"""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[str]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Tuple=14      ,__UpperCAmelCase    :Optional[Any]=7      ,__UpperCAmelCase    :Dict=True      ,__UpperCAmelCase    :List[str]=True      ,__UpperCAmelCase    :Optional[int]=True      ,__UpperCAmelCase    :str=99      ,__UpperCAmelCase    :Union[str, Any]=32      ,__UpperCAmelCase    :int=5      ,__UpperCAmelCase    :int=4      ,__UpperCAmelCase    :Optional[Any]=37      ,__UpperCAmelCase    :Dict="gelu"      ,__UpperCAmelCase    :int=0.1      ,__UpperCAmelCase    :List[Any]=0.1      ,__UpperCAmelCase    :List[Any]=5_12      ,__UpperCAmelCase    :List[Any]=0.02      ,__UpperCAmelCase    :Union[str, Any]=None      ,)      ->					Any:




									"""simple docstring"""



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







				def   lowercase_		(    self    :Optional[Any]  )      ->					Dict:




									"""simple docstring"""



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

									lowerCamelCase__		:   Optional[int]  				=					None
									if self.use_input_mask:
														lowerCamelCase__		:   Dict  				=					random_attention_mask([self.batch_size, self.seq_length]  )

									lowerCamelCase__		:   List[str]  				=					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   lowercase_		(    self    :Optional[Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.prepare_config_and_inputs()
									lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   str  				=					config_and_inputs
									lowerCamelCase__		:   List[Any]  				=					{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Any:




									"""simple docstring"""



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

									lowerCamelCase__		:   Optional[Any]  				=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									lowerCamelCase__		:   List[str]  				=					ids_tensor([self.batch_size, self.seq_length]      ,vocab_size=2  )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Any  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					20
									lowerCamelCase__		:   List[str]  				=					model_class_name(UpperCAmelCase__  )

									lowerCamelCase__		:   Optional[int]  				=					model.init_cache(input_ids.shape[0]      ,UpperCAmelCase__  )
									lowerCamelCase__		:   Dict  				=					jnp.ones((input_ids.shape[0], max_decoder_length)      ,dtype='''i4'''  )

									lowerCamelCase__		:   List[Any]  				=					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]      ,(input_ids.shape[0], input_ids.shape[-1] - 1)  )
									lowerCamelCase__		:   Union[str, Any]  				=					model(
									    input_ids[:, :-1]      ,attention_mask=UpperCAmelCase__      ,past_key_values=UpperCAmelCase__      ,position_ids=UpperCAmelCase__      ,)

									lowerCamelCase__		:   List[str]  				=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,dtype='''i4'''  )
									lowerCamelCase__		:   List[str]  				=					model(
									    input_ids[:, -1:]      ,attention_mask=UpperCAmelCase__      ,past_key_values=outputs_cache.past_key_values      ,position_ids=UpperCAmelCase__      ,)

									lowerCamelCase__		:   Optional[int]  				=					model(UpperCAmelCase__  )

									lowerCamelCase__		:   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   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :int  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					20
									lowerCamelCase__		:   List[str]  				=					model_class_name(UpperCAmelCase__  )

									lowerCamelCase__		:   List[str]  				=					jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]      ,axis=-1      ,)

									lowerCamelCase__		:   str  				=					model.init_cache(input_ids.shape[0]      ,UpperCAmelCase__  )
									lowerCamelCase__		:   Union[str, Any]  				=					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]      ,(input_ids.shape[0], input_ids.shape[-1] - 1)  )

									lowerCamelCase__		:   Any  				=					model(
									    input_ids[:, :-1]      ,attention_mask=UpperCAmelCase__      ,past_key_values=UpperCAmelCase__      ,position_ids=UpperCAmelCase__      ,)
									lowerCamelCase__		:   Any  				=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,dtype='''i4'''  )
									lowerCamelCase__		:   Optional[Any]  				=					model(
									    input_ids[:, -1:]      ,past_key_values=outputs_cache.past_key_values      ,attention_mask=UpperCAmelCase__      ,position_ids=UpperCAmelCase__      ,)

									lowerCamelCase__		:   Optional[int]  				=					model(UpperCAmelCase__      ,attention_mask=UpperCAmelCase__  )
									lowerCamelCase__		:   Tuple  				=					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     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				UpperCAmelCase	=      (FlaxXGLMForCausalLM,) if is_flax_available() else ()







				def   lowercase_		(    self    :Union[str, Any]  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					FlaxXGLMModelTester(self  )







				def   lowercase_		(    self    :Dict  )      ->					int:




									"""simple docstring"""



									for model_class_name in self.all_model_classes:
														lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   Optional[Any]  				=					self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__      ,UpperCAmelCase__  )







				def   lowercase_		(    self    :List[str]  )      ->					Dict:




									"""simple docstring"""



									for model_class_name in self.all_model_classes:
														lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   int  				=					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    :Union[str, Any]  )      ->					Tuple:




									"""simple docstring"""



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

									lowerCamelCase__		:   int  				=					FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''  )
									lowerCamelCase__		:   List[str]  				=					1
									lowerCamelCase__		:   Tuple  				=					False

									lowerCamelCase__		:   Union[str, Any]  				=					jax.jit(model.generate  )

									lowerCamelCase__		:   Optional[int]  				=					jit_generate(inputs['''input_ids''']      ,attention_mask=inputs['''attention_mask''']  ).sequences

									lowerCamelCase__		:   str  				=					tokenizer.batch_decode(UpperCAmelCase__      ,skip_special_tokens=UpperCAmelCase__  )

									lowerCamelCase__		:   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(UpperCAmelCase__      ,UpperCAmelCase__  )







				@is_pt_flax_cross_test
				def   lowercase_		(    self    :Any  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   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
																			lowerCamelCase__		:   List[str]  				=					self._prepare_for_class(UpperCAmelCase__      ,UpperCAmelCase__  )
																			lowerCamelCase__		:   Dict  				=					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			lowerCamelCase__		:   Union[str, Any]  				=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			lowerCamelCase__		:   Optional[int]  				=					getattr(UpperCAmelCase__      ,UpperCAmelCase__  )

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

																			with torch.no_grad():
																								lowerCamelCase__		:   Optional[int]  				=					pt_model(**UpperCAmelCase__  ).to_tuple()

																			lowerCamelCase__		:   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:
																								pt_model.save_pretrained(UpperCAmelCase__  )
																								lowerCamelCase__		:   Any  				=					model_class.from_pretrained(UpperCAmelCase__      ,from_pt=UpperCAmelCase__  )

																			lowerCamelCase__		:   Optional[int]  				=					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    :List[Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



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

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

																			lowerCamelCase__		:   Optional[int]  				=					pt_model_class(UpperCAmelCase__  ).eval()
																			lowerCamelCase__		:   Any  				=					False
																			lowerCamelCase__		:   Tuple  				=					model_class(UpperCAmelCase__      ,dtype=jnp.floataa  )

																			lowerCamelCase__		:   Optional[int]  				=					load_flax_weights_in_pytorch_model(UpperCAmelCase__      ,fx_model.params  )
																			lowerCamelCase__				,						lowerCamelCase__		:   int  				=					pt_inputs['''input_ids'''].shape
																			lowerCamelCase__		:   int  				=					np.random.randint(0      ,seq_length - 1      ,size=(batch_size,)  )
																			for batch_idx, start_index in enumerate(UpperCAmelCase__  ):
																								lowerCamelCase__		:   Tuple  				=					0
																								lowerCamelCase__		:   int  				=					1
																								lowerCamelCase__		:   Optional[int]  				=					0
																								lowerCamelCase__		:   List[Any]  				=					1

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

																			with torch.no_grad():
																								lowerCamelCase__		:   List[Any]  				=					pt_model(**UpperCAmelCase__  ).to_tuple()

																			lowerCamelCase__		:   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__  )
																								lowerCamelCase__		:   List[str]  				=					pt_model_class.from_pretrained(UpperCAmelCase__      ,from_flax=UpperCAmelCase__  )

																			with torch.no_grad():
																								lowerCamelCase__		:   List[str]  				=					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    :List[Any]  )      ->					Any:




									"""simple docstring"""



									for model_class_name in self.all_model_classes:
														lowerCamelCase__		:   Any  				=					model_class_name.from_pretrained('''facebook/xglm-564M'''  )
														lowerCamelCase__		:   Tuple  				=					model(np.ones((1, 1)  )  )
														self.assertIsNotNone(UpperCAmelCase__  )










"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					str(lowerCamelCase_	).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					oct_string[0] == "-"
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_	) <= 7 for char in oct_string	):
								raise ValueError("Non-octal value was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					0
				for char in oct_string:
								SCREAMING_SNAKE_CASE__  				=					8 * decimal_number + int(lowerCamelCase_	)
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					-decimal_number
				return decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
