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        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=14			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=True			,    _UpperCAmelCase=99			,    _UpperCAmelCase=32			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase=[[["global", "local"], 2]]			,    _UpperCAmelCase=37			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=512			,    _UpperCAmelCase=7			,    _UpperCAmelCase=0.0_2			,    ):
  
  
  
  
  
  
  
  													"""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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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=_UpperCAmelCase			,    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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		20
  													a_					=		model_class_name(_UpperCAmelCase		)
  
  													a_					=		model.init_cache(input_ids.shape[0]			,    _UpperCAmelCase		)
  													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=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    position_ids=_UpperCAmelCase			,    )
  
  													a_					=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
  													a_					=		model(
  													    input_ids[:, -1:]			,    attention_mask=_UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    position_ids=_UpperCAmelCase			,    )
  
  													a_					=		model(_UpperCAmelCase		)
  
  													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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		20
  													a_					=		model_class_name(_UpperCAmelCase		)
  
  													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]			,    _UpperCAmelCase		)
  													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=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    position_ids=_UpperCAmelCase			,    )
  													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=_UpperCAmelCase			,    position_ids=_UpperCAmelCase			,    )
  
  													a_					=		model(_UpperCAmelCase			,    attention_mask=_UpperCAmelCase		)
  
  													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        lowercase_				( UpperCamelCase__			,UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  							snake_case_        =		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		FlaxGPTNeoModelTester(self		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													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(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		)
  
  													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(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							@is_pt_flax_cross_test
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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(_UpperCAmelCase			,    _UpperCAmelCase		)
  																									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(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																									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(_UpperCAmelCase		):
  																															a_					=		0
  																															a_					=		1
  																															a_					=		0
  																															a_					=		1
  																									a_					=		pt_model_class(_UpperCAmelCase		).eval()
  																									a_					=		model_class(_UpperCAmelCase			,    dtype=jnp.floataa		)
  
  																									a_					=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,    _UpperCAmelCase		)
  																									a_					=		fx_state
  
  																									with torch.no_grad():
  																															a_					=		pt_model(**_UpperCAmelCase		).to_tuple()
  
  																									a_					=		fx_model(**_UpperCAmelCase		).to_tuple()
  																									self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
  																									for fx_output, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																															self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
  																									with tempfile.TemporaryDirectory() as tmpdirname:
  																															pt_model.save_pretrained(_UpperCAmelCase		)
  																															a_					=		model_class.from_pretrained(_UpperCAmelCase			,    from_pt=_UpperCAmelCase		)
  
  																									a_					=		fx_model_loaded(**_UpperCAmelCase		).to_tuple()
  																									self.assertEqual(
  																									    len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
  																									for fx_output_loaded, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																															self.assert_almost_equals(fx_output_loaded[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
  
  							@is_pt_flax_cross_test
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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(_UpperCAmelCase			,    _UpperCAmelCase		)
  																									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(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																									a_					=		pt_model_class(_UpperCAmelCase		).eval()
  																									a_					=		model_class(_UpperCAmelCase			,    dtype=jnp.floataa		)
  
  																									a_					=		load_flax_weights_in_pytorch_model(_UpperCAmelCase			,    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(_UpperCAmelCase		):
  																															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(**_UpperCAmelCase		).to_tuple()
  
  																									a_					=		fx_model(**_UpperCAmelCase		).to_tuple()
  																									self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
  																									for fx_output, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																															self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
  																									with tempfile.TemporaryDirectory() as tmpdirname:
  																															fx_model.save_pretrained(_UpperCAmelCase		)
  																															a_					=		pt_model_class.from_pretrained(_UpperCAmelCase			,    from_flax=_UpperCAmelCase		)
  
  																									with torch.no_grad():
  																															a_					=		pt_model_loaded(**_UpperCAmelCase		).to_tuple()
  
  																									self.assertEqual(
  																									    len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
  																									for fx_output, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																															self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""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(_UpperCAmelCase		)
style_context: 
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
  																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
  													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
  													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]