






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 					_snake_case						:
	_A          =   XGLMConfig
	_A          =   {}
	_A          =   'gelu'






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=14					,UpperCamelCase=7					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=99					,UpperCamelCase=32					,UpperCamelCase=2					,UpperCamelCase=4					,UpperCamelCase=37					,UpperCamelCase="gelu"					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=512					,UpperCamelCase=0.02					,)						->    Optional[int]:
					snake_case__      :List[str]         =   parent
					snake_case__      :List[Any]         =   batch_size
					snake_case__      :Optional[Any]         =   seq_length
					snake_case__      :Any         =   is_training
					snake_case__      :List[str]         =   use_input_mask
					snake_case__      :List[Any]         =   use_labels
					snake_case__      :str         =   vocab_size
					snake_case__      :Dict         =   d_model
					snake_case__      :str         =   num_hidden_layers
					snake_case__      :Optional[int]         =   num_attention_heads
					snake_case__      :List[str]         =   ffn_dim
					snake_case__      :Any         =   activation_function
					snake_case__      :Union[str, Any]         =   activation_dropout
					snake_case__      :List[str]         =   attention_dropout
					snake_case__      :Optional[int]         =   max_position_embeddings
					snake_case__      :List[str]         =   initializer_range
					snake_case__      :Any         =   None
					snake_case__      :str         =   0
					snake_case__      :List[Any]         =   2
					snake_case__      :List[Any]         =   1






	def    lowerCAmelCase_							(		self       )						->    int:
					return XGLMConfig.from_pretrained("facebook/xglm-564M"       )






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

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

					snake_case__      :Union[str, Any]         =   self.get_config()

					snake_case__      :Tuple         =   floats_tensor([self.num_hidden_layers, self.num_attention_heads]					,2       )

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					)






	def    lowerCAmelCase_							(		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    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Union[str, Any]         =   self.prepare_config_and_inputs()

					(
					    (
					    snake_case__
					)    ,						(
					    snake_case__
					)    ,						(
					    snake_case__
					)    ,						(
					    snake_case__
					)    ,						
					)      :List[str]         =   config_and_inputs

					snake_case__      :List[Any]         =   {
					    "input_ids": input_ids,
					    "head_mask": head_mask,
					}

					return config, inputs_dict




@require_tf
class 					_snake_case						(			_A       ,    _A       ,    unittest.TestCase ):
	_A          =   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	_A          =   (TFXGLMForCausalLM,) if is_tf_available() else ()
	_A          =   (
	    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
	)
	_A          =   False
	_A          =   False
	_A          =   False






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Optional[Any]         =   TFXGLMModelTester(self       )
					snake_case__      :Optional[int]         =   ConfigTester(self					,config_class=UpperCamelCase					,n_embd=37       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					self.config_tester.run_common_tests()






	@slow
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__      :List[Any]         =   TFXGLMModel.from_pretrained(UpperCamelCase       )
									self.assertIsNotNone(UpperCamelCase       )






	@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."       )
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					super().test_resize_token_embeddings()




@require_tf
class 					_snake_case						(			unittest.TestCase ):






	@slow
	def    lowerCAmelCase_							(		self					,UpperCamelCase=True       )						->    str:
					snake_case__      :str         =   TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )
					snake_case__      :Optional[Any]         =   tf.convert_to_tensor([[2, 268, 9_865]]					,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
					snake_case__      :Any         =   [2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
					# fmt: on
					snake_case__      :Dict         =   model.generate(UpperCamelCase					,do_sample=UpperCamelCase					,num_beams=1       )
					if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()					,UpperCamelCase       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :str         =   XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )
					snake_case__      :Tuple         =   TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )

					tf.random.set_seed(0       )
					snake_case__      :Optional[int]         =   tokenizer("Today is a nice day and"					,return_tensors="tf"       )
					snake_case__      :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"       ):
									snake_case__      :Tuple         =   model.generate(UpperCamelCase					,do_sample=UpperCamelCase					,seed=[7, 0]       )
					snake_case__      :str         =   tokenizer.decode(output_ids[0]					,skip_special_tokens=UpperCamelCase       )

					snake_case__      :str         =   (
					    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
					)
					self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :str         =   TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )
					snake_case__      :str         =   XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

					snake_case__      :Any         =   "left"

					# use different length sentences to test batching
					snake_case__      :List[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",
					    "Hello, my dog is a little",
					]

					snake_case__      :Optional[int]         =   tokenizer(UpperCamelCase					,return_tensors="tf"					,padding=UpperCamelCase       )
					snake_case__      :Dict         =   inputs["input_ids"]

					snake_case__      :Any         =   model.generate(input_ids=UpperCamelCase					,attention_mask=inputs["attention_mask"]					,max_new_tokens=12       )

					snake_case__      :Any         =   tokenizer(sentences[0]					,return_tensors="tf"       ).input_ids
					snake_case__      :str         =   model.generate(input_ids=UpperCamelCase					,max_new_tokens=12       )

					snake_case__      :Union[str, Any]         =   tokenizer(sentences[1]					,return_tensors="tf"       ).input_ids
					snake_case__      :str         =   model.generate(input_ids=UpperCamelCase					,max_new_tokens=12       )

					snake_case__      :List[Any]         =   tokenizer.batch_decode(UpperCamelCase					,skip_special_tokens=UpperCamelCase       )
					snake_case__      :Optional[int]         =   tokenizer.decode(output_non_padded[0]					,skip_special_tokens=UpperCamelCase       )
					snake_case__      :List[str]         =   tokenizer.decode(output_padded[0]					,skip_special_tokens=UpperCamelCase       )

					snake_case__      :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 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     :							int      )     -> bool:





				'''simple docstring'''


				if p < 2:
								raise ValueError("p should not be less than 2!"      )
				elif p == 2:
								return True

				snake_case__      :List[str]         =   4
				snake_case__      :Optional[int]         =   (1 << p) - 1
				for _ in range(p - 2      ):
								snake_case__      :List[Any]         =   ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
	print(lucas_lehmer_test(7))
	print(lucas_lehmer_test(1_1))