'''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 	__A				(			enum.Enum   ):
    a__	:						str	       =  0
    a__	:						List[Any]	       =  1
    a__	:						str	       =  2




@add_end_docstrings(_lowerCAmelCase   )
class 	__A				(			_lowerCAmelCase   ):
    a__	:						Dict	       =  "\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   :   Optional[int]			):
         super().__init__(*_lowercase       ,      **_lowercase			)
         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=_lowercase       ,      **self._forward_params			)
                   UpperCAmelCase_	   =       {**self._preprocess_params, **preprocess_params}
                   UpperCAmelCase_	   =       {**self._forward_params, **forward_params}



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[Any]=None       ,      __a   :   List[str]=None       ,      __a   :   int=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   List[Any]=None       ,      **__a   :   str       ,      ):
         UpperCAmelCase_	   =       {}
         if prefix is not None:
              UpperCAmelCase_	   =       prefix
         if prefix:
              UpperCAmelCase_	   =       self.tokenizer(
                  _lowercase       ,      padding=_lowercase       ,      add_special_tokens=_lowercase       ,      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(_lowercase			)
         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(_lowercase       ,      add_special_tokens=_lowercase			)
              if len(_lowercase			) > 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  _lowercase						(self   :   Optional[int]       ,      *__a   :   Optional[int]       ,      **__a   :   Any			):
         # Parse arguments
         if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
              kwargs.update({"add_space_before_punct_symbol": True}			)

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



    def __call__(self   :   List[str]       ,      __a   :   str       ,      **__a   :   Optional[Any]			):
         return super().__call__(_lowercase       ,      **_lowercase			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Any       ,      __a   :   Dict=""       ,      __a   :   Union[str, Any]=None       ,      **__a   :   Tuple			):
         UpperCAmelCase_	   =       self.tokenizer(
             prefix + prompt_text       ,      padding=_lowercase       ,      add_special_tokens=_lowercase       ,      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  _lowercase						(self   :   Union[str, Any]       ,      __a   :   List[str]       ,      **__a   :   Optional[int]			):
         UpperCAmelCase_	   =       model_inputs["input_ids"]
         UpperCAmelCase_	   =       model_inputs.get("attention_mask"       ,      _lowercase			)
         # 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=_lowercase       ,      attention_mask=_lowercase       ,      **_lowercase			)
         UpperCAmelCase_	   =       generated_sequence.shape[0]
         if self.framework == "pt":
              UpperCAmelCase_	   =       generated_sequence.reshape(_lowercase       ,      out_b // in_b       ,      *generated_sequence.shape[1:]			)
         elif self.framework == "tf":
              UpperCAmelCase_	   =       tf.reshape(_lowercase       ,      (in_b, out_b // in_b, *generated_sequence.shape[1:])			)
         return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}






    def  _lowercase						(self   :   Optional[int]       ,      __a   :   Union[str, Any]       ,      __a   :   Optional[int]=ReturnType.FULL_TEXT       ,      __a   :   List[str]=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(
                       _lowercase       ,      skip_special_tokens=_lowercase       ,      clean_up_tokenization_spaces=_lowercase       ,      )

                   # 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=_lowercase       ,      clean_up_tokenization_spaces=_lowercase       ,      )			)

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

                   UpperCAmelCase_	   =       {"generated_text": all_text}
              records.append(_lowercase			)

         return records





'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  ["""pixel_values"""]



    def __init__(self   :   Any       ,      __a   :   bool = True       ,      __a   :   Optional[Dict[str, int]] = None       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   bool = True       ,      __a   :   bool = True       ,      __a   :   Union[int, float] = 1 / 255       ,      __a   :   Dict[str, int] = None       ,      __a   :   bool = True       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         UpperCAmelCase_	   =       size if size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a			)
         UpperCAmelCase_	   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a       ,      param_name="crop_size"			)

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def  _lowercase						(self   :   Tuple       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BILINEAR       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if "shortest_edge" in size:
              UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
              UpperCAmelCase_	   =       (size["height"], size["width"])
         else:
              raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""			)
         return center_crop(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   str       ,      __a   :   np.ndarray       ,      __a   :   float       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   List[str]			):
         return rescale(__a       ,      scale=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   Any       ,      __a   :   np.ndarray       ,      __a   :   Union[float, List[float]]       ,      __a   :   Union[float, List[float]]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Dict       ,      ):
         return normalize(__a       ,      mean=__a       ,      std=__a       ,      data_format=__a       ,      **__a			)






    def  _lowercase						(self   :   Dict       ,      __a   :   ImageInput       ,      __a   :   Optional[bool] = None       ,      __a   :   Dict[str, int] = None       ,      __a   :   PILImageResampling = None       ,      __a   :   bool = None       ,      __a   :   int = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[float] = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[str, TensorType]] = None       ,      __a   :   Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **__a   :   Dict       ,      ):
         UpperCAmelCase_	   =       do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_	   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_	   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_	   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_	   =       crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_	   =       get_size_dict(__a       ,      param_name="crop_size"       ,      default_to_square=__a			)
         UpperCAmelCase_	   =       resample if resample is not None else self.resample
         UpperCAmelCase_	   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_	   =       image_std if image_std is not None else self.image_std

         UpperCAmelCase_	   =       size if size is not None else self.size
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if not is_batched(__a			):
              UpperCAmelCase_	   =       [images]

         if not valid_images(__a			):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."			)

         if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."			)

         if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."			)

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."			)

         # All transformations expect numpy arrays.
         UpperCAmelCase_	   =       [to_numpy_array(__a			) for image in images]

         if do_resize:
              UpperCAmelCase_	   =       [self.resize(image=__a       ,      size=__a       ,      resample=__a			) for image in images]

         if do_center_crop:
              UpperCAmelCase_	   =       [self.center_crop(image=__a       ,      size=__a			) for image in images]

         if do_rescale:
              UpperCAmelCase_	   =       [self.rescale(image=__a       ,      scale=__a			) for image in images]

         if do_normalize:
              UpperCAmelCase_	   =       [self.normalize(image=__a       ,      mean=__a       ,      std=__a			) for image in images]

         UpperCAmelCase_	   =       [to_channel_dimension_format(__a       ,      __a			) for image in images]

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)




