



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

_lowerCamelCase						    =  logging.get_logger(__name__)

@add_end_docstrings(A_ )
class       _SCREAMING_SNAKE_CASE	(A_ ):





     def __init__(       self :							Dict     ,  *UpperCamelCase :							Optional[Any]     ,  **UpperCamelCase :							List[Any]				)->Optional[int]:
      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 :							Any     ,  UpperCamelCase :							Union[str, Any]=None     ,  UpperCamelCase :							Dict=None     ,  UpperCamelCase :							Dict=None				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{}
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{}

      if prompt is not None:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	prompt

      if generate_kwargs is not None:
       __SCREAMING_SNAKE_CASE       :    int       =	generate_kwargs
      if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
        __SCREAMING_SNAKE_CASE       :    str       =	{}
       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"				)
       __SCREAMING_SNAKE_CASE       :    List[str]       =	max_new_tokens
      return preprocess_params, forward_kwargs, {}





     def __call__(       self :							int     ,  UpperCamelCase :							List[Any]     ,  **UpperCamelCase :							Optional[int]				)->Union[str, Any]:
      return super().__call__(UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Tuple=None				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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."				)

       __SCREAMING_SNAKE_CASE       :    int       =	self.model.config.model_type

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

       elif model_type == "pix2struct":
        __SCREAMING_SNAKE_CASE       :    Any       =	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
        __SCREAMING_SNAKE_CASE       :    str       =	self.image_processor(images=UpperCamelCase     ,  return_tensors=self.framework				)
        __SCREAMING_SNAKE_CASE       :    Dict       =	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:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processor(images=UpperCamelCase     ,  return_tensors=self.framework				)

      if self.model.config.model_type == "git" and prompt is None:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None

      return model_inputs





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[Any]=None				)->List[Any]:
      # 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"]				)
      ):
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None

      if generate_kwargs is None:
       __SCREAMING_SNAKE_CASE       :    int       =	{}
      # 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.
      __SCREAMING_SNAKE_CASE       :    List[str]       =	model_inputs.pop(self.model.main_input_name				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.model.generate(UpperCamelCase     ,  **UpperCamelCase     ,  **UpperCamelCase				)
      return model_outputs






     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Optional[int]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    str       =	[]
      for output_ids in model_outputs:
       __SCREAMING_SNAKE_CASE       :    int       =	{
           """generated_text""": self.tokenizer.decode(
               UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  )
       }
       records.append(UpperCamelCase				)
      return records











from collections.abc import Generator
from math import sin






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 if len(__lowerCamelCase     ) != 32:
  raise ValueError("Input must be of length 32"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian += string_aa[8 * i : 8 * i + 8]
 return little_endian






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )

 __SCREAMING_SNAKE_CASE       :    Any       =	format(__lowerCamelCase		,	"08x"     )[-8:]
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"     )
 return little_endian_hex






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	b""
 for char in message:
  bit_string += format(__lowerCamelCase		,	"08b"     ).encode("utf-8"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	format(len(__lowerCamelCase     )		,	"064b"     ).encode("utf-8"     )

 # Pad bit_string to a multiple of 512 chars
 bit_string += b"1"
 while len(__lowerCamelCase     ) % 512 != 448:
  bit_string += b"0"
 bit_string += to_little_endian(start_len[32:]     ) + to_little_endian(start_len[:32]     )

 return bit_string






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 if len(__lowerCamelCase     ) % 512 != 0:
  raise ValueError("Input must have length that's a multiple of 512"     )

 for pos in range(0		,	len(__lowerCamelCase     )		,	512     ):
  __SCREAMING_SNAKE_CASE       :    int       =	bit_string[pos : pos + 512]
  __SCREAMING_SNAKE_CASE       :    Tuple       =	[]
  for i in range(0		,	512		,	32     ):
   block_words.append(int(to_little_endian(block[i : i + 32]     )		,	2     )     )
  yield block_words






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	format(__lowerCamelCase		,	"032b"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	""
 for c in i_str:
  new_str += "1" if c == "0" else "0"
 return int(__lowerCamelCase		,	2     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 return (a + b) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )
 if shift < 0:
  raise ValueError("Shift must be non-negative"     )
 return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	preprocess(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[int(2**32 * abs(sin(i + 1     )     )     ) for i in range(64     )]

 # Starting states
 __SCREAMING_SNAKE_CASE       :    Tuple       =	0X67452301
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0Xefcdab89
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0X98badcfe
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0X10325476

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
 ]

 # Process bit string in chunks, each with 16 32-char words
 for block_words in get_block_words(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    Any       =	aa
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ba
  __SCREAMING_SNAKE_CASE       :    str       =	ca
  __SCREAMING_SNAKE_CASE       :    Dict       =	da

  # Hash current chunk
  for i in range(64     ):
   if i <= 15:
    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	d ^ (b & (c ^ d))
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	i
   elif i <= 31:
    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
    __SCREAMING_SNAKE_CASE       :    int       =	c ^ (d & (b ^ c))
    __SCREAMING_SNAKE_CASE       :    int       =	(5 * i + 1) % 16
   elif i <= 47:
    __SCREAMING_SNAKE_CASE       :    List[str]       =	b ^ c ^ d
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(3 * i + 5) % 16
   else:
    __SCREAMING_SNAKE_CASE       :    Any       =	c ^ (b | not_aa(__lowerCamelCase     ))
    __SCREAMING_SNAKE_CASE       :    str       =	(7 * i) % 16
   __SCREAMING_SNAKE_CASE       :    List[str]       =	(f + a + added_consts[i] + block_words[g]) % 2**32
   __SCREAMING_SNAKE_CASE       :    Dict       =	d
   __SCREAMING_SNAKE_CASE       :    str       =	c
   __SCREAMING_SNAKE_CASE       :    Tuple       =	b
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum_aa(__lowerCamelCase		,	left_rotate_aa(__lowerCamelCase		,	shift_amounts[i]     )     )

  # Add hashed chunk to running total
  __SCREAMING_SNAKE_CASE       :    Dict       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    str       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     )
 return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






