


'''simple docstring'''

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

a_						:       Tuple      			=				logging.get_logger(__name__)





@add_end_docstrings(lowercase    )
class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(*UpperCamelCase    ,						**UpperCamelCase  )
   requires_backends(self    ,						"vision"  )
   self.check_model_type(
       TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






  def 						snake_case	(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_  				=						{}
   lowerCamelCase_  				=						{}

   if prompt is not None:
    lowerCamelCase_  				=						prompt

   if generate_kwargs is not None:
    lowerCamelCase_  				=						generate_kwargs
   if max_new_tokens is not None:
    if "generate_kwargs" not in forward_kwargs:
     lowerCamelCase_  				=						{}
    if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
     raise ValueError(
         "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
         " please use only one"  )
    lowerCamelCase_  				=						max_new_tokens
   return preprocess_params, forward_kwargs, {}






  def __call__(					self    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return super().__call__(UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_  				=						load_image(UpperCamelCase  )

   if prompt is not None:
    if not isinstance(UpperCamelCase    ,						UpperCamelCase  ):
     raise ValueError(
         f'''Received an invalid text input, got - {type(UpperCamelCase  )} - but expected a single string. '''
         "Note also that one single text can be provided for conditional image to text generation."  )

    lowerCamelCase_  				=						self.model.config.model_type

    if model_type == "git":
     lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						return_tensors=self.framework  )
     lowerCamelCase_  				=						self.tokenizer(text=UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ).input_ids
     lowerCamelCase_  				=						[self.tokenizer.cls_token_id] + input_ids
     lowerCamelCase_  				=						torch.tensor(UpperCamelCase  ).unsqueeze(0  )
     model_inputs.update({"input_ids": input_ids}  )

    elif model_type == "pix2struct":
     lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						header_text=UpperCamelCase    ,						return_tensors=self.framework  )

    elif model_type != "vision-encoder-decoder":
     # vision-encoder-decoder does not support conditional generation
     lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						return_tensors=self.framework  )
     lowerCamelCase_  				=						self.tokenizer(UpperCamelCase    ,						return_tensors=self.framework  )
     model_inputs.update(UpperCamelCase  )

    else:
     raise ValueError(f'''Model type {model_type} does not support conditional text generation'''  )

   else:
    lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						return_tensors=self.framework  )

   if self.model.config.model_type == "git" and prompt is None:
    lowerCamelCase_  				=						None

   return model_inputs






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
   # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
   if (
       "input_ids" in model_inputs
       and isinstance(model_inputs["input_ids"]    ,						UpperCamelCase  )
       and all(x is None for x in model_inputs["input_ids"]  )
   ):
    lowerCamelCase_  				=						None

   if generate_kwargs is None:
    lowerCamelCase_  				=						{}
   # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
   #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
   #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
   #  in the `_prepare_model_inputs` method.
   lowerCamelCase_  				=						model_inputs.pop(self.model.main_input_name  )
   lowerCamelCase_  				=						self.model.generate(UpperCamelCase    ,						**UpperCamelCase    ,						**UpperCamelCase  )
   return model_outputs







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for output_ids in model_outputs:
    lowerCamelCase_  				=						{
        "generated_text": self.tokenizer.decode(
            UpperCamelCase    ,						skip_special_tokens=UpperCamelCase    ,						)
    }
    records.append(UpperCamelCase  )
   return records







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


a_						:       Dict      			=				logging.get_logger(__name__)

a_						:       List[Any]      			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_						:       Optional[Any]      			=				{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

a_						:       str      			=				{
    """gpt-neox-20b""": 2048,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]






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







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

   lowerCamelCase_  				=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
   if pre_tok_state.get("add_prefix_space"    ,						UpperCamelCase  ) != add_prefix_space:
    lowerCamelCase_  				=						getattr(UpperCamelCase    ,						pre_tok_state.pop("type"  )  )
    lowerCamelCase_  				=						add_prefix_space
    lowerCamelCase_  				=						pre_tok_class(**UpperCamelCase  )

   lowerCamelCase_  				=						add_prefix_space






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ) + [self.eos_token_id]  )

   if len(UpperCamelCase  ) > self.model_max_length:
    lowerCamelCase_  				=						input_ids[-self.model_max_length :]
   return input_ids



