
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   :
 __lowerCAmelCase  :     int        =			XGLMConfig
 __lowerCAmelCase  :     List[Any]        =			{}
 __lowerCAmelCase  :     List[str]        =			'gelu'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=14					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=99					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=5_12					,  SCREAMING_SNAKE_CASE_=0.0_2					,  ):


   '''simple docstring'''




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


 def      lowercase__     (					self):


   '''simple docstring'''




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


 def      lowercase__     (					self):


   '''simple docstring'''




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

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

   lowercase__  :   str    		=      self.get_config()

   lowercase__  :   List[str]    		=      floats_tensor([self.num_hidden_layers, self.num_attention_heads]					,  2)

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
   )


 def      lowercase__     (					self):


   '''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=SCREAMING_SNAKE_CASE_					,  bos_token_id=self.bos_token_id					,  eos_token_id=self.eos_token_id					,  pad_token_id=self.pad_token_id					,  return_dict=SCREAMING_SNAKE_CASE_					,  )



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self.prepare_config_and_inputs()

   (
       lowercase__    
   )  :   str    		=      config_and_inputs

   lowercase__  :   Any    		=      {
       """input_ids""": input_ids,
       """head_mask""": head_mask,
   }

   return config, inputs_dict



@require_tf
class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Any        =			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __lowerCAmelCase  :     Dict        =			(TFXGLMForCausalLM,) if is_tf_available() else ()
 __lowerCAmelCase  :     Tuple        =			(
     {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __lowerCAmelCase  :     int        =			False
 __lowerCAmelCase  :     List[Any]        =			False
 __lowerCAmelCase  :     Any        =			False


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      TFXGLMModelTester(self)
   lowercase__  :   Optional[int]    		=      ConfigTester(self					,  config_class=SCREAMING_SNAKE_CASE_					,  n_embd=37)


 def      lowercase__     (					self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__  :   Optional[Any]    		=      TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)



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


   '''simple docstring'''




   super().test_resize_token_embeddings()



@require_tf
class       _snake_case   (	unittest.TestCase							):


 @slow
 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=True):


   '''simple docstring'''




   lowercase__  :   Any    		=      TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
   lowercase__  :   Optional[int]    		=      tf.convert_to_tensor([[2, 2_68, 98_65]]					,  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__  :   Union[str, Any]    		=      [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
   # fmt: on
   lowercase__  :   int    		=      model.generate(SCREAMING_SNAKE_CASE_					,  do_sample=SCREAMING_SNAKE_CASE_					,  num_beams=1)
   if verify_outputs:
     self.assertListEqual(output_ids[0].numpy().tolist()					,  SCREAMING_SNAKE_CASE_)


 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




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

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

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



 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




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

   lowercase__  :   str    		=      """left"""

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

   lowercase__  :   Dict    		=      tokenizer(SCREAMING_SNAKE_CASE_					,  return_tensors="""tf"""					,  padding=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      inputs["""input_ids"""]

   lowercase__  :   List[Any]    		=      model.generate(input_ids=SCREAMING_SNAKE_CASE_					,  attention_mask=inputs["""attention_mask"""]					,  max_new_tokens=12)

   lowercase__  :   Dict    		=      tokenizer(sentences[0]					,  return_tensors="""tf""").input_ids
   lowercase__  :   Union[str, Any]    		=      model.generate(input_ids=SCREAMING_SNAKE_CASE_					,  max_new_tokens=12)

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

   lowercase__  :   int    		=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      tokenizer.decode(output_non_padded[0]					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      tokenizer.decode(output_padded[0]					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Tuple    		=      [
       """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(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   self.assertListEqual(SCREAMING_SNAKE_CASE_					,  [non_padded_sentence, padded_sentence])






from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Any    		=      data
   lowercase__  :   Node | None    		=      None



class       _snake_case   :


 def __init__(					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      None
   lowercase__  :   int    		=      None


 def __iter__(					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      self.head
   while self.head:
     yield node.data
     lowercase__  :   str    		=      node.next
     if node == self.head:
       break


 def __len__(					self):


   '''simple docstring'''




   return sum(1 for _ in self)


 def __repr__(					self):


   '''simple docstring'''




   return "->".join(str(SCREAMING_SNAKE_CASE_) for item in iter(self))


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.insert_nth(len(self)					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.insert_nth(0					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if index < 0 or index > len(self):
     raise IndexError("""list index out of range.""")
   lowercase__  :   Optional[int]    		=      Node(SCREAMING_SNAKE_CASE_)
   if self.head is None:
     lowercase__  :   Union[str, Any]    		=      new_node  # first node points itself
     lowercase__  :   Dict    		=      new_node
   elif index == 0:  # insert at head
     lowercase__  :   int    		=      self.head
     lowercase__  :   int    		=      new_node
   else:
     lowercase__  :   List[str]    		=      self.head
     for _ in range(index - 1):
       lowercase__  :   Union[str, Any]    		=      temp.next
     lowercase__  :   int    		=      temp.next
     lowercase__  :   Optional[Any]    		=      new_node
     if index == len(self) - 1:  # insert at tail
       lowercase__  :   Union[str, Any]    		=      new_node


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.delete_nth(0)


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.delete_nth(len(self) - 1)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_ = 0):


   '''simple docstring'''




   if not 0 <= index < len(self):
     raise IndexError("""list index out of range.""")
   lowercase__  :   str    		=      self.head
   if self.head == self.tail:  # just one node
     lowercase__  :   List[Any]    		=      None
   elif index == 0:  # delete head node
     lowercase__  :   str    		=      self.tail.next.next
     lowercase__  :   List[Any]    		=      self.head.next
   else:
     lowercase__  :   Optional[int]    		=      self.head
     for _ in range(index - 1):
       lowercase__  :   List[Any]    		=      temp.next
     lowercase__  :   Optional[Any]    		=      temp.next
     lowercase__  :   Dict    		=      temp.next.next
     if index == len(self) - 1:  # delete at tail
       lowercase__  :   Tuple    		=      temp
   return delete_node.data



 def      lowercase__     (					self):


   '''simple docstring'''




   return len(self) == 0

def 		UpperCamelCase	(      )		->	None:


  '''simple docstring'''






  lowercase__  :   int    		=      CircularLinkedList()
  assert len(lowercase_   ) == 0
  assert circular_linked_list.is_empty() is True
  assert str(lowercase_   ) == ""

  try:
    circular_linked_list.delete_front()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_tail()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_nth(-1   )
    raise AssertionError
  except IndexError:
    assert True

  try:
    circular_linked_list.delete_nth(0   )
    raise AssertionError
  except IndexError:
    assert True

  assert circular_linked_list.is_empty() is True
  for i in range(5   ):
    assert len(lowercase_   ) == i
    circular_linked_list.insert_nth(lowercase_ , i + 1   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )

  circular_linked_list.insert_tail(6   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 7   )   )
  circular_linked_list.insert_head(0   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(0 , 7   )   )

  assert circular_linked_list.delete_front() == 0
  assert circular_linked_list.delete_tail() == 6
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )
  assert circular_linked_list.delete_nth(2   ) == 3

  circular_linked_list.insert_nth(2 , 3   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )

  assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
     import doctest

     doctest.testmod()






