

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def 	snake_case_      (__A     :						Dict     )			->	List[Any]:
   __lowerCAmelCase  :			int					    =						[
       """encoder.version""",
       """decoder.version""",
       """model.encoder.version""",
       """model.decoder.version""",
       """_float_tensor""",
       """decoder.output_projection.weight""",
   ]
   for k in ignore_keys:
      state_dict.pop(__A					,   __A     )





def 	snake_case_      (__A     :						Any     )			->	Union[str, Any]:
   __lowerCAmelCase    ,__lowerCAmelCase  :			Dict					    =						emb.weight.shape
   __lowerCAmelCase  :			List[Any]					    =						nn.Linear(__A					,   __A					,   bias=__A     )
   __lowerCAmelCase  :			str					    =						emb.weight.data
   return lin_layer





def 	snake_case_      (__A     :						str					,   __A     :						Dict="uclanlp/plbart-base"					,   __A     :						Optional[Any]=False					,   __A     :						Optional[int]=False     )			->	Union[str, Any]:
   __lowerCAmelCase  :			Any					    =						torch.load(__A					,   map_location="""cpu"""     )["""model"""]
   remove_ignore_keys_(__A     )
   __lowerCAmelCase  :			List[str]					    =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

   __lowerCAmelCase  :			Optional[Any]					    =						PLBartConfig.from_pretrained(__A					,   vocab_size=__A     )

   __lowerCAmelCase  :			Any					    =						state_dict["""decoder.embed_tokens.weight"""]
   if not classification:
      __lowerCAmelCase  :			Any					    =						PLBartForConditionalGeneration(__A     )
      model.model.load_state_dict(__A     )
      if finetuned:
         __lowerCAmelCase  :			List[Any]					    =						make_linear_from_emb(model.model.shared     )

   else:
      __lowerCAmelCase  :			Any					    =						{}
      for key, value in state_dict.copy().items():
         if key.startswith("""classification_heads.sentence_classification_head"""     ):
            __lowerCAmelCase  :			Tuple					    =						value
            state_dict.pop(__A     )
      __lowerCAmelCase  :			str					    =						PLBartForSequenceClassification(__A     )
      model.model.load_state_dict(__A     )
      model.classification_head.load_state_dict(__A     )

   return model


if __name__ == "__main__":
   __UpperCAmelCase      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument(
       """--hf_config""",
       default="""uclanlp/plbart-base""",
       type=str,
       help="""Which huggingface architecture to use: plbart-base""",
   )
   parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
   parser.add_argument(
       """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
   )
   __UpperCAmelCase      = parser.parse_args()
   __UpperCAmelCase      = convert_fairseq_plbart_checkpoint_from_disk(
       args.fairseq_path,
       hf_config_path=args.hf_config,
       finetuned=args.finetuned,
       classification=args.classification,
   )
   model.save_pretrained(args.pytorch_dump_folder_path)








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 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		A  ,		)  ->    List[Any]:



          '''simple docstring'''



          a      =      parent
          a      =      13
          a      =      7
          a      =      True
          a      =      True
          a      =      True
          a      =      True
          a      =      True
          a      =      99
          a      =      32
          a      =      2
          a      =      4
          a      =      37
          a      =      "gelu"
          a      =      0.1
          a      =      0.1
          a      =      512
          a      =      16
          a      =      2
          a      =      0.0_2
          a      =      3
          a      =      4
          a      =      None
          a      =      self.vocab_size - 1







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



          '''simple docstring'''



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

          a      =      None
          if self.use_input_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      =      None
          if self.use_mc_token_ids:
               a      =      ids_tensor([self.batch_size, self.num_choices]  ,		self.seq_length )

          a      =      None
          a      =      None
          a      =      None
          if self.use_labels:
               a      =      ids_tensor([self.batch_size]  ,		self.type_sequence_label_size )
               a      =      ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels )
               a      =      ids_tensor([self.batch_size]  ,		self.num_choices )

          a      =      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  ,		)

          a      =      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 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		*A )  ->    Optional[int]:



          '''simple docstring'''



          a      =      TFCTRLModel(config=A )
          a      =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          a      =      model(A )

          a      =      [input_ids, None, input_mask]  # None is the input for 'past'
          a      =      model(A )

          a      =      model(A )

          self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		*A )  ->    Tuple:



          '''simple docstring'''



          a      =      TFCTRLLMHeadModel(config=A )
          a      =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          a      =      model(A )
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		*A )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.num_labels
          a      =      ids_tensor([self.batch_size]  ,		self.type_sequence_label_size )
          a      =      {
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
              "labels": sequence_labels,
          }
          a      =      TFCTRLForSequenceClassification(A )
          a      =      model(A )
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels) )







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



          '''simple docstring'''



          a      =      self.prepare_config_and_inputs()

          (
              (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      (
              a
          )  ,      
          )      =      config_and_inputs

          a      =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict







@require_tf
class 			a__       (					UpperCamelCase__   ,					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Any							=	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
     a		:			Optional[Any]							=	(TFCTRLLMHeadModel,) if is_tf_available() else ()
     a		:			List[Any]							=	(
         {
             """feature-extraction""": TFCTRLModel,
             """text-classification""": TFCTRLForSequenceClassification,
             """text-generation""": TFCTRLLMHeadModel,
             """zero-shot""": TFCTRLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     a		:			Union[str, Any]							=	False
     a		:			str							=	False







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



          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 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      TFCTRLModelTester(self )
          a      =      ConfigTester(self  ,		config_class=A  ,		n_embd=37 )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_model(*A )







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_lm_head(*A )







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_for_sequence_classification(*A )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a  ,      a      =      self.model_tester.prepare_config_and_inputs_for_common()
          a      =      [TFCTRLLMHeadModel]
          a      =      [TFCTRLForSequenceClassification]

          for model_class in self.all_model_classes:
               a      =      model_class(A )
               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:
                    a      =      model.get_output_embeddings()
                    assert isinstance(A  ,		tf.keras.layers.Layer )
                    a      =      model.get_bias()
                    assert isinstance(A  ,		A )
                    for k, v in name.items():
                         assert isinstance(A  ,		tf.Variable )
               elif model_class in list_other_models_with_output_ebd:
                    a      =      model.get_output_embeddings()
                    assert isinstance(A  ,		tf.keras.layers.Layer )
                    a      =      model.get_bias()
                    assert name is None
               else:
                    a      =      model.get_output_embeddings()
                    assert x is None
                    a      =      model.get_bias()
                    assert name is None







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a      =      TFCTRLModel.from_pretrained(A )
               self.assertIsNotNone(A )







@require_tf
class 			a__       (					unittest.TestCase      ):







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      TFCTRLLMHeadModel.from_pretrained("ctrl" )
          a      =      tf.convert_to_tensor([[11859, 0, 1611, 8]]  ,		dtype=tf.intaa )  # Legal the president is
          a      =      [
              11859,
              0,
              1611,
              8,
              5,
              150,
              26449,
              2,
              19,
              348,
              469,
              3,
              2595,
              48,
              20740,
              246533,
              246533,
              19,
              30,
              5,
          ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

          a      =      model.generate(A  ,		do_sample=A )
          self.assertListEqual(output_ids[0].numpy().tolist()  ,		A )

