





'''simple docstring'''

import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
			import jax

			from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
			from transformers.models.bert.modeling_flax_bert import FlaxBertModel
			from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class    UpperCAmelCase			(    unittest.TestCase	):




		@slow
		def 					UpperCAmelCase__ (self   :						Union[str, Any]					)       ->			Any:
						for model_name in ["bert-base-cased", "bert-large-uncased"]:
										with self.subTest(lowerCamelCase__					):
														lowercase    =			AutoConfig.from_pretrained(lowerCamelCase__					)
														self.assertIsNotNone(lowerCamelCase__					)
														self.assertIsInstance(lowerCamelCase__				,      lowerCamelCase__					)

														lowercase    =			FlaxAutoModel.from_pretrained(lowerCamelCase__					)
														self.assertIsNotNone(lowerCamelCase__					)
														self.assertIsInstance(lowerCamelCase__				,      lowerCamelCase__					)




		@slow
		def 					UpperCAmelCase__ (self   :						Optional[int]					)       ->			Optional[Any]:
						for model_name in ["roberta-base", "roberta-large"]:
										with self.subTest(lowerCamelCase__					):
														lowercase    =			AutoConfig.from_pretrained(lowerCamelCase__					)
														self.assertIsNotNone(lowerCamelCase__					)
														self.assertIsInstance(lowerCamelCase__				,      lowerCamelCase__					)

														lowercase    =			FlaxAutoModel.from_pretrained(lowerCamelCase__					)
														self.assertIsNotNone(lowerCamelCase__					)
														self.assertIsInstance(lowerCamelCase__				,      lowerCamelCase__					)




		@slow
		def 					UpperCAmelCase__ (self   :						List[Any]					)       ->			Optional[Any]:
						for model_name in ["bert-base-cased", "bert-large-uncased"]:
										lowercase    =			AutoTokenizer.from_pretrained(lowerCamelCase__					)
										lowercase    =			FlaxBertModel.from_pretrained(lowerCamelCase__					)
										lowercase    =			tokenizer("Do you support jax jitted function?"				,      return_tensors=TensorType.JAX					)

										@jax.jit
										def eval(**A__   :						str					):
														return model(**lowerCamelCase__					)

										eval(**lowerCamelCase__					).block_until_ready()




		@slow
		def 					UpperCAmelCase__ (self   :						Union[str, Any]					)       ->			List[str]:
						for model_name in ["roberta-base", "roberta-large"]:
										lowercase    =			AutoTokenizer.from_pretrained(lowerCamelCase__					)
										lowercase    =			FlaxRobertaModel.from_pretrained(lowerCamelCase__					)
										lowercase    =			tokenizer("Do you support jax jitted function?"				,      return_tensors=TensorType.JAX					)

										@jax.jit
										def eval(**A__   :						Any					):
														return model(**lowerCamelCase__					)

										eval(**lowerCamelCase__					).block_until_ready()




		def 					UpperCAmelCase__ (self   :						str					)       ->			Union[str, Any]:
						with self.assertRaisesRegex(
						    lowerCamelCase__				,      "bert-base is not a local folder and is not a valid model identifier"					):
										lowercase    =			FlaxAutoModel.from_pretrained("bert-base"					)




		def 					UpperCAmelCase__ (self   :						List[str]					)       ->			List[str]:
						with self.assertRaisesRegex(
						    lowerCamelCase__				,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
										lowercase    =			FlaxAutoModel.from_pretrained(lowerCamelCase__				,      revision="aaaaaa"					)




		def 					UpperCAmelCase__ (self   :						List[str]					)       ->			List[Any]:
						with self.assertRaisesRegex(
						    lowerCamelCase__				,      "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"				,      ):
										lowercase    =			FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"					)





		def 					UpperCAmelCase__ (self   :						Optional[Any]					)       ->			str:
						with self.assertRaisesRegex(lowerCamelCase__				,      "Use `from_pt=True` to load this model"					):
										lowercase    =			FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"					)








"""simple docstring"""


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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
   raise Exception('requires fairseq >= 1.0.0a')

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

__snake_case    					=					'Hello world! cécé herlolip'


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							bool  ):
 lowercase__       :       int				=						FairseqRobertaModel.from_pretrained(lowerCamelCase__  )
 roberta.eval()  # disable dropout
 lowercase__       :       Tuple				=						roberta.model.encoder.sentence_encoder
 lowercase__       :       Tuple				=						XLMRobertaConfig(
     vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings       ,		hidden_size=roberta.cfg.model.encoder_embed_dim       ,		num_hidden_layers=roberta.cfg.model.encoder_layers       ,		num_attention_heads=roberta.cfg.model.encoder_attention_heads       ,		intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim       ,		max_position_embeddings=5_14       ,		type_vocab_size=1       ,		layer_norm_eps=1E-5       ,		)
 if classification_head:
  lowercase__       :       Any				=						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

 print("""Our RoBERTa config:"""       ,		lowerCamelCase__  )

 lowercase__       :       List[Any]				=						XLMRobertaXLForSequenceClassification(lowerCamelCase__  ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__  )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 lowercase__       :       int				=						roberta_sent_encoder.embed_tokens.weight
 lowercase__       :       Union[str, Any]				=						roberta_sent_encoder.embed_positions.weight
 lowercase__       :       int				=						torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.

 lowercase__       :       int				=						roberta_sent_encoder.layer_norm.weight
 lowercase__       :       List[Any]				=						roberta_sent_encoder.layer_norm.bias

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

  lowercase__       :       RobertaAttention				=						layer.attention
  lowercase__       :       str				=						roberta_layer.self_attn_layer_norm.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn_layer_norm.bias

  # self attention
  lowercase__       :       BertSelfAttention				=						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)  )
  )

  lowercase__       :       Optional[Any]				=						roberta_layer.self_attn.q_proj.weight
  lowercase__       :       str				=						roberta_layer.self_attn.q_proj.bias
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.weight
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.bias
  lowercase__       :       int				=						roberta_layer.self_attn.v_proj.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn.v_proj.bias

  # self-attention output
  lowercase__       :       BertSelfOutput				=						layer.attention.output
  assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  lowercase__       :       Any				=						roberta_layer.self_attn.out_proj.weight
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.out_proj.bias

  # this one is final layer norm
  lowercase__       :       Optional[Any]				=						roberta_layer.final_layer_norm.weight
  lowercase__       :       Any				=						roberta_layer.final_layer_norm.bias

  # intermediate
  lowercase__       :       BertIntermediate				=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Dict				=						roberta_layer.fca.weight
  lowercase__       :       Any				=						roberta_layer.fca.bias

  # output
  lowercase__       :       BertOutput				=						layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Union[str, Any]				=						roberta_layer.fca.weight
  lowercase__       :       Optional[Any]				=						roberta_layer.fca.bias
  # end of layer

 if classification_head:
  lowercase__       :       Optional[Any]				=						roberta.model.classification_heads["""mnli"""].dense.weight
  lowercase__       :       str				=						roberta.model.classification_heads["""mnli"""].dense.bias
  lowercase__       :       str				=						roberta.model.classification_heads["""mnli"""].out_proj.weight
  lowercase__       :       List[str]				=						roberta.model.classification_heads["""mnli"""].out_proj.bias
 else:
  # LM Head
  lowercase__       :       Tuple				=						roberta.model.encoder.lm_head.dense.weight
  lowercase__       :       int				=						roberta.model.encoder.lm_head.dense.bias
  lowercase__       :       Any				=						roberta.model.encoder.lm_head.layer_norm.weight
  lowercase__       :       Union[str, Any]				=						roberta.model.encoder.lm_head.layer_norm.bias
  lowercase__       :       Dict				=						roberta.model.encoder.lm_head.weight
  lowercase__       :       List[Any]				=						roberta.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 lowercase__       :       torch.Tensor				=						roberta.encode(lowerCamelCase__  ).unsqueeze(0  )  # batch of size 1

 lowercase__       :       Any				=						model(lowerCamelCase__  )[0]
 if classification_head:
  lowercase__       :       Optional[Any]				=						roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase__  )  )
 else:
  lowercase__       :       Tuple				=						roberta.model(lowerCamelCase__  )[0]
 print(our_output.shape       ,		their_output.shape  )
 lowercase__       :       Tuple				=						torch.max(torch.abs(our_output - their_output  )  ).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
 lowercase__       :       int				=						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__":
   __snake_case    					=					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.'
   )
   __snake_case    					=					parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )