    __UpperCamelCase=7						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase=99						,    __UpperCamelCase=24						,    __UpperCamelCase=32						,    __UpperCamelCase=5						,    __UpperCamelCase=4						,    __UpperCamelCase=37						,    __UpperCamelCase="gelu"						,    __UpperCamelCase=0.1						,    __UpperCamelCase=0.1						,    __UpperCamelCase=512						,    __UpperCamelCase=16						,    __UpperCamelCase=2						,    __UpperCamelCase=0.02						,    __UpperCamelCase=4						,    ):
      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_								=					embedding_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 					lowercase_						(					self					):
      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_								=					ElectraConfig(
          vocab_size=self.vocab_size						,    hidden_size=self.hidden_size						,    embedding_size=self.embedding_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						,    initializer_range=self.initializer_range						,    )

      return config, input_ids, token_type_ids, attention_mask

  def 					lowercase_						(					self					):
      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 							lowerCamelCase       (		snake_case__			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		True

  lowerCAmelCase_								=		(
      (
          FlaxElectraModel,
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForPreTraining,
          FlaxElectraForTokenClassification,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForMultipleChoice,
          FlaxElectraForSequenceClassification,
      )
      if is_flax_available()
      else ()
  )

  def 					lowercase_						(					self					):
      A_								=					FlaxElectraModelTester(self					)

  @slow
  def 					lowercase_						(					self					):







      for model_class_name in self.all_model_classes:
          if model_class_name == FlaxElectraForMaskedLM:
              A_								=					model_class_name.from_pretrained("google/electra-small-generator"					)
          else:
              A_								=					model_class_name.from_pretrained("google/electra-small-discriminator"					)
          A_								=					model(np.ones((1, 1)					)					)
          self.assertIsNotNone(__UpperCamelCase					)














import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
_a       =    logging.get_logger(__name__)

_a       =    '''Hello world! cécé herlolip'''


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     FairseqRobertaModel.from_pretrained(__lowerCAmelCase      )
						roberta.eval()  # disable dropout
						_UpperCAmelCase							   =     roberta.model.encoder.sentence_encoder
						_UpperCAmelCase							   =     RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,						hidden_size=roberta.args.encoder_embed_dim					,						num_hidden_layers=roberta.args.encoder_layers					,						num_attention_heads=roberta.args.encoder_attention_heads					,						intermediate_size=roberta.args.encoder_ffn_embed_dim					,						max_position_embeddings=514					,						type_vocab_size=1					,						layer_norm_eps=1E-5					,						)
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     RobertaForSequenceClassification(__lowerCAmelCase      ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase      )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						_UpperCAmelCase							   =     roberta_sent_encoder.embed_tokens.weight
						_UpperCAmelCase							   =     roberta_sent_encoder.embed_positions.weight
						_UpperCAmelCase							   =     torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.
						_UpperCAmelCase							   =     roberta_sent_encoder.emb_layer_norm.weight
						_UpperCAmelCase							   =     roberta_sent_encoder.emb_layer_norm.bias

						for i in range(config.num_hidden_layers      ):
												# Encoder: start of layer
												_UpperCAmelCase							   =     model.roberta.encoder.layer[i]
												_UpperCAmelCase							   =     roberta_sent_encoder.layers[i]

												# self attention
												_UpperCAmelCase							   =     layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size)      )
												)

												_UpperCAmelCase							   =     roberta_layer.self_attn.q_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.q_proj.bias
												_UpperCAmelCase							   =     roberta_layer.self_attn.k_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.k_proj.bias
												_UpperCAmelCase							   =     roberta_layer.self_attn.v_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.v_proj.bias

												# self-attention output
												_UpperCAmelCase							   =     layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												_UpperCAmelCase							   =     roberta_layer.self_attn.out_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.out_proj.bias
												_UpperCAmelCase							   =     roberta_layer.self_attn_layer_norm.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn_layer_norm.bias

												# intermediate
												_UpperCAmelCase							   =     layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												_UpperCAmelCase							   =     roberta_layer.fca.weight
												_UpperCAmelCase							   =     roberta_layer.fca.bias

												# output
												_UpperCAmelCase							   =     layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												_UpperCAmelCase							   =     roberta_layer.fca.weight
												_UpperCAmelCase							   =     roberta_layer.fca.bias
												_UpperCAmelCase							   =     roberta_layer.final_layer_norm.weight
												_UpperCAmelCase							   =     roberta_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].dense.weight
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].dense.bias
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.weight
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.bias
						else:
												# LM Head
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.dense.weight
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.dense.bias
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.layer_norm.weight
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.layer_norm.bias
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.weight
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.bias

						# Let's check that we get the same results.
						_UpperCAmelCase							   =     roberta.encode(__lowerCAmelCase      ).unsqueeze(0      )  # batch of size 1

						_UpperCAmelCase							   =     model(__lowerCAmelCase      )[0]
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'](roberta.extract_features(__lowerCAmelCase      )      )
						else:
												_UpperCAmelCase							   =     roberta.model(__lowerCAmelCase      )[0]
						print(our_output.shape					,						their_output.shape      )
						_UpperCAmelCase							   =     torch.max(torch.abs(our_output - their_output      )      ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
						_UpperCAmelCase							   =     torch.allclose(__lowerCAmelCase					,						__lowerCAmelCase					,						atol=1E-3      )
						print('Do both models output the same tensors?'					,						'🔥' if success else '💩'      )
						if not success:
												raise Exception('Something went wRoNg'      )

						pathlib.Path(__lowerCAmelCase      ).mkdir(parents=__lowerCAmelCase					,						exist_ok=__lowerCAmelCase      )
						print(F"""Saving model to {pytorch_dump_folder_path}"""      )
						model.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		_a       =    parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
