





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    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=			XGLMConfig
 lowercase_		=			{}
 lowercase_		=			"gelu"

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



   '''simple docstring'''



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

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



   '''simple docstring'''



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

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =tf.clip_by_value(
       ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)       , clip_value_min=0       , clip_value_max=3)

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

   lowerCamelCase__:      Dict			  =self.get_config()

   lowerCamelCase__:      Optional[Any]			  =floats_tensor([self.num_hidden_layers, self.num_attention_heads]       , 2)

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
   )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->int:



   '''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=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 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.prepare_config_and_inputs()

   (
       (
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					
   ):      Union[str, Any]			  =config_and_inputs

   lowerCamelCase__:      str			  ={
       "input_ids": input_ids,
       "head_mask": head_mask,
   }

   return config, inputs_dict



@require_tf
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 lowercase_		=			(TFXGLMForCausalLM,) if is_tf_available() else ()
 lowercase_		=			(
     {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
 )
 lowercase_		=			False
 lowercase_		=			False
 lowercase_		=			False

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =TFXGLMModelTester(self)
   lowerCamelCase__:      str			  =ConfigTester(self       , config_class=UpperCAmelCase_       , n_embd=37)

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->str:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Dict:



   '''simple docstring'''



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







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



   '''simple docstring'''



   super().test_resize_token_embeddings()



@require_tf
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   str=True)       ->Any:



   '''simple docstring'''



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

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =XGLMTokenizer.from_pretrained("facebook/xglm-564M")
   lowerCamelCase__:      Tuple			  =TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")

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

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







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[str]:



   '''simple docstring'''



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

   lowerCamelCase__:      str			  ="left"

   # use different length sentences to test batching
   lowerCamelCase__:      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",
   ]

   lowerCamelCase__:      Optional[Any]			  =tokenizer(UpperCAmelCase_       , return_tensors="tf"       , padding=UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =inputs["input_ids"]

   lowerCamelCase__:      str			  =model.generate(input_ids=UpperCAmelCase_       , attention_mask=inputs["attention_mask"]       , max_new_tokens=12)

   lowerCamelCase__:      Tuple			  =tokenizer(sentences[0]       , return_tensors="tf").input_ids
   lowerCamelCase__:      Union[str, Any]			  =model.generate(input_ids=UpperCAmelCase_       , max_new_tokens=12)

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

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

   lowerCamelCase__:      int			  =[
       "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 (     lowercase__   ,	lowercase__						):

      if density <= 0:
            raise ValueError('''Impossible fluid density'''						)
      if bulk_modulus <= 0:
            raise ValueError('''Impossible bulk modulus'''						)

      return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()


