


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		)



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]