






from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.xglm.modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		)



@require_tf
class 						lowerCamelCase				:







		'''simple docstring'''



		_snake_case   :   List[Any]							=  XGLMConfig
		_snake_case   :   Optional[int]							=  {}
		_snake_case   :   List[Any]							=  '''gelu'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_4    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=4    ,		_UpperCamelCase=3_7    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=0.02    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				Union[str, Any]						=						parent
							UpperCAmelCase_   :				List[Any]						=						batch_size
							UpperCAmelCase_   :				Optional[int]						=						seq_length
							UpperCAmelCase_   :				Optional[int]						=						is_training
							UpperCAmelCase_   :				List[Any]						=						use_input_mask
							UpperCAmelCase_   :				List[str]						=						use_labels
							UpperCAmelCase_   :				Any						=						vocab_size
							UpperCAmelCase_   :				Dict						=						d_model
							UpperCAmelCase_   :				Any						=						num_hidden_layers
							UpperCAmelCase_   :				Optional[Any]						=						num_attention_heads
							UpperCAmelCase_   :				Union[str, Any]						=						ffn_dim
							UpperCAmelCase_   :				str						=						activation_function
							UpperCAmelCase_   :				List[Any]						=						activation_dropout
							UpperCAmelCase_   :				Union[str, Any]						=						attention_dropout
							UpperCAmelCase_   :				Union[str, Any]						=						max_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						initializer_range
							UpperCAmelCase_   :				List[str]						=						None
							UpperCAmelCase_   :				Tuple						=						0
							UpperCAmelCase_   :				Dict						=						2
							UpperCAmelCase_   :				Union[str, Any]						=						1



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							return XGLMConfig.from_pretrained('facebook/xglm-564M'       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size       )    ,		clip_value_min=0    ,		clip_value_max=3       )

							UpperCAmelCase_   :				Tuple						=						None
							if self.use_input_mask:
												UpperCAmelCase_   :				Union[str, Any]						=						random_attention_mask([self.batch_size, self.seq_length]       )

							UpperCAmelCase_   :				Tuple						=						self.get_config()

							UpperCAmelCase_   :				List[Any]						=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]    ,		2       )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return 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_dict=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Dict						=						self.prepare_config_and_inputs()

							(
							    UpperCAmelCase_  
							)   :				Tuple						=						config_and_inputs

							UpperCAmelCase_   :				Union[str, Any]						=						{
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict





@require_tf
class 						lowerCamelCase				(_snake_case			,  _snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Optional[Any]							=  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
		_snake_case   :   Dict							=  (TFXGLMForCausalLM,) if is_tf_available() else ()
		_snake_case   :   Optional[Any]							=  (
		    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
		)
		_snake_case   :   Optional[int]							=  False
		_snake_case   :   List[Any]							=  False
		_snake_case   :   Union[str, Any]							=  False



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Optional[Any]						=						TFXGLMModelTester(self       )
							UpperCAmelCase_   :				List[Any]						=						ConfigTester(self    ,		config_class=_UpperCamelCase    ,		n_embd=3_7       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							self.config_tester.run_common_tests()



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_   :				Any						=						TFXGLMModel.from_pretrained(_UpperCamelCase       )
												self.assertIsNotNone(_UpperCamelCase       )



		@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'       )
		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							super().test_resize_token_embeddings()





@require_tf
class 						lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		@slow
		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase=True       )     ->							int:
							UpperCAmelCase_   :				Tuple						=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )
							UpperCAmelCase_   :				Any						=						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]    ,		dtype=tf.intaa       )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							UpperCAmelCase_   :				int						=						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
							# fmt: on
							UpperCAmelCase_   :				Union[str, Any]						=						model.generate(_UpperCamelCase    ,		do_sample=_UpperCamelCase    ,		num_beams=1       )
							if verify_outputs:
												self.assertListEqual(output_ids[0].numpy().tolist()    ,		_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Tuple						=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'       )
							UpperCAmelCase_   :				Tuple						=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )

							tf.random.set_seed(0       )
							UpperCAmelCase_   :				str						=						tokenizer('Today is a nice day and'    ,		return_tensors='tf'       )
							UpperCAmelCase_   :				int						=						tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(':/CPU:0'       ):
												UpperCAmelCase_   :				str						=						model.generate(_UpperCamelCase    ,		do_sample=_UpperCamelCase    ,		seed=[7, 0]       )
							UpperCAmelCase_   :				Any						=						tokenizer.decode(output_ids[0]    ,		skip_special_tokens=_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						(
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[str]						=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )
							UpperCAmelCase_   :				Tuple						=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'       )

							UpperCAmelCase_   :				Optional[Any]						=						'left'

							# use different length sentences to test batching
							UpperCAmelCase_   :				List[Any]						=						[
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When',
							    'Hello, my dog is a little',
							]

							UpperCAmelCase_   :				Optional[int]						=						tokenizer(_UpperCamelCase    ,		return_tensors='tf'    ,		padding=_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[int]						=						inputs['input_ids']

							UpperCAmelCase_   :				int						=						model.generate(input_ids=_UpperCamelCase    ,		attention_mask=inputs['attention_mask']    ,		max_new_tokens=1_2       )

							UpperCAmelCase_   :				int						=						tokenizer(sentences[0]    ,		return_tensors='tf'       ).input_ids
							UpperCAmelCase_   :				int						=						model.generate(input_ids=_UpperCamelCase    ,		max_new_tokens=1_2       )

							UpperCAmelCase_   :				Any						=						tokenizer(sentences[1]    ,		return_tensors='tf'       ).input_ids
							UpperCAmelCase_   :				Dict						=						model.generate(input_ids=_UpperCamelCase    ,		max_new_tokens=1_2       )

							UpperCAmelCase_   :				Union[str, Any]						=						tokenizer.batch_decode(_UpperCamelCase    ,		skip_special_tokens=_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						tokenizer.decode(output_non_padded[0]    ,		skip_special_tokens=_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						tokenizer.decode(output_padded[0]    ,		skip_special_tokens=_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						[
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
							    'a single',
							    'Hello, my dog is a little bit of a shy one, but he is very friendly',
							]
							self.assertListEqual(_UpperCamelCase    ,		_UpperCamelCase       )
							self.assertListEqual(_UpperCamelCase    ,		[non_padded_sentence, padded_sentence]       )














def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							int					,	__snake_case   :							List[str]  ):


					'''simple docstring'''


					if n == 0:
										return 1

					elif n % 2 == 1:
										return (binary_exponentiation(__snake_case					,	n - 1					,	__snake_case  ) * a) % mod

					else:
										UpperCAmelCase_   :				Optional[int]						=						binary_exponentiation(__snake_case					,	n / 2					,	__snake_case  )
										return (b * b) % mod


# a prime number
__UpperCAmelCase     							=						701

__UpperCAmelCase     							=						1000000000
__UpperCAmelCase     							=						10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)






