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

__magic_name__								=logging.get_logger(__name__)



@add_end_docstrings(__UpperCAmelCase							)
class     _A   (    __UpperCAmelCase							):






		def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





							'''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 		_a					(self   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None	)		->      Optional[int]:





							'''simple docstring'''





							UpperCamelCase__										=      {}
							UpperCamelCase__										=      {}

							if prompt is not None:
												UpperCamelCase__										=      prompt

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






		def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





							'''simple docstring'''





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






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None	)		->      Optional[int]:





							'''simple docstring'''





							UpperCamelCase__										=      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__										=      self.model.config.model_type

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

												elif model_type == "pix2struct":
																	UpperCamelCase__										=      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__										=      self.image_processor(images=_lowerCamelCase   ,				return_tensors=self.framework	)
																	UpperCamelCase__										=      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__										=      self.image_processor(images=_lowerCamelCase   ,				return_tensors=self.framework	)

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

							return model_inputs






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None	)		->      Union[str, Any]:





							'''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''']	)
							):
												UpperCamelCase__										=      None

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






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





							'''simple docstring'''





							UpperCamelCase__										=      []
							for output_ids in model_outputs:
												UpperCamelCase__										=      {
												    '''generated_text''': self.tokenizer.decode(
												        _lowerCamelCase   ,				skip_special_tokens=_lowerCamelCase   ,				)
												}
												records.append(_lowerCamelCase	)
							return records




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

     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])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=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''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



