



"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
     import tensorflow as tf

if is_keras_nlp_available():
     from transformers.models.gpta import TFGPTaTokenizer


lowercase__		:				Tuple          =	["""gpt2"""]
lowercase__		:				Optional[Any]          =	"""gpt2"""

if is_tf_available():







     class       UpperCamelCase__   (	tf.Module  ):




           """simple docstring"""




           def __init__(      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
             super().__init__()
             lowerCAmelCase_   :				Union[str, Any]								= tokenizer
             lowerCAmelCase_   :				Tuple								= AutoConfig.from_pretrained(UpperCamelCase__       )
             lowerCAmelCase_   :				Tuple								= TFGPTaLMHeadModel.from_config(UpperCamelCase__       )





           @tf.function(input_signature=(tf.TensorSpec((None,)    ,			tf.string    ,			name='text'       ),)       )
           def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
             lowerCAmelCase_   :				Optional[int]								= self.tokenizer(UpperCamelCase__       )
             lowerCAmelCase_   :				List[str]								= tokenized['input_ids'].to_tensor()

             lowerCAmelCase_   :				Optional[int]								= tf.cast(input_ids_dense > 0    ,			tf.intaa       )
             # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

             lowerCAmelCase_   :				int								= self.model(input_ids=UpperCamelCase__    ,			attention_mask=UpperCamelCase__       )['logits']

             return outputs







@require_tf
@require_keras_nlp
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        super().setUp()

        lowerCAmelCase_   :				str								= [GPTaTokenizer.from_pretrained(UpperCamelCase__       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
        lowerCAmelCase_   :				Dict								= [TFGPTaTokenizer.from_pretrained(UpperCamelCase__       ) for checkpoint in TOKENIZER_CHECKPOINTS]
        assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

        lowerCAmelCase_   :				int								= [
            'This is a straightforward English test sentence.',
            'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
            'Now we\'re going to add some Chinese: 一 二 三 一二三',
            'And some much more rare Chinese: 齉 堃 齉堃',
            'Je vais aussi écrire en français pour tester les accents',
            'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
        ]
        lowerCAmelCase_   :				Optional[int]								= list(zip(self.test_sentences    ,			self.test_sentences[::-1]       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):



        for tokenizer, tf_tokenizer in zip(self.tokenizers    ,			self.tf_tokenizers       ):
          for test_inputs in self.test_sentences:
            lowerCAmelCase_   :				str								= tokenizer([test_inputs]    ,			return_tensors='tf'       )
            lowerCAmelCase_   :				Tuple								= tf_tokenizer([test_inputs]       )

            for key in python_outputs.keys():
              # convert them to numpy to avoid messing with ragged tensors
              lowerCAmelCase_   :				Any								= python_outputs[key].numpy()
              lowerCAmelCase_   :				List[str]								= tf_outputs[key].numpy()

              self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape       )       )
              self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__    ,			tf.intaa       ) == tf_outputs_values       )       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):



        for tf_tokenizer in self.tf_tokenizers:
          lowerCAmelCase_   :				str								= tf.function(UpperCamelCase__       )
          for test_inputs in self.test_sentences:
            lowerCAmelCase_   :				str								= tf.constant(UpperCamelCase__       )
            lowerCAmelCase_   :				Any								= compiled_tokenizer(UpperCamelCase__       )
            lowerCAmelCase_   :				List[Any]								= tf_tokenizer(UpperCamelCase__       )

            for key in eager_outputs.keys():
              self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):



        for tf_tokenizer in self.tf_tokenizers:
          lowerCAmelCase_   :				Union[str, Any]								= ModelToSave(tokenizer=UpperCamelCase__       )
          lowerCAmelCase_   :				Tuple								= tf.convert_to_tensor([self.test_sentences[0]]       )
          lowerCAmelCase_   :				int								= model.serving(UpperCamelCase__       )  # Build model with some sample inputs
          with TemporaryDirectory() as tempdir:
            lowerCAmelCase_   :				Union[str, Any]								= Path(UpperCamelCase__       ) / 'saved.model'
            tf.saved_model.save(UpperCamelCase__    ,			UpperCamelCase__    ,			signatures={'serving_default': model.serving}       )
            lowerCAmelCase_   :				Any								= tf.saved_model.load(UpperCamelCase__       )
          lowerCAmelCase_   :				int								= loaded_model.signatures['serving_default'](UpperCamelCase__       )['output_0']
          # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
          self.assertTrue(tf.reduce_all(out == loaded_output       )       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):



        for tf_tokenizer in self.tf_tokenizers:
          lowerCAmelCase_   :				str								= tf.convert_to_tensor([self.test_sentences[0]]       )
          lowerCAmelCase_   :				Optional[Any]								= tf_tokenizer(UpperCamelCase__       )  # Build model with some sample inputs

          lowerCAmelCase_   :				Optional[Any]								= tf_tokenizer.get_config()
          lowerCAmelCase_   :				int								= TFGPTaTokenizer.from_config(UpperCamelCase__       )
          lowerCAmelCase_   :				Optional[int]								= model_from_config(UpperCamelCase__       )

          for key in from_config_output.keys():
            self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]       )       )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):



        for tf_tokenizer in self.tf_tokenizers:
          # for the test to run
          lowerCAmelCase_   :				int								= 1_2_3_1_2_3

          for max_length in [3, 5, 1_0_2_4]:
            lowerCAmelCase_   :				List[Any]								= tf.convert_to_tensor([self.test_sentences[0]]       )
            lowerCAmelCase_   :				str								= tf_tokenizer(UpperCamelCase__    ,			max_length=UpperCamelCase__       )

            lowerCAmelCase_   :				Union[str, Any]								= out['input_ids'].numpy().shape[1]

            assert out_length == max_length











import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  max_position_embeddings=args.max_target_positions ,  num_layers=args.decoder_layers ,  attention_heads=args.decoder_attention_heads ,  ffn_dim=args.decoder_ffn_embed_dim ,  d_model=args.decoder_embed_dim ,  layerdrop=args.decoder_layerdrop ,  dropout=args.dropout ,  attention_dropout=args.attention_dropout ,  activation_dropout=args.activation_dropout ,  activation_function='''gelu''' ,  scale_embedding=not args.no_scale_embedding ,  tie_word_embeddings=args.share_decoder_input_output_embed ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




