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 					_lowercase    (     enum.Enum			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[str]          =    0
    SCREAMING_SNAKE_CASE:				str          =    1
    SCREAMING_SNAKE_CASE:				Any          =    2





@add_end_docstrings(UpperCAmelCase__			)
class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    '\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       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       super().__init__(*lowerCamelCase__       ,						**lowerCamelCase__ )
       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.
          lowerCAmelCase_:							Union[str, Any]       =	None
          if self.model.config.prefix is not None:
             lowerCAmelCase_:							Optional[int]       =	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.
             lowerCAmelCase_:							List[str]       =	self.XL_PREFIX
          if prefix is not None:
             # Recalculate some generate_kwargs linked to prefix.
             lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	self._sanitize_parameters(prefix=lowerCamelCase__       ,						**self._forward_params )
             lowerCAmelCase_:							str       =	{**self._preprocess_params, **preprocess_params}
             lowerCAmelCase_:							List[Any]       =	{**self._forward_params, **forward_params}




    def      _a    (    self       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Tuple       =	{}
       if prefix is not None:
          lowerCAmelCase_:							str       =	prefix
       if prefix:
          lowerCAmelCase_:							Any       =	self.tokenizer(
              lowerCamelCase__       ,						padding=lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__       ,						return_tensors=self.framework )
          lowerCAmelCase_:							str       =	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']" )
          lowerCAmelCase_:							str       =	handle_long_generation

       preprocess_params.update(lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	generate_kwargs

       lowerCAmelCase_:							Optional[Any]       =	{}
       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`" )
          lowerCAmelCase_:							Union[str, Any]       =	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`" )
          lowerCAmelCase_:							int       =	ReturnType.TENSORS
       if return_type is not None:
          lowerCAmelCase_:							int       =	return_type
       if clean_up_tokenization_spaces is not None:
          lowerCAmelCase_:							Tuple       =	clean_up_tokenization_spaces

       if stop_sequence is not None:
          lowerCAmelCase_:							Union[str, Any]       =	self.tokenizer.encode(lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__ )
          if len(lowerCamelCase__ ) > 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." )
          lowerCAmelCase_:							Optional[Any]       =	stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params




    def      _a    (    self       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       # Parse arguments
       if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
          kwargs.update({"add_space_before_punct_symbol": True} )

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




    def __call__(    self       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       return super().__call__(lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=""       ,						lowerCamelCase__=None       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	self.tokenizer(
           prefix + prompt_text       ,						padding=lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__       ,						return_tensors=self.framework )
       lowerCAmelCase_:							Optional[int]       =	prompt_text

       if handle_long_generation == "hole":
          lowerCAmelCase_:							Any       =	inputs["input_ids"].shape[-1]
          if "max_new_tokens" in generate_kwargs:
             lowerCAmelCase_:							List[str]       =	generate_kwargs["max_new_tokens"]
          else:
             lowerCAmelCase_:							Optional[Any]       =	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:
             lowerCAmelCase_:							Tuple       =	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" )

             lowerCAmelCase_:							Tuple       =	inputs["input_ids"][:, -keep_length:]
             if "attention_mask" in inputs:
                lowerCAmelCase_:							Any       =	inputs["attention_mask"][:, -keep_length:]

       return inputs




    def      _a    (    self       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	model_inputs["input_ids"]
       lowerCAmelCase_:							Optional[int]       =	model_inputs.get("attention_mask"       ,						lowerCamelCase__ )
       # Allow empty prompts
       if input_ids.shape[1] == 0:
          lowerCAmelCase_:							List[Any]       =	None
          lowerCAmelCase_:							List[str]       =	None
          lowerCAmelCase_:							List[Any]       =	1
       else:
          lowerCAmelCase_:							str       =	input_ids.shape[0]
       lowerCAmelCase_:							List[str]       =	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.
       lowerCAmelCase_:							Tuple       =	generate_kwargs.pop("prefix_length"       ,						0 )
       if prefix_length > 0:
          lowerCAmelCase_:							str       =	"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:
             lowerCAmelCase_:							int       =	generate_kwargs.get("max_length" ) or self.model.config.max_length
             generate_kwargs["max_length"] += prefix_length
          lowerCAmelCase_:							Optional[Any]       =	"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
       lowerCAmelCase_:							Optional[Any]       =	self.model.generate(input_ids=lowerCamelCase__       ,						attention_mask=lowerCamelCase__       ,						**lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	generated_sequence.shape[0]
       if self.framework == "pt":
          lowerCAmelCase_:							Optional[int]       =	generated_sequence.reshape(lowerCamelCase__       ,						out_b // in_b       ,						*generated_sequence.shape[1:] )
       elif self.framework == "tf":
          lowerCAmelCase_:							Any       =	tf.reshape(lowerCamelCase__       ,						(in_b, out_b // in_b, *generated_sequence.shape[1:]) )
       return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=ReturnType.FULL_TEXT       ,						lowerCamelCase__=True ):
       lowerCAmelCase_:							Tuple       =	model_outputs["generated_sequence"][0]
       lowerCAmelCase_:							Union[str, Any]       =	model_outputs["input_ids"]
       lowerCAmelCase_:							Any       =	model_outputs["prompt_text"]
       lowerCAmelCase_:							str       =	generated_sequence.numpy().tolist()
       lowerCAmelCase_:							int       =	[]
       for sequence in generated_sequence:
          if return_type == ReturnType.TENSORS:
             lowerCAmelCase_:							Any       =	{"generated_token_ids": sequence}
          elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
             # Decode text
             lowerCAmelCase_:							int       =	self.tokenizer.decode(
                 lowerCamelCase__       ,						skip_special_tokens=lowerCamelCase__       ,						clean_up_tokenization_spaces=lowerCamelCase__       ,						)

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

             if return_type == ReturnType.FULL_TEXT:
                lowerCAmelCase_:							Any       =	prompt_text + text[prompt_length:]
             else:
                lowerCAmelCase_:							Union[str, Any]       =	text[prompt_length:]

             lowerCAmelCase_:							List[Any]       =	{"generated_text": all_text}
          records.append(lowerCamelCase__ )

       return records
class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self ):
       lowerCAmelCase_:							dict[str, TrieNode]       =	{}  # Mapping from char to TrieNode
       lowerCAmelCase_:							str       =	False




    def      _a    (    self       ,						lowerCamelCase__ ):
       for word in words:
          self.insert(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	self
       for char in word:
          if char not in curr.nodes:
             lowerCAmelCase_:							List[Any]       =	TrieNode()
          lowerCAmelCase_:							Optional[Any]       =	curr.nodes[char]
       lowerCAmelCase_:							Dict       =	True




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	self
       for char in word:
          if char not in curr.nodes:
             return False
          lowerCAmelCase_:							Dict       =	curr.nodes[char]
       return curr.is_leaf




    def      _a    (    self       ,						lowerCamelCase__ ):

       def _delete(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) -> bool:
          if index == len(lowerCamelCase__ ):
             # If word does not exist
             if not curr.is_leaf:
                return False
             lowerCAmelCase_:							List[str]       =	False
             return len(curr.nodes ) == 0
          lowerCAmelCase_:							Union[str, Any]       =	word[index]
          lowerCAmelCase_:							Optional[int]       =	curr.nodes.get(lowerCamelCase__ )
          # If char not in current trie node
          if not char_node:
             return False
          # Flag to check if node can be deleted
          lowerCAmelCase_:							List[Any]       =	_delete(lowerCamelCase__       ,						lowerCamelCase__       ,						index + 1 )
          if delete_curr:
             del curr.nodes[char]
             return len(curr.nodes ) == 0
          return delete_curr

       _delete(self       ,						lowerCamelCase__       ,						0 )





def 		snake_case__	(		lowercase							,				lowercase					):
   if node.is_leaf:
      print(lowercase							,				end=" "					)

   for key, value in node.nodes.items():
      print_words(lowercase							,				word + key					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	"banana bananas bandana band apple all beast".split()
   lowerCAmelCase_:							Optional[Any]       =	TrieNode()
   root.insert_many(lowercase					)
   # print_words(root, "")
   assert all(root.find(lowercase					) for word in words					)
   assert root.find("banana"					)
   assert not root.find("bandanas"					)
   assert not root.find("apps"					)
   assert root.find("apple"					)
   assert root.find("all"					)
   root.delete("all"					)
   assert not root.find("all"					)
   root.delete("banana"					)
   assert not root.find("banana"					)
   assert root.find("bananas"					)
   return True
def 		snake_case__	(		lowercase							,				lowercase					):
   print(str(lowercase					)							,				"works!" if passes else "doesn't work :("					)
def 		snake_case__	(		):
   assert test_trie()
def 		snake_case__	(		):
   print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
 main()