





"""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

_UpperCamelCase					      =   logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     *__lowercase	:Union[str, Any]    ,     **__lowercase	:Tuple      ):
											super().__init__(*__lowercase    ,     **__lowercase      )
											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        __lowercase	(  self	:List[str]    ,     __lowercase	:int=None    ,     __lowercase	:List[Any]=None    ,     __lowercase	:List[str]=None      ):
											__lowerCamelCase   :   List[Any]       ={}
											__lowerCamelCase   :   Any       ={}

											if prompt is not None:
																		__lowerCamelCase   :   int       =prompt

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




				def __call__(  self	:int    ,     __lowercase	:Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **__lowercase	:Union[str, Any]      ):
											return super().__call__(__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:int=None      ):
											__lowerCamelCase   :   Any       =load_image(__lowercase      )

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

																		__lowerCamelCase   :   Optional[Any]       =self.model.config.model_type

																		if model_type == "git":
																									__lowerCamelCase   :   Dict       =self.image_processor(images=__lowercase    ,     return_tensors=self.framework      )
																									__lowerCamelCase   :   int       =self.tokenizer(text=__lowercase    ,     add_special_tokens=__lowercase      ).input_ids
																									__lowerCamelCase   :   Optional[int]       =[self.tokenizer.cls_token_id] + input_ids
																									__lowerCamelCase   :   Any       =torch.tensor(__lowercase      ).unsqueeze(0      )
																									model_inputs.update({'''input_ids''': input_ids}      )

																		elif model_type == "pix2struct":
																									__lowerCamelCase   :   List[str]       =self.image_processor(images=__lowercase    ,     header_text=__lowercase    ,     return_tensors=self.framework      )

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

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

											else:
																		__lowerCamelCase   :   Union[str, Any]       =self.image_processor(images=__lowercase    ,     return_tensors=self.framework      )

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

											return model_inputs




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:List[str]    ,     __lowercase	:Any=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''']    ,     __lowercase      )
											    and all(x is None for x in model_inputs['''input_ids''']      )
											):
																		__lowerCamelCase   :   Optional[Any]       =None

											if generate_kwargs is None:
																		__lowerCamelCase   :   List[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.
											__lowerCamelCase   :   Tuple       =model_inputs.pop(self.model.main_input_name      )
											__lowerCamelCase   :   List[Any]       =self.model.generate(__lowercase    ,     **__lowercase    ,     **__lowercase      )
											return model_outputs




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











"""simple docstring"""





import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
							_UpperCamelCase					      =   pd.read_csv('sample_data.csv', header=None)
							_UpperCamelCase					      =   df.shape[:1][0]
							# If you're using some other dataset input the target column
							_UpperCamelCase					      =   df.iloc[:, 1:2]
							_UpperCamelCase					      =   actual_data.values.reshape(len_data, 1)
							_UpperCamelCase					      =   MinMaxScaler().fit_transform(actual_data)
							_UpperCamelCase					      =   10
							_UpperCamelCase					      =   5
							_UpperCamelCase					      =   20
							_UpperCamelCase					      =   len_data - periods * look_back
							_UpperCamelCase					      =   actual_data[:division]
							_UpperCamelCase					      =   actual_data[division - look_back :]
							_UpperCamelCase      ,     _UpperCamelCase					      =   [], []
							_UpperCamelCase      ,     _UpperCamelCase					      =   [], []

							for i in range(0, len(train_data) - forward_days - look_back + 1):
														train_x.append(train_data[i : i + look_back])
														train_y.append(train_data[i + look_back : i + look_back + forward_days])
							for i in range(0, len(test_data) - forward_days - look_back + 1):
														test_x.append(test_data[i : i + look_back])
														test_y.append(test_data[i + look_back : i + look_back + forward_days])
							_UpperCamelCase					      =   np.array(train_x)
							_UpperCamelCase					      =   np.array(test_x)
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in train_y])
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in test_y])

							_UpperCamelCase					      =   Sequential()
							model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
							model.add(LSTM(64, input_shape=(128, 1)))
							model.add(Dense(forward_days))
							model.compile(loss='mean_squared_error', optimizer='adam')
							_UpperCamelCase					      =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							_UpperCamelCase					      =   model.predict(x_test)




