code: 
  
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=14,					lowercase_=7,					lowercase_=True,					lowercase_=True,					lowercase_=False,					lowercase_=True,					lowercase_=99,					lowercase_=32,					lowercase_=4,					lowercase_=4,					lowercase_=[[["global", "local"], 2]],					lowercase_=37,					lowercase_="gelu",					lowercase_=0.1,					lowercase_=0.1,					lowercase_=512,					lowercase_=7,					lowercase_=0.02,					)							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=parent
       a__						=batch_size
       a__						=seq_length
       a__						=is_training
       a__						=use_input_mask
       a__						=use_token_type_ids
       a__						=use_labels
       a__						=vocab_size
       a__						=hidden_size
       a__						=num_hidden_layers
       a__						=num_attention_heads
       a__						=attention_types
       a__						=intermediate_size
       a__						=hidden_act
       a__						=hidden_dropout_prob
       a__						=attention_probs_dropout_prob
       a__						=max_position_embeddings
       a__						=window_size
       a__						=initializer_range
       a__						=None
       a__						=vocab_size - 1
       a__						=vocab_size - 1
       a__						=vocab_size - 1
  
  
  
     def 				_UpperCAmelCase				(       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__						=GPTNeoConfig(
           vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_layers=self.num_hidden_layers,					num_heads=self.num_attention_heads,					max_position_embeddings=self.max_position_embeddings,					use_cache=lowercase_,					bos_token_id=self.bos_token_id,					eos_token_id=self.eos_token_id,					pad_token_id=self.pad_token_id,					window_size=self.window_size,					attention_types=self.attention_types,					)
  
       return (config, input_ids, input_mask)
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.prepare_config_and_inputs()
       a__,   a__,   a__						=config_and_inputs
       a__						={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=20
       a__						=model_class_name(lowercase_ )
  
       a__						=model.init_cache(input_ids.shape[0],					lowercase_ )
       a__						=jnp.ones((input_ids.shape[0], max_decoder_length),					dtype='''i4''' )
  
       a__						=jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1 )[None, :],					(input_ids.shape[0], input_ids.shape[-1] - 1) )
       a__						=model(
           input_ids[:, :-1],					attention_mask=lowercase_,					past_key_values=lowercase_,					position_ids=lowercase_,					)
  
       a__						=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],					dtype='''i4''' )
       a__						=model(
           input_ids[:, -1:],					attention_mask=lowercase_,					past_key_values=outputs_cache.past_key_values,					position_ids=lowercase_,					)
  
       a__						=model(lowercase_ )
  
       a__						=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 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=20
       a__						=model_class_name(lowercase_ )
  
       a__						=jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )],					axis=-1,					)
  
       a__						=model.init_cache(input_ids.shape[0],					lowercase_ )
       a__						=jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1 )[None, :],					(input_ids.shape[0], input_ids.shape[-1] - 1) )
  
       a__						=model(
           input_ids[:, :-1],					attention_mask=lowercase_,					past_key_values=lowercase_,					position_ids=lowercase_,					)
       a__						=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],					dtype='''i4''' )
       a__						=model(
           input_ids[:, -1:],					past_key_values=outputs_cache.past_key_values,					attention_mask=lowercase_,					position_ids=lowercase_,					)
  
       a__						=model(lowercase_,					attention_mask=lowercase_ )
  
       a__						=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 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     lowerCamelCase__				:			int												=     (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=FlaxGPTNeoModelTester(self )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for model_class_name in self.all_model_classes:
         a__,   a__,   a__						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(lowercase_,					lowercase_,					lowercase_,					lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for model_class_name in self.all_model_classes:
         a__,   a__,   a__						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             lowercase_,					lowercase_,					lowercase_,					lowercase_ )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=GPTaTokenizer.from_pretrained('''gpt2''',					pad_token='''<|endoftext|>''',					padding_side='''left''' )
       a__						=tokenizer(['''Hello this is a long string''', '''Hey'''],					return_tensors='''np''',					padding=lowercase_,					truncation=lowercase_ )
  
       a__						=FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
       a__						=False
       a__						=model.config.eos_token_id
  
       a__						=jax.jit(model.generate )
  
       a__						=jit_generate(
           inputs['''input_ids'''],					attention_mask=inputs['''attention_mask'''],					pad_token_id=tokenizer.pad_token_id ).sequences
  
       a__						=tokenizer.batch_decode(lowercase_,					skip_special_tokens=lowercase_ )
  
       a__						=[
           '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
           '''Hey, I\'m a little late to the party. I\'m going to''',
       ]
  
       self.assertListEqual(lowercase_,					lowercase_ )
  
  
  
     @is_pt_flax_cross_test
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__,   a__						=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
           a__						=self._prepare_for_class(lowercase_,					lowercase_ )
           a__						={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
  
           # load corresponding PyTorch class
           a__						=model_class.__name__[4:]  # Skip the "Flax" at the beginning
           a__						=getattr(lowercase_,					lowercase_ )
  
           a__,   a__						=pt_inputs['''input_ids'''].shape
           a__						=np.random.randint(0,					seq_length - 1,					size=(batch_size,) )
           for batch_idx, start_index in enumerate(lowercase_ ):
             a__						=0
             a__						=1
             a__						=0
             a__						=1
           a__						=pt_model_class(lowercase_ ).eval()
           a__						=model_class(lowercase_,					dtype=jnp.floataa )
  
           a__						=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),					lowercase_ )
           a__						=fx_state
  
           with torch.no_grad():
             a__						=pt_model(**lowercase_ ).to_tuple()
  
           a__						=fx_model(**lowercase_ ).to_tuple()
           self.assertEqual(len(lowercase_ ),					len(lowercase_ ),					'''Output lengths differ between Flax and PyTorch''' )
           for fx_output, pt_output in zip(lowercase_,					lowercase_ ):
             self.assert_almost_equals(fx_output[:, -1],					pt_output[:, -1].numpy(),					4E-2 )
  
           with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(lowercase_ )
             a__						=model_class.from_pretrained(lowercase_,					from_pt=lowercase_ )
  
           a__						=fx_model_loaded(**lowercase_ ).to_tuple()
           self.assertEqual(
               len(lowercase_ ),					len(lowercase_ ),					'''Output lengths differ between Flax and PyTorch''' )
           for fx_output_loaded, pt_output in zip(lowercase_,					lowercase_ ):
             self.assert_almost_equals(fx_output_loaded[:, -1],					pt_output[:, -1].numpy(),					4E-2 )
  
  
  
     @is_pt_flax_cross_test
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__,   a__						=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
           a__						=self._prepare_for_class(lowercase_,					lowercase_ )
           a__						={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
  
           # load corresponding PyTorch class
           a__						=model_class.__name__[4:]  # Skip the "Flax" at the beginning
           a__						=getattr(lowercase_,					lowercase_ )
  
           a__						=pt_model_class(lowercase_ ).eval()
           a__						=model_class(lowercase_,					dtype=jnp.floataa )
  
           a__						=load_flax_weights_in_pytorch_model(lowercase_,					fx_model.params )
           a__,   a__						=pt_inputs['''input_ids'''].shape
           a__						=np.random.randint(0,					seq_length - 1,					size=(batch_size,) )
           for batch_idx, start_index in enumerate(lowercase_ ):
             a__						=0
             a__						=1
             a__						=0
             a__						=1
  
           # make sure weights are tied in PyTorch
           pt_model.tie_weights()
  
           with torch.no_grad():
             a__						=pt_model(**lowercase_ ).to_tuple()
  
           a__						=fx_model(**lowercase_ ).to_tuple()
           self.assertEqual(len(lowercase_ ),					len(lowercase_ ),					'''Output lengths differ between Flax and PyTorch''' )
           for fx_output, pt_output in zip(lowercase_,					lowercase_ ):
             self.assert_almost_equals(fx_output[:, -1],					pt_output[:, -1].numpy(),					4E-2 )
  
           with tempfile.TemporaryDirectory() as tmpdirname:
             fx_model.save_pretrained(lowercase_ )
             a__						=pt_model_class.from_pretrained(lowercase_,					from_flax=lowercase_ )
  
           with torch.no_grad():
             a__						=pt_model_loaded(**lowercase_ ).to_tuple()
  
           self.assertEqual(
               len(lowercase_ ),					len(lowercase_ ),					'''Output lengths differ between Flax and PyTorch''' )
           for fx_output, pt_output in zip(lowercase_,					lowercase_ ):
             self.assert_almost_equals(fx_output[:, -1],					pt_output[:, -1].numpy(),					4E-2 )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for model_class_name in self.all_model_classes:
         a__						=model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
         a__						=model(np.ones((1, 1) ) )
         self.assertIsNotNone(lowercase_ )
  
style_context: 
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
         from .tokenization_xglm import XGLMTokenizer
  else:
         lowerCamelCase     =      None
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
      },
      '''tokenizer_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
      },
  }
  
  lowerCamelCase     =      {
      '''facebook/xglm-564M''': 2048,
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
     lowerCamelCase__				:			Any												=     XGLMTokenizer
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=7
       a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
  
       a__						=kwargs.get('''additional_special_tokens''',					[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)
  
       a__						=vocab_file
       a__						=False if not self.vocab_file else True
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       a__						=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
  
       if token_ids_a is None:
         return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
         copyfile(self.vocab_file,					lowercase_ )
  
       return (out_vocab_file,)
  