
"""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 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :int				 =						XGLMConfig
			UpperCAmelCase_  :Union[str, Any]				 =						{}
			UpperCAmelCase_  :List[str]				 =						"gelu"






			def __init__(					self       ,   __A       ,   __A=14       ,   __A=7       ,   __A=True       ,   __A=True       ,   __A=True       ,   __A=99       ,   __A=32       ,   __A=2       ,   __A=4       ,   __A=37       ,   __A="gelu"       ,   __A=0.1       ,   __A=0.1       ,   __A=512       ,   __A=0.0_2       ,   )			->     Any:
						lowerCAmelCase_    :Optional[Any]			     =  parent
						lowerCAmelCase_    :Tuple			     =  batch_size
						lowerCAmelCase_    :str			     =  seq_length
						lowerCAmelCase_    :Dict			     =  is_training
						lowerCAmelCase_    :Optional[int]			     =  use_input_mask
						lowerCAmelCase_    :int			     =  use_labels
						lowerCAmelCase_    :Dict			     =  vocab_size
						lowerCAmelCase_    :Optional[int]			     =  d_model
						lowerCAmelCase_    :Any			     =  num_hidden_layers
						lowerCAmelCase_    :str			     =  num_attention_heads
						lowerCAmelCase_    :Tuple			     =  ffn_dim
						lowerCAmelCase_    :List[Any]			     =  activation_function
						lowerCAmelCase_    :List[str]			     =  activation_dropout
						lowerCAmelCase_    :Union[str, Any]			     =  attention_dropout
						lowerCAmelCase_    :Optional[Any]			     =  max_position_embeddings
						lowerCAmelCase_    :Any			     =  initializer_range
						lowerCAmelCase_    :List[Any]			     =  None
						lowerCAmelCase_    :Tuple			     =  0
						lowerCAmelCase_    :Optional[Any]			     =  2
						lowerCAmelCase_    :List[Any]			     =  1






			def  __lowerCAmelCase (					self       )			->     Any:
						return XGLMConfig.from_pretrained("""facebook/xglm-564M"""       )






			def  __lowerCAmelCase (					self       )			->     str:
						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_    :int			     =  None
						if self.use_input_mask:
									lowerCAmelCase_    :str			     =  random_attention_mask([self.batch_size, self.seq_length]       )

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

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

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)






			def  __lowerCAmelCase (					self       )			->     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=__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  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :List[Any]			     =  self.prepare_config_and_inputs()

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

						lowerCAmelCase_    :List[str]			     =  {
						    """input_ids""": input_ids,
						    """head_mask""": head_mask,
						}

						return config, inputs_dict




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			A__	, A__	, unittest.TestCase							):
			UpperCAmelCase_  :Optional[int]				 =						(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			UpperCAmelCase_  :Union[str, Any]				 =						(TFXGLMForCausalLM,) if is_tf_available() else ()
			UpperCAmelCase_  :Dict				 =						(
			    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			UpperCAmelCase_  :Union[str, Any]				 =						False
			UpperCAmelCase_  :int				 =						False
			UpperCAmelCase_  :Optional[Any]				 =						False






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Dict			     =  TFXGLMModelTester(self       )
						lowerCAmelCase_    :int			     =  ConfigTester(self       ,   config_class=__UpperCAmelCase       ,   n_embd=37       )






			def  __lowerCAmelCase (					self       )			->     int:
						self.config_tester.run_common_tests()






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


						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :Union[str, Any]			     =  TFXGLMModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )






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




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			@slow
			def  __lowerCAmelCase (					self       ,   __A=True       )			->     Optional[int]:
						lowerCAmelCase_    :Optional[int]			     =  TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""       )
						lowerCAmelCase_    :Optional[int]			     =  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_    :str			     =  [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_    :str			     =  model.generate(__UpperCAmelCase       ,   do_sample=__UpperCAmelCase       ,   num_beams=1       )
						if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()       ,   __UpperCAmelCase       )






			@slow
			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :Optional[Any]			     =  XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""       )
						lowerCAmelCase_    :List[str]			     =  TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""       )

						tf.random.set_seed(0       )
						lowerCAmelCase_    :str			     =  tokenizer("""Today is a nice day and"""       ,   return_tensors="""tf"""       )
						lowerCAmelCase_    :int			     =  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_    :Union[str, Any]			     =  tokenizer.decode(output_ids[0]       ,   skip_special_tokens=__UpperCAmelCase       )

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






			@slow
			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :Any			     =  TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""       )
						lowerCAmelCase_    :List[Any]			     =  XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""       )

						lowerCAmelCase_    :Dict			     =  """left"""

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

						lowerCAmelCase_    :str			     =  tokenizer(__UpperCAmelCase       ,   return_tensors="""tf"""       ,   padding=__UpperCAmelCase       )
						lowerCAmelCase_    :List[str]			     =  inputs["""input_ids"""]

						lowerCAmelCase_    :Tuple			     =  model.generate(input_ids=__UpperCAmelCase       ,   attention_mask=inputs["""attention_mask"""]       ,   max_new_tokens=12       )

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

						lowerCAmelCase_    :Tuple			     =  tokenizer(sentences[1]       ,   return_tensors="""tf"""       ).input_ids
						lowerCAmelCase_    :Optional[int]			     =  model.generate(input_ids=__UpperCAmelCase       ,   max_new_tokens=12       )

						lowerCAmelCase_    :int			     =  tokenizer.batch_decode(__UpperCAmelCase       ,   skip_special_tokens=__UpperCAmelCase       )
						lowerCAmelCase_    :List[Any]			     =  tokenizer.decode(output_non_padded[0]       ,   skip_special_tokens=__UpperCAmelCase       )
						lowerCAmelCase_    :List[str]			     =  tokenizer.decode(output_padded[0]       ,   skip_special_tokens=__UpperCAmelCase       )

						lowerCAmelCase_    :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 math import factorial

UpperCAmelCase__				   =		{str(digit): factorial(digit) for digit in range(10)}
def 						_a    (      a :int )							->			int:
 if not isinstance(a	,							a ):
  raise TypeError('''Parameter number must be int''' )

 if number < 0:
  raise ValueError('''Parameter number must be greater than or equal to 0''' )

 # Converts number in string to iterate on its digits and adds its factorial.
 return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )
def 						_a    (      a :int = 60	,							a :int = 1_000_000 )							->			int:

 if not isinstance(a	,							a ) or not isinstance(a	,							a ):
  raise TypeError('''Parameters chain_length and number_limit must be int''' )

 if chain_length <= 0 or number_limit <= 0:
  raise ValueError(
      '''Parameters chain_length and number_limit must be greater than 0''' )

 # the counter for the chains with the exact desired length
 a		  =      0
 # the cached sizes of the previous chains
 a		  =      {}

 for start_chain_element in range(1	,							a ):
  # The temporary set will contain the elements of the chain
  a		  =      set()
  a		  =      0

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  a		  =      start_chain_element
  while (
      chain_element not in chain_sets_lengths
      and chain_element not in chain_set
      and chain_set_length <= chain_length
  ):
   chain_set.add(a )
   chain_set_length += 1
   a		  =      digit_factorial_sum(a )

  if chain_element in chain_sets_lengths:
   chain_set_length += chain_sets_lengths[chain_element]

  a		  =      chain_set_length

  # If chain contains the exact amount of elements increase the counter
  if chain_set_length == chain_length:
   chains_counter += 1

 return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f"""{solution()}""")


