

from collections.abc import Generator
from math import sin


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			if len(a__) != 3_2:
						raise ValueError("""Input must be of length 32""")

			a_		:		str      				=      B""""""
			for i in [3, 2, 1, 0]:
						little_endian += string_aa[8 * i : 8 * i + 8]
			return little_endian


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



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

			a_		:		Tuple      				=      format(a__   ,     """08x""")[-8:]
			a_		:		Optional[int]      				=      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  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Tuple      				=      B""""""
			for char in message:
						bit_string += format(a__   ,     """08b""").encode("""utf-8""")
			a_		:		Dict      				=      format(len(a__)   ,     """064b""").encode("""utf-8""")

			# Pad bit_string to a multiple of 512 chars
			bit_string += b"1"
			while len(a__) % 5_1_2 != 4_4_8:
						bit_string += b"0"
			bit_string += to_little_endian(start_len[3_2:]) + to_little_endian(start_len[:3_2])

			return bit_string


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			if len(a__) % 5_1_2 != 0:
						raise ValueError("""Input must have length that's a multiple of 512""")

			for pos in range(0   ,     len(a__)   ,     5_1_2):
						a_		:		Any      				=      bit_string[pos : pos + 5_1_2]
						a_		:		str      				=      []
						for i in range(0   ,     5_1_2   ,     3_2):
									block_words.append(int(to_little_endian(block[i : i + 3_2])   ,     2))
						yield block_words


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



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

			a_		:		Optional[int]      				=      format(a__   ,     """032b""")
			a_		:		List[str]      				=      """"""
			for c in i_str:
						new_str += "1" if c == "0" else "0"
			return int(a__   ,     2)


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			return (a + b) % 2**3_2


def  _UpperCAmelCase							(		a__   ,     a__):

			'''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 >> (3_2 - shift))) % 2**3_2


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Any      				=      preprocess(a__)

			a_		:		Union[str, Any]      				=      [int(2**3_2 * abs(sin(i + 1))) for i in range(6_4)]

			# Starting states
			a_		:		Union[str, Any]      				=      0x67452301
			a_		:		Union[str, Any]      				=      0xefcdab89
			a_		:		List[Any]      				=      0x98badcfe
			a_		:		List[Any]      				=      0x10325476

			a_		:		int      				=      [
			    7,
			    1_2,
			    1_7,
			    2_2,
			    7,
			    1_2,
			    1_7,
			    2_2,
			    7,
			    1_2,
			    1_7,
			    2_2,
			    7,
			    1_2,
			    1_7,
			    2_2,
			    5,
			    9,
			    1_4,
			    2_0,
			    5,
			    9,
			    1_4,
			    2_0,
			    5,
			    9,
			    1_4,
			    2_0,
			    5,
			    9,
			    1_4,
			    2_0,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    4,
			    1_1,
			    1_6,
			    2_3,
			    6,
			    1_0,
			    1_5,
			    2_1,
			    6,
			    1_0,
			    1_5,
			    2_1,
			    6,
			    1_0,
			    1_5,
			    2_1,
			    6,
			    1_0,
			    1_5,
			    2_1,
			]

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

						# Hash current chunk
						for i in range(6_4):
									if i <= 1_5:
												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
												a_		:		List[str]      				=      d ^ (b & (c ^ d))
												a_		:		List[Any]      				=      i
									elif i <= 3_1:
												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
												a_		:		List[Any]      				=      c ^ (d & (b ^ c))
												a_		:		Optional[int]      				=      (5 * i + 1) % 1_6
									elif i <= 4_7:
												a_		:		Optional[Any]      				=      b ^ c ^ d
												a_		:		Dict      				=      (3 * i + 5) % 1_6
									else:
												a_		:		Tuple      				=      c ^ (b | not_aa(a__))
												a_		:		int      				=      (7 * i) % 1_6
									a_		:		int      				=      (f + a + added_consts[i] + block_words[g]) % 2**3_2
									a_		:		Optional[Any]      				=      d
									a_		:		int      				=      c
									a_		:		Union[str, Any]      				=      b
									a_		:		List[Any]      				=      sum_aa(a__   ,     left_rotate_aa(a__   ,     shift_amounts[i]))

						# Add hashed chunk to running total
						a_		:		Optional[Any]      				=      sum_aa(a__   ,     a__)
						a_		:		Union[str, Any]      				=      sum_aa(a__   ,     a__)
						a_		:		Union[str, Any]      				=      sum_aa(a__   ,     a__)
						a_		:		int      				=      sum_aa(a__   ,     a__)

			a_		:		Any      				=      reformat_hex(a__) + reformat_hex(a__) + reformat_hex(a__) + reformat_hex(a__)
			return digest


if __name__ == "__main__":
	import doctest

	doctest.testmod()







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

_lowercase: Union[str, Any]      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(*lowerCamelCase_		,       **lowerCamelCase_		)
						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      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}

						if prompt is not None:
											a					     =   prompt

						if generate_kwargs is not None:
											a					     =   generate_kwargs
						if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
																a					     =   {}
											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"		)
											a					     =   max_new_tokens
						return preprocess_params, forward_kwargs, {}




	def __call__(self		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)

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

											a					     =   self.model.config.model_type

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

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

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

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

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

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

						return model_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs["input_ids"]		,       lowerCamelCase_		)
						    and all(x is None for x in model_inputs["input_ids"]		)
						):
											a					     =   None

						if generate_kwargs is None:
											a					     =   {}
						# 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.
						a					     =   model_inputs.pop(self.model.main_input_name		)
						a					     =   self.model.generate(lowerCamelCase_		,       **lowerCamelCase_		,       **lowerCamelCase_		)
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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