code: 
  
  
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import CTRLConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
        import tensorflow as tf
  
        from transformers.models.ctrl.modeling_tf_ctrl import (
            TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCTRLForSequenceClassification,
            TFCTRLLMHeadModel,
            TFCTRLModel,
        )
  
  class      UpperCAmelCase_						(__SCREAMING_SNAKE_CASE						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[str]              =	parent
           __lowerCamelCase     :			str              =	13
           __lowerCamelCase     :			Any              =	7
           __lowerCamelCase     :			str              =	True
           __lowerCamelCase     :			int              =	True
           __lowerCamelCase     :			Optional[Any]              =	True
           __lowerCamelCase     :			Optional[Any]              =	True
           __lowerCamelCase     :			str              =	True
           __lowerCamelCase     :			Any              =	99
           __lowerCamelCase     :			int              =	32
           __lowerCamelCase     :			str              =	2
           __lowerCamelCase     :			Optional[Any]              =	4
           __lowerCamelCase     :			str              =	37
           __lowerCamelCase     :			Optional[int]              =	'''gelu'''
           __lowerCamelCase     :			List[Any]              =	0.1
           __lowerCamelCase     :			Dict              =	0.1
           __lowerCamelCase     :			str              =	5_12
           __lowerCamelCase     :			List[str]              =	16
           __lowerCamelCase     :			Tuple              =	2
           __lowerCamelCase     :			List[str]              =	0.0_2
           __lowerCamelCase     :			Optional[int]              =	3
           __lowerCamelCase     :			Optional[int]              =	4
           __lowerCamelCase     :			List[str]              =	None
           __lowerCamelCase     :			List[str]              =	self.vocab_size - 1
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			Union[str, Any]              =	ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size			)
  
           __lowerCamelCase     :			Optional[Any]              =	None
           if self.use_input_mask:
                __lowerCamelCase     :			List[str]              =	random_attention_mask([self.batch_size, self.seq_length]			)
  
           __lowerCamelCase     :			str              =	None
           if self.use_token_type_ids:
                __lowerCamelCase     :			str              =	ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size			)
  
           __lowerCamelCase     :			Optional[int]              =	None
           if self.use_mc_token_ids:
                __lowerCamelCase     :			List[str]              =	ids_tensor([self.batch_size, self.num_choices]    ,  self.seq_length			)
  
           __lowerCamelCase     :			Dict              =	None
           __lowerCamelCase     :			Any              =	None
           __lowerCamelCase     :			str              =	None
           if self.use_labels:
                __lowerCamelCase     :			Optional[Any]              =	ids_tensor([self.batch_size]    ,  self.type_sequence_label_size			)
                __lowerCamelCase     :			Optional[int]              =	ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels			)
                __lowerCamelCase     :			List[str]              =	ids_tensor([self.batch_size]    ,  self.num_choices			)
  
           __lowerCamelCase     :			List[Any]              =	CTRLConfig(
               vocab_size=self.vocab_size    ,  n_embd=self.hidden_size    ,  n_layer=self.num_hidden_layers    ,  n_head=self.num_attention_heads    ,  n_positions=self.max_position_embeddings    ,  pad_token_id=self.pad_token_id    ,  )
  
           __lowerCamelCase     :			List[Any]              =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,  2			)
  
           return (
               config,
               input_ids,
               input_mask,
               head_mask,
               token_type_ids,
               mc_token_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  *SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           __lowerCamelCase     :			Tuple              =	TFCTRLModel(config=UpperCamelCase__			)
           __lowerCamelCase     :			Union[str, Any]              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           __lowerCamelCase     :			Optional[int]              =	model(UpperCamelCase__			)
  
           __lowerCamelCase     :			Optional[Any]              =	[input_ids, None, input_mask]  # None is the input for 'past'
           __lowerCamelCase     :			str              =	model(UpperCamelCase__			)
  
           __lowerCamelCase     :			Optional[Any]              =	model(UpperCamelCase__			)
  
           self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  *SCREAMING_SNAKE_CASE_			)       ->  List[Any]:
           __lowerCamelCase     :			Union[str, Any]              =	TFCTRLLMHeadModel(config=UpperCamelCase__			)
           __lowerCamelCase     :			int              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           __lowerCamelCase     :			Any              =	model(UpperCamelCase__			)
           self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  *SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           __lowerCamelCase     :			int              =	self.num_labels
           __lowerCamelCase     :			Optional[int]              =	ids_tensor([self.batch_size]    ,  self.type_sequence_label_size			)
           __lowerCamelCase     :			Any              =	{
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''labels''': sequence_labels,
           }
           __lowerCamelCase     :			Dict              =	TFCTRLForSequenceClassification(UpperCamelCase__			)
           __lowerCamelCase     :			int              =	model(UpperCamelCase__			)
           self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase     :			Union[str, Any]              =	self.prepare_config_and_inputs()
  
           (
               __lowerCamelCase
           )     :			str              =	config_and_inputs
  
           __lowerCamelCase     :			Any              =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      List[Any]							  =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
      lowerCamelCase						:      int							  =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
      lowerCamelCase						:      str							  =    (
          {
              'feature-extraction': TFCTRLModel,
              'text-classification': TFCTRLForSequenceClassification,
              'text-generation': TFCTRLLMHeadModel,
              'zero-shot': TFCTRLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      lowerCamelCase						:      Union[str, Any]							  =    False
      lowerCamelCase						:      List[str]							  =    False
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  str:
  
  
  
  
  
  
  
           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
                # config could not be created.
                return True
  
           return False
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			str              =	TFCTRLModelTester(self			)
           __lowerCamelCase     :			Dict              =	ConfigTester(self    ,  config_class=UpperCamelCase__    ,  n_embd=37			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           self.config_tester.run_common_tests()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			Optional[int]              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Union[str, Any]              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase     :			Dict              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[Any]:
           __lowerCamelCase     :			Union[str, Any]              =	self.model_tester.prepare_config_and_inputs_for_common()
           __lowerCamelCase     :			int              =	[TFCTRLLMHeadModel]
           __lowerCamelCase     :			Tuple              =	[TFCTRLForSequenceClassification]
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			List[Any]              =	model_class(UpperCamelCase__			)
                model.build()  # may be needed for the get_bias() call below
                assert isinstance(model.get_input_embeddings()    ,  tf.keras.layers.Layer			)
  
                if model_class in list_lm_models:
                     __lowerCamelCase     :			Optional[Any]              =	model.get_output_embeddings()
                     assert isinstance(UpperCamelCase__    ,  tf.keras.layers.Layer			)
                     __lowerCamelCase     :			Any              =	model.get_bias()
                     assert isinstance(UpperCamelCase__    ,  UpperCamelCase__			)
                     for k, v in name.items():
                          assert isinstance(UpperCamelCase__    ,  tf.Variable			)
                elif model_class in list_other_models_with_output_ebd:
                     __lowerCamelCase     :			List[Any]              =	model.get_output_embeddings()
                     assert isinstance(UpperCamelCase__    ,  tf.keras.layers.Layer			)
                     __lowerCamelCase     :			int              =	model.get_bias()
                     assert name is None
                else:
                     __lowerCamelCase     :			Tuple              =	model.get_output_embeddings()
                     assert x is None
                     __lowerCamelCase     :			Dict              =	model.get_bias()
                     assert name is None
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  Optional[int]:
  
  
  
  
  
  
  
           for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCamelCase     :			str              =	TFCTRLModel.from_pretrained(UpperCamelCase__			)
                self.assertIsNotNone(UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			Optional[Any]              =	TFCTRLLMHeadModel.from_pretrained('ctrl'			)
           __lowerCamelCase     :			Optional[Any]              =	tf.convert_to_tensor([[1_18_59, 0, 16_11, 8]]    ,  dtype=tf.intaa			)  # Legal the president is
           __lowerCamelCase     :			Dict              =	[
               1_18_59,
               0,
               16_11,
               8,
               5,
               1_50,
               2_64_49,
               2,
               19,
               3_48,
               4_69,
               3,
               25_95,
               48,
               2_07_40,
               24_65_33,
               24_65_33,
               19,
               30,
               5,
           ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
           __lowerCamelCase     :			int              =	model.generate(UpperCamelCase__    ,  do_sample=UpperCamelCase__			)
           self.assertListEqual(output_ids[0].numpy().tolist()    ,  UpperCamelCase__			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''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 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__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
       __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
       roberta.eval()  # disable dropout
       __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
       __lowerCamelCase     :			Optional[Any]              =	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=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
       if classification_head:
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'   ,					UpperCAmelCase_							)
  
       __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
       __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
       __lowerCamelCase     :			Tuple              =	torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
       __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
       __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias
  
       for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
            __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]
  
            # self attention
            __lowerCamelCase     :			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)							)
            )
  
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias
  
            # self-attention output
            __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias
  
            # intermediate
            __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias
  
            # output
            __lowerCamelCase     :			BertOutput              =	layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			str              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
            __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
            __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
            __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
       __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
       if classification_head:
            __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
       else:
            __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
       print(our_output.shape   ,					their_output.shape							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
       print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
       __lowerCamelCase     :			List[str]              =	torch.allclose(UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=1e-3							)
       print('Do both models output the same tensors?'   ,					'🔥' if success else '💩'							)
       if not success:
            raise Exception('Something went wRoNg'							)
  
       pathlib.Path(UpperCAmelCase_							).mkdir(parents=UpperCAmelCase_   ,					exist_ok=UpperCAmelCase_							)
       print(F'Saving model to {pytorch_dump_folder_path}'							)
       model.save_pretrained(UpperCAmelCase_							)
  
  
  if __name__ == "__main__":
        A__	:      Optional[Any]				=						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__	:      Optional[Any]				=						parser.parse_args()
        convert_roberta_checkpoint_to_pytorch(
            args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
        )
  
  
  
  
  
  
  