





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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Any:



   '''simple docstring'''



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

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



   '''simple docstring'''



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

   lowerCamelCase__:				List[Any]        =None
   if self.use_input_mask:
     lowerCamelCase__:				Dict        =random_attention_mask([self.batch_size, self.seq_length])

   lowerCamelCase__:				Any        =self.get_config()

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

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
   )

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



   '''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    :       Union[str, Any])	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =self.prepare_config_and_inputs()

   (
       (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , 
   ):				str        =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    :       Dict)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =TFXGLMModelTester(self)
   lowerCamelCase__:				List[str]        =ConfigTester(self	,			config_class=UpperCAmelCase_	,			n_embd=37)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   self.config_tester.run_common_tests()

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



   '''simple docstring'''



   for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:				str        =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    :       Dict)	->Optional[int]:



   '''simple docstring'''



   super().test_resize_token_embeddings()



@require_tf
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int=True)	->Tuple:



   '''simple docstring'''



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



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =XGLMTokenizer.from_pretrained("facebook/xglm-564M")
   lowerCamelCase__:				str        =TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")

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



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
   lowerCamelCase__:				Dict        =XGLMTokenizer.from_pretrained("facebook/xglm-564M")

   lowerCamelCase__:				Union[str, Any]        ="left"

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

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

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

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

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

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

   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 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])








from ..utils import DummyObject, requires_backends



class        _SCREAMING_SNAKE_CASE						(     metaclass=__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["torch", "torchsde"]

 def __init__(self    :       Union[str, Any]	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Tuple)	->Any:



   '''simple docstring'''



   requires_backends(self	,			["torch", "torchsde"])

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Dict	,			*UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])







 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Tuple	,			*UpperCAmelCase_    :       Tuple	,			**UpperCAmelCase_    :       Any)	->Any:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])

