


'''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     _lowerCamelCase       :




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    XGLMConfig
 __lowercase					:		List[Any]     				=    {}
 __lowercase					:		Dict     				=    '''gelu'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=14   ,   __lowercase=7   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=99   ,   __lowercase=32   ,   __lowercase=2   ,   __lowercase=4   ,   __lowercase=37   ,   __lowercase="gelu"   ,   __lowercase=0.1   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=0.0_2   ,   ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

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

  __A	:		List[str]	  =      self.get_config()

  __A	:		Optional[int]	  =      floats_tensor([self.num_hidden_layers, self.num_attention_heads]   ,   2    )

  return (
      config,
      input_ids,
      input_mask,
      head_mask,
  )


 def 			snake_case__ (   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=__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 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      self.prepare_config_and_inputs()

  (
      (
      __A
  )  ,(
      __A
  )  ,(
      __A
  )  ,(
      __A
  )  ,
  )	:		Dict	  =      config_and_inputs

  __A	:		List[str]	  =      {
      'input_ids': input_ids,
      'head_mask': head_mask,
  }

  return config, inputs_dict


@require_tf
class     _lowerCamelCase       (     snake_case_     ,		snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __lowercase					:		Union[str, Any]     				=    (TFXGLMForCausalLM,) if is_tf_available() else ()
 __lowercase					:		Optional[Any]     				=    (
     {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __lowercase					:		List[Any]     				=    False
 __lowercase					:		Dict     				=    False
 __lowercase					:		str     				=    False


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      TFXGLMModelTester(self    )
  __A	:		Union[str, Any]	  =      ConfigTester(self   ,   config_class=__lowercase   ,   n_embd=37    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.config_tester.run_common_tests()


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







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







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

  """simple docstring"""







  super().test_resize_token_embeddings()


@require_tf
class     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 @slow
 def 			snake_case__ (   self   ,   __lowercase=True    ):

  """simple docstring"""







  __A	:		int	  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
  __A	:		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
  __A	:		List[str]	  =      [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
  __A	:		int	  =      model.generate(__lowercase   ,   do_sample=__lowercase   ,   num_beams=1    )
  if verify_outputs:
   self.assertListEqual(output_ids[0].numpy().tolist()   ,   __lowercase    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )
  __A	:		Optional[Any]	  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )

  tf.random.set_seed(0    )
  __A	:		Any	  =      tokenizer('Today is a nice day and'   ,   return_tensors='tf'    )
  __A	:		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'    ):
   __A	:		Tuple	  =      model.generate(__lowercase   ,   do_sample=__lowercase   ,   seed=[7, 0]    )
  __A	:		Dict	  =      tokenizer.decode(output_ids[0]   ,   skip_special_tokens=__lowercase    )

  __A	:		Dict	  =      (
      'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
  )
  self.assertEqual(__lowercase   ,   __lowercase    )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
  __A	:		int	  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )

  __A	:		Optional[Any]	  =      'left'

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

  __A	:		Tuple	  =      tokenizer(__lowercase   ,   return_tensors='tf'   ,   padding=__lowercase    )
  __A	:		int	  =      inputs['input_ids']

  __A	:		Optional[Any]	  =      model.generate(input_ids=__lowercase   ,   attention_mask=inputs['attention_mask']   ,   max_new_tokens=12    )

  __A	:		int	  =      tokenizer(sentences[0]   ,   return_tensors='tf'    ).input_ids
  __A	:		List[Any]	  =      model.generate(input_ids=__lowercase   ,   max_new_tokens=12    )

  __A	:		int	  =      tokenizer(sentences[1]   ,   return_tensors='tf'    ).input_ids
  __A	:		Optional[Any]	  =      model.generate(input_ids=__lowercase   ,   max_new_tokens=12    )

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

  __A	:		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 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'''
from __future__ import annotations

import math
import random
from typing import Any

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		list[Any]	  =      []
  __A	:		int	  =      0
  __A	:		int	  =      0


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.head == self.tail


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.data.append(__lowercase    )
  __A	:		Any	  =      self.tail + 1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.data[self.head]
  __A	:		Union[str, Any]	  =      self.head + 1
  return ret


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.tail - self.head







 def 			snake_case__ (   self    ):

  """simple docstring"""







  print(self.data    )
  print('**************'    )
  print(self.data[self.head : self.tail]    )


class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      data
  __A	:		MyNode | None	  =      None
  __A	:		MyNode | None	  =      None
  __A	:		int	  =      1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.data


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.left


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.right


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.height


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      data


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      node


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      node







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      height


def       _lowercase							(					UpperCamelCase__							:       MyNode | None      ):
 if node is None:
  return 0
 return node.get_height()


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 if a > b:
  return a
 return b


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('left rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_left()
 assert ret is not None
 node.set_left(ret.get_right()      )
 ret.set_right(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Optional[Any]	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('right rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_right()
 assert ret is not None
 node.set_right(ret.get_left()      )
 ret.set_left(UpperCamelCase__      )
 __A	:		List[str]	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Any	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Dict	  =      node.get_left()
 assert left_child is not None
 node.set_left(left_rotation(UpperCamelCase__      )      )
 return right_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Tuple	  =      node.get_right()
 assert right_child is not None
 node.set_right(right_rotation(UpperCamelCase__      )      )
 return left_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode | None,					UpperCamelCase__							:       Any      ):
 if node is None:
  return MyNode(UpperCamelCase__      )
 if data < node.get_data():
  node.set_left(insert_node(node.get_left(),					UpperCamelCase__      )      )
  if (
      get_height(node.get_left()      ) - get_height(node.get_right()      ) == 2
  ):  # an unbalance detected
   __A	:		Union[str, Any]	  =      node.get_left()
   assert left_child is not None
   if (
       data < left_child.get_data()
   ):  # new node is the left child of the left child
    __A	:		Optional[Any]	  =      right_rotation(UpperCamelCase__      )
   else:
    __A	:		Any	  =      lr_rotation(UpperCamelCase__      )
 else:
  node.set_right(insert_node(node.get_right(),					UpperCamelCase__      )      )
  if get_height(node.get_right()      ) - get_height(node.get_left()      ) == 2:
   __A	:		Union[str, Any]	  =      node.get_right()
   assert right_child is not None
   if data < right_child.get_data():
    __A	:		int	  =      rl_rotation(UpperCamelCase__      )
   else:
    __A	:		Optional[Any]	  =      left_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 return node


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		str	  =      root.get_right()
  if right_child is None:
   break
  __A	:		Union[str, Any]	  =      right_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		Union[str, Any]	  =      root.get_left()
  if left_child is None:
   break
  __A	:		int	  =      left_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode,					UpperCamelCase__							:       Any      ):
 __A	:		List[Any]	  =      root.get_left()
 __A	:		int	  =      root.get_right()
 if root.get_data() == data:
  if left_child is not None and right_child is not None:
   __A	:		List[Any]	  =      get_left_most(UpperCamelCase__      )
   root.set_data(UpperCamelCase__      )
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
  elif left_child is not None:
   __A	:		Tuple	  =      left_child
  elif right_child is not None:
   __A	:		str	  =      right_child
  else:
   return None
 elif root.get_data() > data:
  if left_child is None:
   print('No such data'      )
   return root
  else:
   root.set_left(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
 else:  # root.get_data() < data
  if right_child is None:
   return root
  else:
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )

 if get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == 2:
  assert right_child is not None
  if get_height(right_child.get_right()      ) > get_height(right_child.get_left()      ):
   __A	:		List[str]	  =      left_rotation(UpperCamelCase__      )
  else:
   __A	:		Union[str, Any]	  =      rl_rotation(UpperCamelCase__      )
 elif get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == -2:
  assert left_child is not None
  if get_height(left_child.get_left()      ) > get_height(left_child.get_right()      ):
   __A	:		str	  =      right_rotation(UpperCamelCase__      )
  else:
   __A	:		List[Any]	  =      lr_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(root.get_right()      ),					get_height(root.get_left()      )      ) + 1
 root.set_height(UpperCamelCase__      )
 return root

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		MyNode | None	  =      None


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return get_height(self.root    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('insert:' + str(__lowercase    )    )
  __A	:		Optional[int]	  =      insert_node(self.root   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('delete:' + str(__lowercase    )    )
  if self.root is None:
   print('Tree is empty!'    )
   return
  __A	:		List[Any]	  =      del_node(self.root   ,   __lowercase    )







 def __str__(   self   ,   ):  # a level traversale, gives a more intuitive look on the tree

  """simple docstring"""







  __A	:		Union[str, Any]	  =      ''
  __A	:		Optional[Any]	  =      MyQueue()
  q.push(self.root    )
  __A	:		Tuple	  =      self.get_height()
  if layer == 0:
   return output
  __A	:		List[Any]	  =      0
  while not q.is_empty():
   __A	:		Union[str, Any]	  =      q.pop()
   __A	:		Any	  =      ' ' * int(math.pow(2   ,   layer - 1    )    )
   output += space
   if node is None:
    output += "*"
    q.push(__lowercase    )
    q.push(__lowercase    )
   else:
    output += str(node.get_data()    )
    q.push(node.get_left()    )
    q.push(node.get_right()    )
   output += space
   __A	:		Optional[int]	  =      cnt + 1
   for i in range(100    ):
    if cnt == math.pow(2   ,   __lowercase    ) - 1:
     __A	:		Optional[int]	  =      layer - 1
     if layer == 0:
      output += "\n*************************************"
      return output
     output += "\n"
     break
  output += "\n*************************************"
  return output


def       _lowercase							(					):
 import doctest

 doctest.testmod()


if __name__ == "__main__":
 _test()
 UpperCAmelCase_			:  Optional[Any]										=   AVLtree()
 UpperCAmelCase_			:  Dict										=   list(range(1_0))
 random.shuffle(lst)
 for i in lst:
  t.insert(i)
  print(str(t))
 random.shuffle(lst)
 for i in lst:
  t.del_node(i)
  print(str(t))






