'''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=  logging.get_logger(__name__)






@add_end_docstrings(_lowerCamelCase						)
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None    ):
         __UpperCamelCase	:	int		    =  {}
         __UpperCamelCase	:	Optional[int]		    =  {}

         if prompt is not None:
             __UpperCamelCase	:	str		    =  prompt

         if generate_kwargs is not None:
             __UpperCamelCase	:	Any		    =  generate_kwargs
         if max_new_tokens is not None:
             if "generate_kwargs" not in forward_kwargs:
                 __UpperCamelCase	:	Optional[int]		    =  {}
             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'    )
             __UpperCamelCase	:	Optional[Any]		    =  max_new_tokens
         return preprocess_params, forward_kwargs, {}







     def __call__(		self     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):
         return super().__call__(_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         __UpperCamelCase	:	Optional[Any]		    =  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.'    )

             __UpperCamelCase	:	Any		    =  self.model.config.model_type

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

             elif model_type == "pix2struct":
                 __UpperCamelCase	:	Optional[int]		    =  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
                 __UpperCamelCase	:	Dict		    =  self.image_processor(images=_lowerCamelCase     ,			return_tensors=self.framework    )
                 __UpperCamelCase	:	int		    =  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:
             __UpperCamelCase	:	str		    =  self.image_processor(images=_lowerCamelCase     ,			return_tensors=self.framework    )

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

         return model_inputs







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         # 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']     ,			_lowerCamelCase    )
             and all(x is None for x in model_inputs['input_ids']    )
         ):
             __UpperCamelCase	:	Dict		    =  None

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  []
         for output_ids in model_outputs:
             __UpperCamelCase	:	List[Any]		    =  {
                 'generated_text': self.tokenizer.decode(
                     _lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase     ,			)
             }
             records.append(_lowerCamelCase    )
         return records




'''simple docstring'''
from timeit import timeit

a=  {
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	str		    =  len(_a			) - 1
    while start_i < end_i:
        if s[start_i] == s[end_i]:
            start_i += 1
            end_i -= 1
        else:
            return False
    return True







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  len(_a			) // 2
    __UpperCamelCase	:	Tuple		    =  len(_a			)

    # We need to traverse till half of the length of string
    # as we can get access of the i'th last element from
    # i'th index.
    # eg: [0,1,2,3,4,5] => 4th index can be accessed
    # with the help of 1st index (i==n-i-1)
    # where n is length of string
    return all(s[i] == s[n - i - 1] for i in range(_a			)			)







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    if len(_a			) <= 2:
        return True
    if s[0] == s[len(_a			) - 1]:
        return is_palindrome_recursive(s[1:-1]			)
    else:
        return False







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    return s == s[::-1]







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  f"""all({name}(key) is value for key, value in test_data.items())"""
    __UpperCamelCase	:	Union[str, Any]		    =  f"""from __main__ import test_data, {name}"""
    __UpperCamelCase	:	Any		    =  5_0_0_0_0_0
    __UpperCamelCase	:	Tuple		    =  timeit(stmt=_a				,     setup=_a				,     number=_a			)
    print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""			)


if __name__ == "__main__":
  for key, value in test_data.items():
    assert is_palindrome(key) is is_palindrome_recursive(key)
    assert is_palindrome(key) is is_palindrome_slice(key)
    print(F"""{key:21} {value}""")
  print('''a man a plan a canal panama''')

  # finished 500,000 runs in 0.46793 seconds
  benchmark_function('''is_palindrome_slice''')
  # finished 500,000 runs in 0.85234 seconds
  benchmark_function('''is_palindrome''')
  # finished 500,000 runs in 1.32028 seconds
  benchmark_function('''is_palindrome_recursive''')
  # finished 500,000 runs in 2.08679 seconds
  benchmark_function('''is_palindrome_traversal''')



