code: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
      import jax
      import jax.numpy as jnp
  
      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel
  
  if is_torch_available():
      import torch
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase=14       ,					_lowerCamelCase=7       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase=False       ,					_lowerCamelCase=True       ,					_lowerCamelCase=99       ,					_lowerCamelCase=32       ,					_lowerCamelCase=5       ,					_lowerCamelCase=4       ,					_lowerCamelCase=37       ,					_lowerCamelCase="gelu"       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=512       ,					_lowerCamelCase=0.02       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						parent
          UpperCAmelCase__ :       str						=						batch_size
          UpperCAmelCase__ :       Dict						=						seq_length
          UpperCAmelCase__ :       Tuple						=						is_training
          UpperCAmelCase__ :       int						=						use_input_mask
          UpperCAmelCase__ :       Optional[Any]						=						use_token_type_ids
          UpperCAmelCase__ :       Union[str, Any]						=						use_labels
          UpperCAmelCase__ :       List[str]						=						vocab_size
          UpperCAmelCase__ :       Union[str, Any]						=						hidden_size
          UpperCAmelCase__ :       Optional[Any]						=						num_hidden_layers
          UpperCAmelCase__ :       Optional[int]						=						num_attention_heads
          UpperCAmelCase__ :       List[str]						=						intermediate_size
          UpperCAmelCase__ :       List[str]						=						hidden_act
          UpperCAmelCase__ :       int						=						hidden_dropout_prob
          UpperCAmelCase__ :       Dict						=						attention_probs_dropout_prob
          UpperCAmelCase__ :       Tuple						=						max_position_embeddings
          UpperCAmelCase__ :       Optional[int]						=						initializer_range
          UpperCAmelCase__ :       int						=						None
          UpperCAmelCase__ :       Any						=						vocab_size - 1
          UpperCAmelCase__ :       List[Any]						=						vocab_size - 1
          UpperCAmelCase__ :       Any						=						vocab_size - 1
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size  )
  
          UpperCAmelCase__ :       Tuple						=						None
          if self.use_input_mask:
                UpperCAmelCase__ :       int						=						random_attention_mask([self.batch_size, self.seq_length]  )
  
          UpperCAmelCase__ :       List[str]						=						GPTaConfig(
              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       ,					use_cache=_lowerCamelCase       ,					bos_token_id=self.bos_token_id       ,					eos_token_id=self.eos_token_id       ,					pad_token_id=self.pad_token_id       ,					)
  
          return (config, input_ids, input_mask)
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.prepare_config_and_inputs()
          UpperCAmelCase__ :       Dict						=						config_and_inputs
          UpperCAmelCase__ :       Tuple						=						{"""input_ids""": input_ids, """attention_mask""": attention_mask}
          return config, inputs_dict
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.prepare_config_and_inputs()
  
          UpperCAmelCase__ :       Optional[int]						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
          UpperCAmelCase__ :       List[str]						=						ids_tensor([self.batch_size, self.seq_length]       ,					vocab_size=2  )
  
          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						20
          UpperCAmelCase__ :       Any						=						model_class_name(_lowerCamelCase  )
  
          UpperCAmelCase__ :       int						=						model.init_cache(input_ids.shape[0]       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[Any]						=						jnp.ones((input_ids.shape[0], max_decoder_length)       ,					dtype="""i4"""  )
  
          UpperCAmelCase__ :       int						=						jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,					(input_ids.shape[0], input_ids.shape[-1] - 1)  )
          UpperCAmelCase__ :       Optional[Any]						=						model(
              input_ids[:, :-1]       ,					attention_mask=_lowerCamelCase       ,					past_key_values=_lowerCamelCase       ,					position_ids=_lowerCamelCase       ,					)
  
          UpperCAmelCase__ :       Union[str, Any]						=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,					dtype="""i4"""  )
          UpperCAmelCase__ :       List[str]						=						model(
              input_ids[:, -1:]       ,					attention_mask=_lowerCamelCase       ,					past_key_values=outputs_cache.past_key_values       ,					position_ids=_lowerCamelCase       ,					)
  
          UpperCAmelCase__ :       List[str]						=						model(_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[int]						=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
          self.parent.assertTrue(diff < 1e-3       ,					msg=F"""Max diff is {diff}"""  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						20
          UpperCAmelCase__ :       Tuple						=						model_class_name(_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]       ,					axis=-1       ,					)
  
          UpperCAmelCase__ :       Tuple						=						model.init_cache(input_ids.shape[0]       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       List[Any]						=						jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,					(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						model(
              input_ids[:, :-1]       ,					attention_mask=_lowerCamelCase       ,					past_key_values=_lowerCamelCase       ,					position_ids=_lowerCamelCase       ,					)
          UpperCAmelCase__ :       Any						=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,					dtype="""i4"""  )
          UpperCAmelCase__ :       List[str]						=						model(
              input_ids[:, -1:]       ,					past_key_values=outputs_cache.past_key_values       ,					attention_mask=_lowerCamelCase       ,					position_ids=_lowerCamelCase       ,					)
  
          UpperCAmelCase__ :       Optional[Any]						=						model(_lowerCamelCase       ,					attention_mask=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[int]						=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
          self.parent.assertTrue(diff < 1e-3       ,					msg=F"""Max diff is {diff}"""  )
  
  
  
  
  @require_flax
  class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE							=     (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						FlaxGPTaModelTester(self  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_class_name in self.all_model_classes:
                UpperCAmelCase__ :       Optional[Any]						=						self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_class_name in self.all_model_classes:
                UpperCAmelCase__ :       Tuple						=						self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						GPTaTokenizer.from_pretrained("""gpt2"""       ,					pad_token="""</s>"""       ,					padding_side="""left"""  )
          UpperCAmelCase__ :       Optional[Any]						=						tokenizer(["""Hello this is a long string""", """Hey"""]       ,					return_tensors="""np"""       ,					padding=_lowerCamelCase       ,					truncation=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""  )
          UpperCAmelCase__ :       Optional[int]						=						False
          UpperCAmelCase__ :       List[Any]						=						model.config.eos_token_id
  
          UpperCAmelCase__ :       Optional[int]						=						jax.jit(model.generate  )
  
          UpperCAmelCase__ :       Any						=						jit_generate(inputs["""input_ids"""]       ,					attention_mask=inputs["""attention_mask"""]  ).sequences
  
          UpperCAmelCase__ :       int						=						tokenizer.batch_decode(_lowerCamelCase       ,					skip_special_tokens=_lowerCamelCase  )
  
          UpperCAmelCase__ :       str						=						[
              """Hello this is a long string of words. I'm going to start with the first one.\n""",
              """Hey, I'm not sure if I'm going to be able to do""",
          ]
  
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    @is_pt_flax_cross_test
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__  ):
                      # prepare inputs
                      UpperCAmelCase__ :       Dict						=						self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       Tuple						=						{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
                      # load corresponding PyTorch class
                      UpperCAmelCase__ :       Dict						=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      UpperCAmelCase__ :       List[str]						=						getattr(_lowerCamelCase       ,					_lowerCamelCase  )
  
                      UpperCAmelCase__ :       Union[str, Any]						=						pt_inputs["""input_ids"""].shape
                      UpperCAmelCase__ :       Dict						=						np.random.randint(0       ,					seq_length - 1       ,					size=(batch_size,)  )
                      for batch_idx, start_index in enumerate(_lowerCamelCase  ):
                            UpperCAmelCase__ :       List[str]						=						0
                            UpperCAmelCase__ :       Dict						=						1
                            UpperCAmelCase__ :       Any						=						0
                            UpperCAmelCase__ :       List[Any]						=						1
                      UpperCAmelCase__ :       int						=						pt_model_class(_lowerCamelCase  ).eval()
                      UpperCAmelCase__ :       Union[str, Any]						=						model_class(_lowerCamelCase       ,					dtype=jnp.floataa  )
  
                      UpperCAmelCase__ :       str						=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						fx_state
  
                      with torch.no_grad():
                            UpperCAmelCase__ :       str						=						pt_model(**_lowerCamelCase  ).to_tuple()
  
                      UpperCAmelCase__ :       Tuple						=						fx_model(**_lowerCamelCase  ).to_tuple()
                      self.assertEqual(len(_lowerCamelCase  )       ,					len(_lowerCamelCase  )       ,					"""Output lengths differ between Flax and PyTorch"""  )
                      for fx_output, pt_output in zip(_lowerCamelCase       ,					_lowerCamelCase  ):
                            self.assert_almost_equals(fx_output[:, -1]       ,					pt_output[:, -1].numpy()       ,					4e-2  )
  
                      with tempfile.TemporaryDirectory() as tmpdirname:
                            pt_model.save_pretrained(_lowerCamelCase  )
                            UpperCAmelCase__ :       Optional[int]						=						model_class.from_pretrained(_lowerCamelCase       ,					from_pt=_lowerCamelCase  )
  
                      UpperCAmelCase__ :       Tuple						=						fx_model_loaded(**_lowerCamelCase  ).to_tuple()
                      self.assertEqual(
                          len(_lowerCamelCase  )       ,					len(_lowerCamelCase  )       ,					"""Output lengths differ between Flax and PyTorch"""  )
                      for fx_output_loaded, pt_output in zip(_lowerCamelCase       ,					_lowerCamelCase  ):
                            self.assert_almost_equals(fx_output_loaded[:, -1]       ,					pt_output[:, -1].numpy()       ,					4e-2  )
  
  
  
    @is_pt_flax_cross_test
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__  ):
                      # prepare inputs
                      UpperCAmelCase__ :       str						=						self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       Dict						=						{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
                      # load corresponding PyTorch class
                      UpperCAmelCase__ :       Optional[Any]						=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      UpperCAmelCase__ :       Dict						=						getattr(_lowerCamelCase       ,					_lowerCamelCase  )
  
                      UpperCAmelCase__ :       str						=						pt_model_class(_lowerCamelCase  ).eval()
                      UpperCAmelCase__ :       Dict						=						model_class(_lowerCamelCase       ,					dtype=jnp.floataa  )
  
                      UpperCAmelCase__ :       int						=						load_flax_weights_in_pytorch_model(_lowerCamelCase       ,					fx_model.params  )
                      UpperCAmelCase__ :       List[str]						=						pt_inputs["""input_ids"""].shape
                      UpperCAmelCase__ :       int						=						np.random.randint(0       ,					seq_length - 1       ,					size=(batch_size,)  )
                      for batch_idx, start_index in enumerate(_lowerCamelCase  ):
                            UpperCAmelCase__ :       Any						=						0
                            UpperCAmelCase__ :       Dict						=						1
                            UpperCAmelCase__ :       Union[str, Any]						=						0
                            UpperCAmelCase__ :       Optional[Any]						=						1
  
                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()
  
                      with torch.no_grad():
                            UpperCAmelCase__ :       Union[str, Any]						=						pt_model(**_lowerCamelCase  ).to_tuple()
  
                      UpperCAmelCase__ :       Union[str, Any]						=						fx_model(**_lowerCamelCase  ).to_tuple()
                      self.assertEqual(len(_lowerCamelCase  )       ,					len(_lowerCamelCase  )       ,					"""Output lengths differ between Flax and PyTorch"""  )
                      for fx_output, pt_output in zip(_lowerCamelCase       ,					_lowerCamelCase  ):
                            self.assert_almost_equals(fx_output[:, -1]       ,					pt_output[:, -1].numpy()       ,					4e-2  )
  
                      with tempfile.TemporaryDirectory() as tmpdirname:
                            fx_model.save_pretrained(_lowerCamelCase  )
                            UpperCAmelCase__ :       Optional[int]						=						pt_model_class.from_pretrained(_lowerCamelCase       ,					from_flax=_lowerCamelCase  )
  
                      with torch.no_grad():
                            UpperCAmelCase__ :       int						=						pt_model_loaded(**_lowerCamelCase  ).to_tuple()
  
                      self.assertEqual(
                          len(_lowerCamelCase  )       ,					len(_lowerCamelCase  )       ,					"""Output lengths differ between Flax and PyTorch"""  )
                      for fx_output, pt_output in zip(_lowerCamelCase       ,					_lowerCamelCase  ):
                            self.assert_almost_equals(fx_output[:, -1]       ,					pt_output[:, -1].numpy()       ,					4e-2  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_class_name in self.all_model_classes:
                UpperCAmelCase__ :       Optional[int]						=						model_class_name.from_pretrained("""gpt2"""       ,					from_pt=_lowerCamelCase  )
                UpperCAmelCase__ :       List[Any]						=						model(np.ones((1, 1)  )  )
                self.assertIsNotNone(_lowerCamelCase  )
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  