




'''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 	lowerCAmelCase_  :







		'''simple docstring'''

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







							'''simple docstring'''



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



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







							'''simple docstring'''



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

							A:		Optional[int]        =		None
							if self.use_input_mask:
												A:		Union[str, Any]        =		random_attention_mask([self.batch_size, self.seq_length]  )

							A:		Optional[int]        =		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=SCREAMING_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 				_snake_case    (   self							:       Dict  ) ->		str:







							'''simple docstring'''



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



		def 				_snake_case    (   self							:       List[str]  ) ->		int:







							'''simple docstring'''



							A:		str        =		self.prepare_config_and_inputs()

							A:		Union[str, Any]        =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
							A:		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 				_snake_case    (   self							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       int				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       Tuple  ) ->		Dict:







							'''simple docstring'''



							A:		Optional[Any]        =		20
							A:		str        =		model_class_name(SCREAMING_SNAKE_CASE_  )

							A:		Tuple        =		model.init_cache(input_ids.shape[0]				,     SCREAMING_SNAKE_CASE_  )
							A:		Tuple        =		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:		List[Any]        =		model(
							    input_ids[:, :-1]				,     attention_mask=SCREAMING_SNAKE_CASE_				,     past_key_values=SCREAMING_SNAKE_CASE_				,     position_ids=SCREAMING_SNAKE_CASE_				,     )

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

							A:		Tuple        =		model(SCREAMING_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 				_snake_case    (   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ) ->		Optional[int]:







							'''simple docstring'''



							A:		List[str]        =		20
							A:		Optional[int]        =		model_class_name(SCREAMING_SNAKE_CASE_  )

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

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

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

							A:		Tuple        =		model(SCREAMING_SNAKE_CASE_				,     attention_mask=SCREAMING_SNAKE_CASE_  )
							A:		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 	lowerCAmelCase_  (					UpperCAmelCase_					,				UpperCAmelCase_					,				unittest.TestCase       ):







		'''simple docstring'''

		UpperCamelCase_ :			int        =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
		UpperCamelCase_ :			int        =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()



		def 				_snake_case    (   self							:       str  ) ->		int:







							'''simple docstring'''



							A:		List[Any]        =		FlaxXGLMModelTester(self  )



		def 				_snake_case    (   self							:       Optional[int]  ) ->		List[Any]:







							'''simple docstring'''



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



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







							'''simple docstring'''



							for model_class_name in self.all_model_classes:
												A:		Dict        =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



		@slow
		def 				_snake_case    (   self							:       Dict  ) ->		Tuple:







							'''simple docstring'''



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

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

							A:		str        =		jax.jit(model.generate  )

							A:		Tuple        =		jit_generate(inputs['''input_ids''']				,     attention_mask=inputs['''attention_mask''']  ).sequences

							A:		str        =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				,     skip_special_tokens=SCREAMING_SNAKE_CASE_  )

							A:		Tuple        =		[
							    '''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(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



		@is_pt_flax_cross_test
		def 				_snake_case    (   self							:       List[str]  ) ->		Tuple:







							'''simple docstring'''



							A:		Union[str, Any]        =		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(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
																	A:		Union[str, Any]        =		{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

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

																	with torch.no_grad():
																						A:		List[str]        =		pt_model(**SCREAMING_SNAKE_CASE_  ).to_tuple()

																	A:		Any        =		fx_model(**SCREAMING_SNAKE_CASE_  ).to_tuple()
																	self.assertEqual(len(SCREAMING_SNAKE_CASE_  )				,     len(SCREAMING_SNAKE_CASE_  )				,     '''Output lengths differ between Flax and PyTorch'''  )
																	for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
																						self.assert_almost_equals(fx_output[:, -1]				,     pt_output[:, -1].numpy()				,     4E-2  )

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						pt_model.save_pretrained(SCREAMING_SNAKE_CASE_  )
																						A:		Tuple        =		model_class.from_pretrained(SCREAMING_SNAKE_CASE_				,     from_pt=SCREAMING_SNAKE_CASE_  )

																	A:		Union[str, Any]        =		fx_model_loaded(**SCREAMING_SNAKE_CASE_  ).to_tuple()
																	self.assertEqual(
																	    len(SCREAMING_SNAKE_CASE_  )				,     len(SCREAMING_SNAKE_CASE_  )				,     '''Output lengths differ between Flax and PyTorch'''  )
																	for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
																						self.assert_almost_equals(fx_output_loaded[:, -1]				,     pt_output[:, -1].numpy()				,     4E-2  )



		@is_pt_flax_cross_test
		def 				_snake_case    (   self							:       Any  ) ->		Union[str, Any]:







							'''simple docstring'''



							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:		Union[str, Any]        =		self._prepare_for_class(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
																	A:		Dict        =		{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

																	A:		Tuple        =		pt_model_class(SCREAMING_SNAKE_CASE_  ).eval()
																	A:		List[str]        =		False
																	A:		Dict        =		model_class(SCREAMING_SNAKE_CASE_				,     dtype=jnp.floataa  )

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

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

																	with torch.no_grad():
																						A:		int        =		pt_model(**SCREAMING_SNAKE_CASE_  ).to_tuple()

																	A:		List[Any]        =		fx_model(**SCREAMING_SNAKE_CASE_  ).to_tuple()
																	self.assertEqual(len(SCREAMING_SNAKE_CASE_  )				,     len(SCREAMING_SNAKE_CASE_  )				,     '''Output lengths differ between Flax and PyTorch'''  )
																	for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ):
																						self.assert_almost_equals(fx_output[:, -1]				,     pt_output[:, -1].numpy()				,     4E-2  )

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						fx_model.save_pretrained(SCREAMING_SNAKE_CASE_  )
																						A:		Union[str, Any]        =		pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_				,     from_flax=SCREAMING_SNAKE_CASE_  )

																	with torch.no_grad():
																						A:		Dict        =		pt_model_loaded(**SCREAMING_SNAKE_CASE_  ).to_tuple()

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




		@slow
		def 				_snake_case    (   self							:       Union[str, Any]  ) ->		str:







							'''simple docstring'''



							for model_class_name in self.all_model_classes:
												A:		str        =		model_class_name.from_pretrained('''facebook/xglm-564M'''  )
												A:		Union[str, Any]        =		model(np.ones((1, 1)  )  )
												self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )












'''simple docstring'''


def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   int:
     A:		str        =		str(__lowercase  ).strip()
     if not bin_string:
          raise ValueError('''Empty string was passed to the function'''  )
     A:		int        =		bin_string[0] == '''-'''
     if is_negative:
          A:		Optional[Any]        =		bin_string[1:]
     if not all(char in '''01''' for char in bin_string  ):
          raise ValueError('''Non-binary value was passed to the function'''  )
     A:		Any        =		0
     for char in bin_string:
          A:		Dict        =		2 * decimal_number + int(__lowercase  )
     return -decimal_number if is_negative else decimal_number


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

     testmod()






