


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 			A_   (			enum.Enum       ):




      '''simple docstring'''


      __snake_case   			=  0
      __snake_case   			=  1
      __snake_case   			=  2



@add_end_docstrings(UpperCAmelCase__       )
class 			A_   (			UpperCAmelCase__       ):




      '''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:			Optional[Any]		, *a:			Any		, **a:			List[str]							):
       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						:				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 	_snake_case      (							self:			Any		, a:			Tuple=None		, a:			Tuple=None		, a:			Union[str, Any]=None		, a:			List[Any]=None		, a:			Dict=None		, a:			int=None		, a:			str=None		, a:			List[str]=None		, **a:			int		, ):
       __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 	_snake_case      (							self:			Tuple		, *a:			Any		, **a:			Dict							):
       # 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:			Dict		, a:			Dict		, **a:			Optional[int]							):
       return super().__call__(lowerCamelCase__		, **lowerCamelCase__							)




      def 	_snake_case      (							self:			int		, a:			List[Any]		, a:			Optional[int]=""		, a:			Any=None		, **a:			Optional[Any]							):
       __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 	_snake_case      (							self:			Any		, a:			str		, **a:			Dict							):
       __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 	_snake_case      (							self:			List[Any]		, a:			Tuple		, a:			Any=ReturnType.FULL_TEXT		, a:			Optional[Any]=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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a  :      List[str]            =    {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    ["""EfficientFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[str]            =    [
     """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """EfficientFormerForImageClassification""",
     """EfficientFormerForImageClassificationWithTeacher""",
     """EfficientFormerModel""",
     """EfficientFormerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[Any]            =    [
     """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFEfficientFormerForImageClassification""",
     """TFEfficientFormerForImageClassificationWithTeacher""",
     """TFEfficientFormerModel""",
     """TFEfficientFormerPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientformer import EfficientFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
      EfficientFormerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
      TFEfficientFormerPreTrainedModel,
  )

else:
 import sys

 a  :      Tuple            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)