
'''simple docstring'''







import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
					import numpy as np

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
					import torch
@require_flax
class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self  ,    __UpperCamelCase  ,    __UpperCamelCase=14  ,    __UpperCamelCase=7  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=99  ,    __UpperCamelCase=32  ,    __UpperCamelCase=5  ,    __UpperCamelCase=4  ,    __UpperCamelCase=37  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=512  ,    __UpperCamelCase=0.0_2  ,    __UpperCamelCase=None  ,    ):



					'''simple docstring'''






					__a       :     List[Any]								=	parent
					__a       :     Tuple								=	batch_size
					__a       :     Any								=	seq_length
					__a       :     int								=	is_training
					__a       :     List[str]								=	use_input_mask
					__a       :     Any								=	use_labels
					__a       :     Union[str, Any]								=	vocab_size
					__a       :     Optional[Any]								=	d_model
					__a       :     Optional[Any]								=	num_hidden_layers
					__a       :     str								=	num_attention_heads
					__a       :     Optional[Any]								=	ffn_dim
					__a       :     List[Any]								=	activation_function
					__a       :     Any								=	activation_dropout
					__a       :     Tuple								=	attention_dropout
					__a       :     List[str]								=	max_position_embeddings
					__a       :     Any								=	initializer_range
					__a       :     str								=	None
					__a       :     Any								=	0
					__a       :     Tuple								=	2
					__a       :     int								=	1
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Any								=	np.clip(ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size			)  ,    3  ,    self.vocab_size			)

					__a       :     List[Any]								=	None
					if self.use_input_mask:
									__a       :     Dict								=	random_attention_mask([self.batch_size, self.seq_length]			)

					__a       :     Optional[Any]								=	XGLMConfig(
					    vocab_size=self.vocab_size  ,    d_model=self.hidden_size  ,    num_layers=self.num_hidden_layers  ,    attention_heads=self.num_attention_heads  ,    ffn_dim=self.ffn_dim  ,    activation_function=self.activation_function  ,    activation_dropout=self.activation_dropout  ,    attention_dropout=self.attention_dropout  ,    max_position_embeddings=self.max_position_embeddings  ,    initializer_range=self.initializer_range  ,    use_cache=__UpperCamelCase  ,    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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Tuple								=	self.prepare_config_and_inputs()
					__a							,	__a							,	__a       :     List[Any]								=	config_and_inputs
					__a       :     str								=	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a							,	__a							,	__a       :     Any								=	self.prepare_config_and_inputs()

					__a       :     Any								=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
					__a       :     Any								=	ids_tensor([self.batch_size, self.seq_length]  ,    vocab_size=2			)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	20
					__a       :     Tuple								=	model_class_name(__UpperCamelCase			)

					__a       :     Dict								=	model.init_cache(input_ids.shape[0]  ,    __UpperCamelCase			)
					__a       :     Union[str, Any]								=	jnp.ones((input_ids.shape[0], max_decoder_length)  ,    dtype="""i4"""			)

					__a       :     Optional[int]								=	jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)
					__a       :     Any								=	model(
					    input_ids[:, :-1]  ,    attention_mask=__UpperCamelCase  ,    past_key_values=__UpperCamelCase  ,    position_ids=__UpperCamelCase  ,    )

					__a       :     str								=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype="""i4"""			)
					__a       :     Dict								=	model(
					    input_ids[:, -1:]  ,    attention_mask=__UpperCamelCase  ,    past_key_values=outputs_cache.past_key_values  ,    position_ids=__UpperCamelCase  ,    )

					__a       :     Union[str, Any]								=	model(__UpperCamelCase			)

					__a       :     Optional[Any]								=	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 __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	20
					__a       :     int								=	model_class_name(__UpperCamelCase			)

					__a       :     Optional[Any]								=	jnp.concatenate(
					    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]  ,    axis=-1  ,    )

					__a       :     str								=	model.init_cache(input_ids.shape[0]  ,    __UpperCamelCase			)
					__a       :     Dict								=	jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)

					__a       :     Any								=	model(
					    input_ids[:, :-1]  ,    attention_mask=__UpperCamelCase  ,    past_key_values=__UpperCamelCase  ,    position_ids=__UpperCamelCase  ,    )
					__a       :     str								=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype="""i4"""			)
					__a       :     List[Any]								=	model(
					    input_ids[:, -1:]  ,    past_key_values=outputs_cache.past_key_values  ,    attention_mask=__UpperCamelCase  ,    position_ids=__UpperCamelCase  ,    )

					__a       :     List[str]								=	model(__UpperCamelCase  ,    attention_mask=__UpperCamelCase			)
					__a       :     Dict								=	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_sentencepiece
@require_flax
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					__UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
	lowercase__	    =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Any								=	FlaxXGLMModelTester(self			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					for model_class_name in self.all_model_classes:
									__a							,	__a							,	__a       :     Tuple								=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					for model_class_name in self.all_model_classes:
									__a							,	__a							,	__a       :     int								=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	XGLMTokenizer.from_pretrained("""XGLM"""  ,    padding_side="""left"""			)
					__a       :     int								=	tokenizer(["""Hello this is a long string""", """Hey"""]  ,    return_tensors="""np"""  ,    padding=__UpperCamelCase  ,    truncation=__UpperCamelCase			)

					__a       :     Any								=	FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
					__a       :     Tuple								=	1
					__a       :     Optional[int]								=	False

					__a       :     Tuple								=	jax.jit(model.generate			)

					__a       :     int								=	jit_generate(inputs["""input_ids"""]  ,    attention_mask=inputs["""attention_mask"""]			).sequences

					__a       :     Dict								=	tokenizer.batch_decode(__UpperCamelCase  ,    skip_special_tokens=__UpperCamelCase			)

					__a       :     Union[str, Any]								=	[
					    """Hello this is a long string of questions, but I'm not sure if I'm""",
					    """Hey, I'm a newbie to the forum and I'""",
					]

					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@is_pt_flax_cross_test
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a							,	__a       :     str								=	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       :     List[str]								=	self._prepare_for_class(__UpperCamelCase  ,    __UpperCamelCase			)
													__a       :     List[Any]								=	{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													__a       :     Dict								=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
													__a       :     int								=	getattr(__UpperCamelCase  ,    __UpperCamelCase			)

													__a							,	__a       :     Dict								=	pt_inputs["""input_ids"""].shape
													__a       :     int								=	np.random.randint(0  ,    seq_length - 1  ,    size=(batch_size,)			)
													for batch_idx, start_index in enumerate(__UpperCamelCase			):
																	__a       :     Any								=	0
																	__a       :     Any								=	1
																	__a       :     Optional[Any]								=	0
																	__a       :     Dict								=	1
													__a       :     Optional[int]								=	pt_model_class(__UpperCamelCase			).eval()
													# Flax models don't use the `use_cache` option and cache is not returned as a default.
													# So we disable `use_cache` here for PyTorch model.
													__a       :     Tuple								=	False
													__a       :     Tuple								=	model_class(__UpperCamelCase  ,    dtype=jnp.floataa			)
													__a       :     Optional[Any]								=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,    __UpperCamelCase			)
													__a       :     str								=	fx_state

													with torch.no_grad():
																	__a       :     Any								=	pt_model(**__UpperCamelCase			).to_tuple()

													__a       :     str								=	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       :     str								=	model_class.from_pretrained(__UpperCamelCase  ,    from_pt=__UpperCamelCase			)

													__a       :     Any								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a							,	__a       :     Dict								=	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       :     Optional[Any]								=	self._prepare_for_class(__UpperCamelCase  ,    __UpperCamelCase			)
													__a       :     Any								=	{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													__a       :     List[Any]								=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
													__a       :     Dict								=	getattr(__UpperCamelCase  ,    __UpperCamelCase			)

													__a       :     int								=	pt_model_class(__UpperCamelCase			).eval()
													__a       :     List[str]								=	False
													__a       :     Any								=	model_class(__UpperCamelCase  ,    dtype=jnp.floataa			)

													__a       :     Tuple								=	load_flax_weights_in_pytorch_model(__UpperCamelCase  ,    fx_model.params			)
													__a							,	__a       :     Any								=	pt_inputs["""input_ids"""].shape
													__a       :     Any								=	np.random.randint(0  ,    seq_length - 1  ,    size=(batch_size,)			)
													for batch_idx, start_index in enumerate(__UpperCamelCase			):
																	__a       :     Any								=	0
																	__a       :     Dict								=	1
																	__a       :     Any								=	0
																	__a       :     List[str]								=	1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
																	__a       :     List[str]								=	pt_model(**__UpperCamelCase			).to_tuple()

													__a       :     int								=	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       :     List[Any]								=	pt_model_class.from_pretrained(__UpperCamelCase  ,    from_flax=__UpperCamelCase			)

													with torch.no_grad():
																	__a       :     Union[str, Any]								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					for model_class_name in self.all_model_classes:
									__a       :     List[str]								=	model_class_name.from_pretrained("""facebook/xglm-564M"""			)
									__a       :     Optional[int]								=	model(np.ones((1, 1)			)			)
									self.assertIsNotNone(__UpperCamelCase			)

'''simple docstring'''







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	vocab
					__a       :     int								=	normalizers.BertNormalizer(
					    clean_text=__UpperCamelCase  ,    handle_chinese_chars=__UpperCamelCase  ,    strip_accents=__UpperCamelCase  ,    lowercase=__UpperCamelCase  ,    )
					try:
									import rjieba
					except ImportError:
									raise ImportError(
									    """You need to install rjieba to use RoFormerTokenizer. """
									    """See https://pypi.org/project/rjieba/ for installation."""			)
					__a       :     Optional[Any]								=	rjieba
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	[]

					# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
					for token, start, end in self.jieba.tokenize(str(__UpperCamelCase			)  ,    hmm=__UpperCamelCase			):
									if token in self.vocab:
													splits.append(normalized_string[start:end]			)
									else:
													__a       :     str								=	self.normalizers.normalize_str(__UpperCamelCase			).split()
													for token in token_list:
																	if token:
																					__a       :     str								=	start + len(__UpperCamelCase			)
																					splits.append(normalized_string[start:end]			)
																					__a       :     Union[str, Any]								=	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

					return splits

	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					pretok.split(self.jieba_split			)