






"""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   :    List[Any]      ,     snake_case   :    List[Any]      ,     snake_case   :    Any=14      ,     snake_case   :    Any=7      ,     snake_case   :    Optional[int]=True      ,     snake_case   :    str=True      ,     snake_case   :    Union[str, Any]=True      ,     snake_case   :    Tuple=99      ,     snake_case   :    List[Any]=32      ,     snake_case   :    int=5      ,     snake_case   :    List[str]=4      ,     snake_case   :    List[Any]=37      ,     snake_case   :    List[str]="gelu"      ,     snake_case   :    Optional[int]=0.1      ,     snake_case   :    Union[str, Any]=0.1      ,     snake_case   :    List[Any]=512      ,     snake_case   :    str=0.02      ,     snake_case   :    Dict=None      ,     ):
													'''simple docstring'''





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


							def     _UpperCamelCase							(      self   :    str				):
													'''simple docstring'''





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

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

													A__    :	Tuple			=	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=snake_case      ,     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     _UpperCamelCase							(      self   :    Dict				):
													'''simple docstring'''





													A__    :	str			=	self.prepare_config_and_inputs()
													A__	,	A__	,	A__    :	Optional[int]			=	config_and_inputs
													A__    :	Dict			=	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
													return config, inputs_dict


							def     _UpperCamelCase							(      self   :    Dict				):
													'''simple docstring'''





													A__	,	A__	,	A__    :	str			=	self.prepare_config_and_inputs()

													A__    :	Tuple			=	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     _UpperCamelCase							(      self   :    str      ,     snake_case   :    Optional[int]      ,     snake_case   :    int      ,     snake_case   :    Union[str, Any]      ,     snake_case   :    List[Any]				):
													'''simple docstring'''





													A__    :	Union[str, Any]			=	20
													A__    :	Any			=	model_class_name(snake_case				)

													A__    :	str			=	model.init_cache(input_ids.shape[0]      ,     snake_case				)
													A__    :	Optional[int]			=	jnp.ones((input_ids.shape[0], max_decoder_length)      ,     dtype="""i4"""				)

													A__    :	Any			=	jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1				)[None, :]      ,     (input_ids.shape[0], input_ids.shape[-1] - 1)				)
													A__    :	Dict			=	model(
													    input_ids[:, :-1]      ,     attention_mask=snake_case      ,     past_key_values=snake_case      ,     position_ids=snake_case      ,     )

													A__    :	Optional[int]			=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,     dtype="""i4"""				)
													A__    :	Any			=	model(
													    input_ids[:, -1:]      ,     attention_mask=snake_case      ,     past_key_values=outputs_cache.past_key_values      ,     position_ids=snake_case      ,     )

													A__    :	Tuple			=	model(snake_case				)

													A__    :	List[str]			=	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   :    Union[str, Any]      ,     snake_case   :    int      ,     snake_case   :    Optional[Any]      ,     snake_case   :    Any      ,     snake_case   :    str				):
													'''simple docstring'''





													A__    :	str			=	20
													A__    :	List[str]			=	model_class_name(snake_case				)

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

													A__    :	int			=	model.init_cache(input_ids.shape[0]      ,     snake_case				)
													A__    :	Any			=	jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1				)[None, :]      ,     (input_ids.shape[0], input_ids.shape[-1] - 1)				)

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

													A__    :	Optional[int]			=	model(snake_case      ,     attention_mask=snake_case				)
													A__    :	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_sentencepiece
@require_flax
class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase							,		UpperCamelCase							,		unittest.TestCase		):
							snake_case_  =				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							snake_case_  =				(FlaxXGLMForCausalLM,) if is_flax_available() else ()


							def     _UpperCamelCase							(      self   :    Tuple				):
													'''simple docstring'''





													A__    :	Any			=	FlaxXGLMModelTester(self				)


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													for model_class_name in self.all_model_classes:
																			A__	,	A__	,	A__    :	List[str]			=	self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													for model_class_name in self.all_model_classes:
																			A__	,	A__	,	A__    :	List[Any]			=	self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    snake_case      ,     snake_case      ,     snake_case      ,     snake_case				)


							@slow
							def     _UpperCamelCase							(      self   :    List[str]				):
													'''simple docstring'''





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

													A__    :	Union[str, Any]			=	FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""				)
													A__    :	Optional[int]			=	1
													A__    :	Dict			=	False

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

													A__    :	List[Any]			=	jit_generate(inputs["""input_ids"""]      ,     attention_mask=inputs["""attention_mask"""]				).sequences

													A__    :	Optional[int]			=	tokenizer.batch_decode(snake_case      ,     skip_special_tokens=snake_case				)

													A__    :	Dict			=	[
													    """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(snake_case      ,     snake_case				)


							@is_pt_flax_cross_test
							def     _UpperCamelCase							(      self   :    List[str]				):
													'''simple docstring'''





													A__	,	A__    :	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
																									A__    :	Any			=	self._prepare_for_class(snake_case      ,     snake_case				)
																									A__    :	Dict			=	{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__    :	int			=	getattr(snake_case      ,     snake_case				)

																									A__	,	A__    :	Optional[Any]			=	pt_inputs["""input_ids"""].shape
																									A__    :	Optional[Any]			=	np.random.randint(0      ,     seq_length - 1      ,     size=(batch_size,)				)
																									for batch_idx, start_index in enumerate(snake_case				):
																															A__    :	List[Any]			=	0
																															A__    :	Union[str, Any]			=	1
																															A__    :	List[str]			=	0
																															A__    :	List[Any]			=	1
																									A__    :	List[str]			=	pt_model_class(snake_case				).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__    :	Optional[int]			=	False
																									A__    :	List[str]			=	model_class(snake_case      ,     dtype=jnp.floataa				)
																									A__    :	Optional[Any]			=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,     snake_case				)
																									A__    :	Tuple			=	fx_state

																									with torch.no_grad():
																															A__    :	Tuple			=	pt_model(**snake_case				).to_tuple()

																									A__    :	List[str]			=	fx_model(**snake_case				).to_tuple()
																									self.assertEqual(len(snake_case				)      ,     len(snake_case				)      ,     """Output lengths differ between Flax and PyTorch"""				)
																									for fx_output, pt_output in zip(snake_case      ,     snake_case				):
																															self.assert_almost_equals(fx_output[:, -1]      ,     pt_output[:, -1].numpy()      ,     4e-2				)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															pt_model.save_pretrained(snake_case				)
																															A__    :	Dict			=	model_class.from_pretrained(snake_case      ,     from_pt=snake_case				)

																									A__    :	int			=	fx_model_loaded(**snake_case				).to_tuple()
																									self.assertEqual(
																									    len(snake_case				)      ,     len(snake_case				)      ,     """Output lengths differ between Flax and PyTorch"""				)
																									for fx_output_loaded, pt_output in zip(snake_case      ,     snake_case				):
																															self.assert_almost_equals(fx_output_loaded[:, -1]      ,     pt_output[:, -1].numpy()      ,     4e-2				)


							@is_pt_flax_cross_test
							def     _UpperCamelCase							(      self   :    List[Any]				):
													'''simple docstring'''





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

																									# load corresponding PyTorch class
																									A__    :	List[str]			=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									A__    :	str			=	getattr(snake_case      ,     snake_case				)

																									A__    :	Optional[Any]			=	pt_model_class(snake_case				).eval()
																									A__    :	List[str]			=	False
																									A__    :	Tuple			=	model_class(snake_case      ,     dtype=jnp.floataa				)

																									A__    :	Union[str, Any]			=	load_flax_weights_in_pytorch_model(snake_case      ,     fx_model.params				)
																									A__	,	A__    :	Dict			=	pt_inputs["""input_ids"""].shape
																									A__    :	str			=	np.random.randint(0      ,     seq_length - 1      ,     size=(batch_size,)				)
																									for batch_idx, start_index in enumerate(snake_case				):
																															A__    :	Dict			=	0
																															A__    :	Optional[Any]			=	1
																															A__    :	Union[str, Any]			=	0
																															A__    :	Optional[int]			=	1

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

																									with torch.no_grad():
																															A__    :	str			=	pt_model(**snake_case				).to_tuple()

																									A__    :	Optional[int]			=	fx_model(**snake_case				).to_tuple()
																									self.assertEqual(len(snake_case				)      ,     len(snake_case				)      ,     """Output lengths differ between Flax and PyTorch"""				)
																									for fx_output, pt_output in zip(snake_case      ,     snake_case				):
																															self.assert_almost_equals(fx_output[:, -1]      ,     pt_output[:, -1].numpy()      ,     4e-2				)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															fx_model.save_pretrained(snake_case				)
																															A__    :	Dict			=	pt_model_class.from_pretrained(snake_case      ,     from_flax=snake_case				)

																									with torch.no_grad():
																															A__    :	Any			=	pt_model_loaded(**snake_case				).to_tuple()

																									self.assertEqual(
																									    len(snake_case				)      ,     len(snake_case				)      ,     """Output lengths differ between Flax and PyTorch"""				)
																									for fx_output, pt_output in zip(snake_case      ,     snake_case				):
																															self.assert_almost_equals(fx_output[:, -1]      ,     pt_output[:, -1].numpy()      ,     4e-2				)


							@slow
							def     _UpperCamelCase							(      self   :    List[Any]				):
													'''simple docstring'''





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














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 4_0_0_0_0_0_0   )		->int:
						A__    :	int			=	1
						A__    :	List[str]			=	2
						A__    :	Union[str, Any]			=	0
						while j <= n:
												if j % 2 == 0:
																		total += j
												A__	,	A__    :	str			=	j, i + j

						return total


if __name__ == "__main__":
						print(F'{solution() = }')






