






'''simple docstring'''




import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

class   _UpperCAmelCase     (	enum.Enum		):




   """simple docstring"""

   snake_case       							=		0
   snake_case       							=		1
   snake_case       							=		2





@add_end_docstrings(snake_case_		)
class   _UpperCAmelCase     (	snake_case_		):




   """simple docstring"""

   snake_case       							=		'\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '

   def __init__(						self					:      str , *__UpperCAmelCase					:      List[Any] , **__UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         super().__init__(*__UpperCAmelCase , **__UpperCAmelCase						)
         self.check_model_type(
             TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING						)
         if "prefix" not in self._preprocess_params:
               # This is very specific. The logic is quite complex and needs to be done
               # as a "default".
               # It also defines both some preprocess_kwargs and generate_kwargs
               # which is why we cannot put them in their respective methods.
               _A     	=      None
               if self.model.config.prefix is not None:
                     _A     	=      self.model.config.prefix
               if prefix is None and self.model.__class__.__name__ in [
                   "XLNetLMHeadModel",
                   "TransfoXLLMHeadModel",
                   "TFXLNetLMHeadModel",
                   "TFTransfoXLLMHeadModel",
               ]:
                     # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                     _A     	=      self.XL_PREFIX
               if prefix is not None:
                     # Recalculate some generate_kwargs linked to prefix.
                     _A	,      _A	,      _A     	=      self._sanitize_parameters(prefix=__UpperCAmelCase , **self._forward_params						)
                     _A     	=      {**self._preprocess_params, **preprocess_params}
                     _A     	=      {**self._forward_params, **forward_params}

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      str=None , __UpperCAmelCase					:      List[Any]=None , __UpperCAmelCase					:      str=None , __UpperCAmelCase					:      List[str]=None , __UpperCAmelCase					:      Any=None , __UpperCAmelCase					:      int=None , __UpperCAmelCase					:      str=None , __UpperCAmelCase					:      int=None , **__UpperCAmelCase					:      Union[str, Any] , ):





         '''simple docstring'''





         _A     	=      {}
         if prefix is not None:
               _A     	=      prefix
         if prefix:
               _A     	=      self.tokenizer(
                   __UpperCAmelCase , padding=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors=self.framework						)
               _A     	=      prefix_inputs["input_ids"].shape[-1]

         if handle_long_generation is not None:
               if handle_long_generation not in {"hole"}:
                     raise ValueError(
                         f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
                         " [None, \'hole\']"						)
               _A     	=      handle_long_generation

         preprocess_params.update(__UpperCAmelCase						)
         _A     	=      generate_kwargs

         _A     	=      {}
         if return_full_text is not None and return_type is None:
               if return_text is not None:
                     raise ValueError("`return_text` is mutually exclusive with `return_full_text`"						)
               if return_tensors is not None:
                     raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`"						)
               _A     	=      ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
         if return_tensors is not None and return_type is None:
               if return_text is not None:
                     raise ValueError("`return_text` is mutually exclusive with `return_tensors`"						)
               _A     	=      ReturnType.TENSORS
         if return_type is not None:
               _A     	=      return_type
         if clean_up_tokenization_spaces is not None:
               _A     	=      clean_up_tokenization_spaces

         if stop_sequence is not None:
               _A     	=      self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase						)
               if len(__UpperCAmelCase						) > 1:
                     warnings.warn(
                         "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
                         " the stop sequence will be used as the stop sequence string in the interim."						)
               _A     	=      stop_sequence_ids[0]

         return preprocess_params, forward_params, postprocess_params

   def 		lowerCAmelCase					(						self					:      List[Any] , *__UpperCAmelCase					:      Dict , **__UpperCAmelCase					:      int						):





         '''simple docstring'''





         if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               kwargs.update({"add_space_before_punct_symbol": True}						)

         return super()._parse_and_tokenize(*__UpperCAmelCase , **__UpperCAmelCase						)

   def __call__(						self					:      Optional[int] , __UpperCAmelCase					:      Any , **__UpperCAmelCase					:      str						):





         '''simple docstring'''





         return super().__call__(__UpperCAmelCase , **__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      int , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Optional[int]="" , __UpperCAmelCase					:      List[Any]=None , **__UpperCAmelCase					:      int						):





         '''simple docstring'''





         _A     	=      self.tokenizer(
             prefix + prompt_text , padding=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors=self.framework						)
         _A     	=      prompt_text

         if handle_long_generation == "hole":
               _A     	=      inputs["input_ids"].shape[-1]
               if "max_new_tokens" in generate_kwargs:
                     _A     	=      generate_kwargs["max_new_tokens"]
               else:
                     _A     	=      generate_kwargs.get("max_length" , self.model.config.max_length						) - cur_len
                     if new_tokens < 0:
                           raise ValueError("We cannot infer how many new tokens are expected"						)
               if cur_len + new_tokens > self.tokenizer.model_max_length:
                     _A     	=      self.tokenizer.model_max_length - new_tokens
                     if keep_length <= 0:
                           raise ValueError(
                               "We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
                               " models max length"						)

                     _A     	=      inputs["input_ids"][:, -keep_length:]
                     if "attention_mask" in inputs:
                           _A     	=      inputs["attention_mask"][:, -keep_length:]

         return inputs

   def 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      Union[str, Any] , **__UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         _A     	=      model_inputs["input_ids"]
         _A     	=      model_inputs.get("attention_mask" , __UpperCAmelCase						)
         # Allow empty prompts
         if input_ids.shape[1] == 0:
               _A     	=      None
               _A     	=      None
               _A     	=      1
         else:
               _A     	=      input_ids.shape[0]
         _A     	=      model_inputs.pop("prompt_text"						)

         # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
         # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
         _A     	=      generate_kwargs.pop("prefix_length" , 0						)
         if prefix_length > 0:
               _A     	=      "max_new_tokens" in generate_kwargs or (
                   "generation_config" in generate_kwargs
                   and generate_kwargs["generation_config"].max_new_tokens is not None
               )
               if not has_max_new_tokens:
                     _A     	=      generate_kwargs.get("max_length"						) or self.model.config.max_length
                     generate_kwargs["max_length"] += prefix_length
               _A     	=      "min_new_tokens" in generate_kwargs or (
                   "generation_config" in generate_kwargs
                   and generate_kwargs["generation_config"].min_new_tokens is not None
               )
               if not has_min_new_tokens and "min_length" in generate_kwargs:
                     generate_kwargs["min_length"] += prefix_length

        # BS x SL
         _A     	=      self.model.generate(input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase , **__UpperCAmelCase						)
         _A     	=      generated_sequence.shape[0]
         if self.framework == "pt":
               _A     	=      generated_sequence.reshape(__UpperCAmelCase , out_b // in_b , *generated_sequence.shape[1:]						)
         elif self.framework == "tf":
               _A     	=      tf.reshape(__UpperCAmelCase , (in_b, out_b // in_b, *generated_sequence.shape[1:])						)
         return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      str , __UpperCAmelCase					:      Optional[int]=ReturnType.FULL_TEXT , __UpperCAmelCase					:      int=True						):





         '''simple docstring'''





         _A     	=      model_outputs["generated_sequence"][0]
         _A     	=      model_outputs["input_ids"]
         _A     	=      model_outputs["prompt_text"]
         _A     	=      generated_sequence.numpy().tolist()
         _A     	=      []
         for sequence in generated_sequence:
               if return_type == ReturnType.TENSORS:
                     _A     	=      {"generated_token_ids": sequence}
               elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                     # Decode text
                     _A     	=      self.tokenizer.decode(
                         __UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )

                     # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                     if input_ids is None:
                           _A     	=      0
                     else:
                           _A     	=      len(
                               self.tokenizer.decode(
                                   input_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )						)

                     if return_type == ReturnType.FULL_TEXT:
                           _A     	=      prompt_text + text[prompt_length:]
                     else:
                           _A     	=      text[prompt_length:]

                     _A     	=      {"generated_text": all_text}
               records.append(__UpperCAmelCase						)

         return records







"""simple docstring"""


import math






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [True] * n
			lowerCamelCase_					=  False
			lowerCamelCase_					=  False
			lowerCamelCase_					=  True

			for i in range(3	,int(n**0.5 + 1						)	,2						):
						lowerCamelCase_					=  i * 2
						while index < n:
									lowerCamelCase_					=  False
									lowerCamelCase_					=  index + i

			lowerCamelCase_					=  [2]

			for i in range(3	,lowerCAmelCase__	,2						):
						if is_prime[i]:
									primes.append(lowerCAmelCase__						)

			return primes






def  lowercase						(	lowerCAmelCase__ = 999_966_663_333						):
			lowerCamelCase_					=  math.floor(math.sqrt(lowerCAmelCase__						)						) + 100
			lowerCamelCase_					=  prime_sieve(lowerCAmelCase__						)

			lowerCamelCase_					=  0
			lowerCamelCase_					=  0
			lowerCamelCase_					=  primes[prime_index]

			while (last_prime**2) <= limit:
						lowerCamelCase_					=  primes[prime_index + 1]

						lowerCamelCase_					=  last_prime**2
						lowerCamelCase_					=  next_prime**2

						# Get numbers divisible by lps(current)
						lowerCamelCase_					=  lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						lowerCamelCase_					=  upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						lowerCamelCase_					=  0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						lowerCamelCase_					=  next_prime
						prime_index += 1

			return matches_sum


if __name__ == "__main__":
						print(solution())



