



'''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"""




  a_												=XGLMConfig
  a_												={}
  a_												="""gelu"""




  def __init__( self      :	Union[str, Any]			,	_a      :	Optional[int]			,	_a      :	Dict=14			,	_a      :	Optional[int]=7			,	_a      :	List[Any]=True			,	_a      :	List[Any]=True			,	_a      :	Any=True			,	_a      :	Optional[int]=99			,	_a      :	List[str]=32			,	_a      :	str=2			,	_a      :	int=4			,	_a      :	Union[str, Any]=37			,	_a      :	Dict="gelu"			,	_a      :	List[Any]=0.1			,	_a      :	Optional[Any]=0.1			,	_a      :	Tuple=512			,	_a      :	Union[str, Any]=0.02			,	)					->					Optional[int]:
    __lowerCamelCase       :       List[str]           = parent
    __lowerCamelCase       :       Union[str, Any]           = batch_size
    __lowerCamelCase       :       Optional[Any]           = seq_length
    __lowerCamelCase       :       List[Any]           = is_training
    __lowerCamelCase       :       Optional[Any]           = use_input_mask
    __lowerCamelCase       :       int           = use_labels
    __lowerCamelCase       :       List[str]           = vocab_size
    __lowerCamelCase       :       Optional[Any]           = d_model
    __lowerCamelCase       :       Tuple           = num_hidden_layers
    __lowerCamelCase       :       Union[str, Any]           = num_attention_heads
    __lowerCamelCase       :       Union[str, Any]           = ffn_dim
    __lowerCamelCase       :       List[Any]           = activation_function
    __lowerCamelCase       :       Dict           = activation_dropout
    __lowerCamelCase       :       Any           = attention_dropout
    __lowerCamelCase       :       Tuple           = max_position_embeddings
    __lowerCamelCase       :       Dict           = initializer_range
    __lowerCamelCase       :       str           = None
    __lowerCamelCase       :       Any           = 0
    __lowerCamelCase       :       Dict           = 2
    __lowerCamelCase       :       str           = 1




  def 						_lowercase     ( self      :	int   )					->					List[str]:
    return XGLMConfig.from_pretrained('facebook/xglm-564M'   )




  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    __lowerCamelCase       :       Any           = tf.clip_by_value(
        ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size   )			,	clip_value_min=0			,	clip_value_max=3   )

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

    __lowerCamelCase       :       List[Any]           = self.get_config()

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

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
    )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Tuple:
    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=_a			,	bos_token_id=self.bos_token_id			,	eos_token_id=self.eos_token_id			,	pad_token_id=self.pad_token_id			,	return_dict=_a			,	)




  def 						_lowercase     ( self      :	List[str]   )					->					Tuple:
    __lowerCamelCase       :       Tuple           = self.prepare_config_and_inputs()

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

    __lowerCamelCase       :       int           = {
        'input_ids': input_ids,
        'head_mask': head_mask,
    }

    return config, inputs_dict






@require_tf
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  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      :	List[str]   )					->					str:
    __lowerCamelCase       :       List[str]           = TFXGLMModelTester(self   )
    __lowerCamelCase       :       Optional[int]           = ConfigTester(self			,	config_class=_a			,	n_embd=37   )




  def 						_lowercase     ( self      :	Any   )					->					Dict:
    self.config_tester.run_common_tests()




  @slow
  def 						_lowercase     ( self      :	Tuple   )					->					str:
    for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __lowerCamelCase       :       List[Any]           = TFXGLMModel.from_pretrained(_a   )
      self.assertIsNotNone(_a   )




  @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'   )
  def 						_lowercase     ( self      :	Dict   )					->					Dict:
    super().test_resize_token_embeddings()






@require_tf
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  @slow
  def 						_lowercase     ( self      :	Dict			,	_a      :	str=True   )					->					str:
    __lowerCamelCase       :       str           = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )
    __lowerCamelCase       :       List[Any]           = tf.convert_to_tensor([[2, 268, 9865]]			,	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       :       List[Any]           = [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
    # fmt: on
    __lowerCamelCase       :       Optional[int]           = model.generate(_a			,	do_sample=_a			,	num_beams=1   )
    if verify_outputs:
      self.assertListEqual(output_ids[0].numpy().tolist()			,	_a   )




  @slow
  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       :       List[Any]           = XGLMTokenizer.from_pretrained('facebook/xglm-564M'   )
    __lowerCamelCase       :       str           = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )

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

    __lowerCamelCase       :       List[Any]           = (
        'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
    )
    self.assertEqual(_a			,	_a   )




  @slow
  def 						_lowercase     ( self      :	str   )					->					Optional[Any]:
    __lowerCamelCase       :       int           = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )
    __lowerCamelCase       :       Tuple           = XGLMTokenizer.from_pretrained('facebook/xglm-564M'   )

    __lowerCamelCase       :       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       :       Optional[int]           = tokenizer(_a			,	return_tensors='tf'			,	padding=_a   )
    __lowerCamelCase       :       str           = inputs['input_ids']

    __lowerCamelCase       :       Union[str, Any]           = model.generate(input_ids=_a			,	attention_mask=inputs['attention_mask']			,	max_new_tokens=12   )

    __lowerCamelCase       :       Any           = tokenizer(sentences[0]			,	return_tensors='tf'   ).input_ids
    __lowerCamelCase       :       Dict           = model.generate(input_ids=_a			,	max_new_tokens=12   )

    __lowerCamelCase       :       int           = tokenizer(sentences[1]			,	return_tensors='tf'   ).input_ids
    __lowerCamelCase       :       int           = model.generate(input_ids=_a			,	max_new_tokens=12   )

    __lowerCamelCase       :       Tuple           = tokenizer.batch_decode(_a			,	skip_special_tokens=_a   )
    __lowerCamelCase       :       Optional[int]           = tokenizer.decode(output_non_padded[0]			,	skip_special_tokens=_a   )
    __lowerCamelCase       :       Tuple           = tokenizer.decode(output_padded[0]			,	skip_special_tokens=_a   )

    __lowerCamelCase       :       str           = [
        '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(_a			,	_a   )
    self.assertListEqual(_a			,	[non_padded_sentence, padded_sentence]   )











'''simple docstring'''





def        a_     (							_lowerCAmelCase       )      ->      list:
  if n_term == "":
    return []
  __lowerCamelCase       :       list           = []
  for temp in range(int(_lowerCAmelCase       )       ):
    series.append(F'1/{temp + 1}' if series else '1'       )
  return series


if __name__ == "__main__":
   _UpperCamelCase											=						input('Enter the last number (nth term) of the Harmonic Series')
   print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
   print(harmonic_series(nth_term))






