


'''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 					lowercase_     (    enum.Enum   ):
    __UpperCAmelCase   			=	0
    __UpperCAmelCase   			=	1
    __UpperCAmelCase   			=	2

@add_end_docstrings(a__   )
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'\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  ,					*a  ,					**a    ):
          super().__init__(*a  ,					**a    )
          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.
                UpperCamelCase__										=				None
                if self.model.config.prefix is not None:
                      UpperCamelCase__										=				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.
                      UpperCamelCase__										=				self.XL_PREFIX
                if prefix is not None:
                      # Recalculate some generate_kwargs linked to prefix.
                      UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				self._sanitize_parameters(prefix=a  ,					**self._forward_params    )
                      UpperCamelCase__										=				{**self._preprocess_params, **preprocess_params}
                      UpperCamelCase__										=				{**self._forward_params, **forward_params}


    def    __a							(     self  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					a=None  ,					**a  ,					):
          UpperCamelCase__										=				{}
          if prefix is not None:
                UpperCamelCase__										=				prefix
          if prefix:
                UpperCamelCase__										=				self.tokenizer(
                    a  ,					padding=a  ,					add_special_tokens=a  ,					return_tensors=self.framework    )
                UpperCamelCase__										=				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']"    )
                UpperCamelCase__										=				handle_long_generation

          preprocess_params.update(a    )
          UpperCamelCase__										=				generate_kwargs

          UpperCamelCase__										=				{}
          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`"    )
                UpperCamelCase__										=				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`"    )
                UpperCamelCase__										=				ReturnType.TENSORS
          if return_type is not None:
                UpperCamelCase__										=				return_type
          if clean_up_tokenization_spaces is not None:
                UpperCamelCase__										=				clean_up_tokenization_spaces

          if stop_sequence is not None:
                UpperCamelCase__										=				self.tokenizer.encode(a  ,					add_special_tokens=a    )
                if len(a    ) > 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."    )
                UpperCamelCase__										=				stop_sequence_ids[0]

          return preprocess_params, forward_params, postprocess_params


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

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


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


    def    __a							(     self  ,					a  ,					a=""  ,					a=None  ,					**a    ):
          UpperCamelCase__										=				self.tokenizer(
              prefix + prompt_text  ,					padding=a  ,					add_special_tokens=a  ,					return_tensors=self.framework    )
          UpperCamelCase__										=				prompt_text

          if handle_long_generation == "hole":
                UpperCamelCase__										=				inputs["input_ids"].shape[-1]
                if "max_new_tokens" in generate_kwargs:
                      UpperCamelCase__										=				generate_kwargs["max_new_tokens"]
                else:
                      UpperCamelCase__										=				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:
                      UpperCamelCase__										=				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"    )

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

          return inputs


    def    __a							(     self  ,					a  ,					**a    ):
          UpperCamelCase__										=				model_inputs["input_ids"]
          UpperCamelCase__										=				model_inputs.get("attention_mask"  ,					a    )
          # Allow empty prompts
          if input_ids.shape[1] == 0:
                UpperCamelCase__										=				None
                UpperCamelCase__										=				None
                UpperCamelCase__										=				1
          else:
                UpperCamelCase__										=				input_ids.shape[0]
          UpperCamelCase__										=				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.
          UpperCamelCase__										=				generate_kwargs.pop("prefix_length"  ,					0    )
          if prefix_length > 0:
                UpperCamelCase__										=				"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:
                      UpperCamelCase__										=				generate_kwargs.get("max_length"    ) or self.model.config.max_length
                      generate_kwargs["max_length"] += prefix_length
                UpperCamelCase__										=				"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
          UpperCamelCase__										=				self.model.generate(input_ids=a  ,					attention_mask=a  ,					**a    )
          UpperCamelCase__										=				generated_sequence.shape[0]
          if self.framework == "pt":
                UpperCamelCase__										=				generated_sequence.reshape(a  ,					out_b // in_b  ,					*generated_sequence.shape[1:]    )
          elif self.framework == "tf":
                UpperCamelCase__										=				tf.reshape(a  ,					(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  ,					a  ,					a=ReturnType.FULL_TEXT  ,					a=True    ):
          UpperCamelCase__										=				model_outputs["generated_sequence"][0]
          UpperCamelCase__										=				model_outputs["input_ids"]
          UpperCamelCase__										=				model_outputs["prompt_text"]
          UpperCamelCase__										=				generated_sequence.numpy().tolist()
          UpperCamelCase__										=				[]
          for sequence in generated_sequence:
                if return_type == ReturnType.TENSORS:
                      UpperCamelCase__										=				{"generated_token_ids": sequence}
                elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                      # Decode text
                      UpperCamelCase__										=				self.tokenizer.decode(
                          a  ,					skip_special_tokens=a  ,					clean_up_tokenization_spaces=a  ,					)

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

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

                      UpperCamelCase__										=				{"generated_text": all_text}
                records.append(a    )

          return records









'''simple docstring'''
from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__snake_case							      =     {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__snake_case							      =     {
    '''facebook/blenderbot_small-90M''': 512,
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				VOCAB_FILES_NAMES
		_a     =				PRETRAINED_VOCAB_FILES_MAP
		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     =				BlenderbotSmallTokenizer




		def __init__(		self    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_="<|endoftext|>"    ,   UpperCamelCase_=False    ,   UpperCamelCase_=True    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=UpperCamelCase_    ,   merges=UpperCamelCase_    ,   add_prefix_space=UpperCamelCase_    ,   trim_offsets=UpperCamelCase_    ,   )    ,   bos_token=UpperCamelCase_    ,   eos_token=UpperCamelCase_    ,   unk_token=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Union[str, Any]												= add_prefix_space




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= [self.sep_token_id]
							UpperCamelCase__   :Any												= [self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]