





'''simple docstring'''


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       a_   :
     __A      =			XGLMConfig
     __A      =			{}
     __A      =			"gelu"





     def __init__( self		:					Dict	,      lowercase		:					int	,      lowercase		:					Union[str, Any]=14	,      lowercase		:					Optional[int]=7	,      lowercase		:					Union[str, Any]=True	,      lowercase		:					Optional[int]=True	,      lowercase		:					Optional[Any]=True	,      lowercase		:					Tuple=99	,      lowercase		:					List[str]=32	,      lowercase		:					Optional[Any]=2	,      lowercase		:					Any=4	,      lowercase		:					Any=37	,      lowercase		:					Optional[Any]="gelu"	,      lowercase		:					List[Any]=0.1	,      lowercase		:					Union[str, Any]=0.1	,      lowercase		:					Union[str, Any]=512	,      lowercase		:					Optional[int]=0.02	,      ):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        return XGLMConfig.from_pretrained("facebook/xglm-564M"						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:List[str]    			= tf.clip_by_value(
            ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)	,      clip_value_min=0	,      clip_value_max=3						)

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

        lowercase_			:Tuple    			= self.get_config()

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

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
        )





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        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=lowercase	,      bos_token_id=self.bos_token_id	,      eos_token_id=self.eos_token_id	,      pad_token_id=self.pad_token_id	,      return_dict=lowercase	,      )







     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:int    			= self.prepare_config_and_inputs()

        (
            (
            lowercase_
        )			,							(
            lowercase_
        )			,							(
            lowercase_
        )			,							(
            lowercase_
        )			,							
        )			:Optional[Any]    			= config_and_inputs

        lowercase_			:Union[str, Any]    			= {
            "input_ids": input_ids,
            "head_mask": head_mask,
        }

        return config, inputs_dict







@require_tf
class       a_   (				_lowerCAmelCase	,						_lowerCAmelCase	,						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 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Dict    			= TFXGLMModelTester(self						)
        lowercase_			:List[str]    			= ConfigTester(self	,      config_class=lowercase	,      n_embd=37						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        self.config_tester.run_common_tests()





     @slow
     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_			:Dict    			= TFXGLMModel.from_pretrained(lowercase						)
           self.assertIsNotNone(lowercase						)







     @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."						)
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        super().test_resize_token_embeddings()







@require_tf
class       a_   (				unittest.TestCase							):





     @slow
     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					Optional[int]=True						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"						)
        lowercase_			:int    			= 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
        lowercase_			:List[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
        lowercase_			:Any    			= model.generate(lowercase	,      do_sample=lowercase	,      num_beams=1						)
        if verify_outputs:
           self.assertListEqual(output_ids[0].numpy().tolist()	,      lowercase						)





     @slow
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= XGLMTokenizer.from_pretrained("facebook/xglm-564M"						)
        lowercase_			:int    			= TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"						)

        tf.random.set_seed(0						)
        lowercase_			:Union[str, Any]    			= tokenizer("Today is a nice day and"	,      return_tensors="tf"						)
        lowercase_			: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"						):
           lowercase_			:Union[str, Any]    			= model.generate(lowercase	,      do_sample=lowercase	,      seed=[7, 0]						)
        lowercase_			:List[Any]    			= tokenizer.decode(output_ids[0]	,      skip_special_tokens=lowercase						)

        lowercase_			:Optional[Any]    			= (
            "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
        )
        self.assertEqual(lowercase	,      lowercase						)







     @slow
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[str]    			= TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"						)
        lowercase_			:List[str]    			= XGLMTokenizer.from_pretrained("facebook/xglm-564M"						)

        lowercase_			:Dict    			= "left"

        # use different length sentences to test batching
        lowercase_			:Optional[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",
        ]

        lowercase_			:Tuple    			= tokenizer(lowercase	,      return_tensors="tf"	,      padding=lowercase						)
        lowercase_			:Union[str, Any]    			= inputs["input_ids"]

        lowercase_			:int    			= model.generate(input_ids=lowercase	,      attention_mask=inputs["attention_mask"]	,      max_new_tokens=12						)

        lowercase_			:List[Any]    			= tokenizer(sentences[0]	,      return_tensors="tf"						).input_ids
        lowercase_			:Any    			= model.generate(input_ids=lowercase	,      max_new_tokens=12						)

        lowercase_			:Any    			= tokenizer(sentences[1]	,      return_tensors="tf"						).input_ids
        lowercase_			:List[str]    			= model.generate(input_ids=lowercase	,      max_new_tokens=12						)

        lowercase_			:List[str]    			= tokenizer.batch_decode(lowercase	,      skip_special_tokens=lowercase						)
        lowercase_			:Any    			= tokenizer.decode(output_non_padded[0]	,      skip_special_tokens=lowercase						)
        lowercase_			:Any    			= tokenizer.decode(output_padded[0]	,      skip_special_tokens=lowercase						)

        lowercase_			:Dict    			= [
            "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(lowercase	,      lowercase						)
        self.assertListEqual(lowercase	,      [non_padded_sentence, padded_sentence]						)









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int = 1_00 ):
   lowercase_			:Tuple    			= n * (n + 1) * (2 * n + 1) / 6
   lowercase_			:List[str]    			= (n * (n + 1) / 2) ** 2
   return int(square_of_sum - sum_of_squares )


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


