


"""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

__SCREAMING_SNAKE_CASE							:    List[Any]		=    logging.get_logger(__name__)





@add_end_docstrings(snake_case__)
class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      int		,       *UpperCAmelCase_							:      str		,       **UpperCAmelCase_							:      int       )					->Any:



														"""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 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      Dict=None		,       UpperCAmelCase_							:      Union[str, Any]=None		,       UpperCAmelCase_							:      int=None       )					->Dict:



														"""simple docstring"""



														snake_case_           =		{}
														snake_case_           =		{}

														if prompt is not None:
																					snake_case_           =		prompt

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



							def __call__(				self							:      str		,       UpperCAmelCase_							:      Union[str, List[str], "Image.Image", List["Image.Image"]]		,       **UpperCAmelCase_							:      int       )					->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Any]=None       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		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."""       )

																					snake_case_           =		self.model.config.model_type

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

																					elif model_type == "pix2struct":
																												snake_case_           =		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
																												snake_case_           =		self.image_processor(images=UpperCAmelCase_		,       return_tensors=self.framework       )
																												snake_case_           =		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:
																					snake_case_           =		self.image_processor(images=UpperCAmelCase_		,       return_tensors=self.framework       )

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

														return model_inputs



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      int=None       )					->List[str]:



														"""simple docstring"""



														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"""]       )
														):
																					snake_case_           =		None

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



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]       )					->Optional[int]:



														"""simple docstring"""



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







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							snake_case_           =		gray_code_sequence_string(_SCREAMING_SNAKE_CASE				)
							#
							# convert them to integers
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														snake_case_           =		int(sequence[i]				, 2				)

							return sequence







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							snake_case_           =		1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							snake_case_           =		gray_code_sequence_string(bit_count - 1				)

							snake_case_           =		[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														snake_case_           =		"""0""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														snake_case_           =		"""1""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



