




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

_snake_case										=       logging.get_logger(__name__)







@add_end_docstrings(lowerCAmelCase__		)
class 						lowercase							(   lowerCAmelCase__		):

     def __init__(  self		,     *_a		,     **_a				)						->							List[str]:
           super().__init__(*__a		,     **__a				)
           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 					a__	(  self		,     _a=None		,     _a=None		,     _a=None				)						->							List[str]:
           _A							:     int			=						{}
           _A							:     Optional[Any]			=						{}

           if prompt is not None:
                 _A							:     Tuple			=						prompt

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

     def __call__(  self		,     _a		,     **_a				)						->							Dict:
           return super().__call__(__a		,     **__a				)

     def 					a__	(  self		,     _a		,     _a=None				)						->							Tuple:
           _A							:     List[str]			=						load_image(__a				)

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

                 _A							:     List[str]			=						self.model.config.model_type

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

                 elif model_type == "pix2struct":
                       _A							:     int			=						self.image_processor(images=__a		,     header_text=__a		,     return_tensors=self.framework				)

                 elif model_type != "vision-encoder-decoder":
                       # vision-encoder-decoder does not support conditional generation
                       _A							:     Optional[Any]			=						self.image_processor(images=__a		,     return_tensors=self.framework				)
                       _A							:     Union[str, Any]			=						self.tokenizer(__a		,     return_tensors=self.framework				)
                       model_inputs.update(__a				)

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

           else:
                 _A							:     int			=						self.image_processor(images=__a		,     return_tensors=self.framework				)

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

           return model_inputs

     def 					a__	(  self		,     _a		,     _a=None				)						->							int:
           # 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"""]		,     __a				)
               and all(x is None for x in model_inputs["""input_ids"""]				)
           ):
                 _A							:     Any			=						None

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



     def 					a__	(  self		,     _a				)						->							List[Any]:
           _A							:     Optional[int]			=						[]
           for output_ids in model_outputs:
                 _A							:     Dict			=						{
                     """generated_text""": self.tokenizer.decode(
                         __a		,     skip_special_tokens=__a		,     )
                 }
                 records.append(__a				)
           return records





"""simple docstring"""


import numpy

# List of input, output pairs
A     :   Any			    =			(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A     :   str			    =			(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A     :   Union[str, Any]			    =			[2, 4, 1, 5]
A     :   int			    =			len(train_data)
A     :   Dict			    =			0.009


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase="train"      ):

			'''simple docstring'''




			return calculate_hypothesis_value(_UpperCamelCase    , _UpperCamelCase      ) - output(
			    _UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for i in range(len(_UpperCamelCase      ) - 1      ):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if data_set == "train":
						return train_data[example_no][1]
			elif data_set == "test":
						return test_data[example_no][1]
			return None


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if data_set == "train":
						return _hypothesis_value(train_data[example_no][0]      )
			elif data_set == "test":
						return _hypothesis_value(test_data[example_no][0]      )
			return None


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=m      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for i in range(_UpperCamelCase      ):
						if index == -1:
									summation_value += _error(_UpperCamelCase      )
						else:
									summation_value += _error(_UpperCamelCase      ) * train_data[i][0][index]
			return summation_value


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			summation_of_cost_derivative(_UpperCamelCase    , _UpperCamelCase      ) / m
			return cost_derivative_value


def _lowerCamelCase	(		):

			'''simple docstring'''




			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			__lowerCAmelCase				    =			0.00_00_02
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			while True:
						j += 1
						__lowerCAmelCase				    =			[0, 0, 0, 0]
						for i in range(0    , len(_UpperCamelCase      )      ):
									__lowerCAmelCase				    =			get_cost_derivative(i - 1      )
									__lowerCAmelCase				    =			(
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    _UpperCamelCase    , _UpperCamelCase    , atol=_UpperCamelCase    , rtol=_UpperCamelCase    , ):
									break
						__lowerCAmelCase				    =			temp_parameter_vector
			print(("Number of iterations:", j)      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			for i in range(len(_UpperCamelCase      )      ):
						print(("Actual output value:", output(_UpperCamelCase    , "test"      ))      )
						print(("Hypothesis output:", calculate_hypothesis_value(_UpperCamelCase    , "test"      ))      )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()




