


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFResNetForImageClassification, TFResNetModel
		from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=10      ,   _SCREAMING_SNAKE_CASE=[10, 20, 30, 40]      ,   _SCREAMING_SNAKE_CASE=[1, 1, 2, 1]      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE="relu"      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      image_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      embeddings_size
									_UpperCAmelCase					=      hidden_sizes
									_UpperCAmelCase					=      depths
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      hidden_act
									_UpperCAmelCase					=      num_labels
									_UpperCAmelCase					=      scope
									_UpperCAmelCase					=      len(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									_UpperCAmelCase					=      None
									if self.use_labels:
																_UpperCAmelCase					=      ids_tensor([self.batch_size]      ,   self.num_labels						)

									_UpperCAmelCase					=      self.get_config()

									return config, pixel_values, labels
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return ResNetConfig(
									    num_channels=self.num_channels      ,   embeddings_size=self.embeddings_size      ,   hidden_sizes=self.hidden_sizes      ,   depths=self.depths      ,   hidden_act=self.hidden_act      ,   num_labels=self.num_labels      ,   image_size=self.image_size      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFResNetModel(config=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape      ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.num_labels
									_UpperCAmelCase					=      TFResNetForImageClassification(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   labels=_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)						)






		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.prepare_config_and_inputs()
									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      config_and_inputs
									_UpperCAmelCase					=      {'pixel_values': pixel_values}
									return config, inputs_dict



@require_tf
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Optional[int]           =     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		_a     :  Optional[int]           =     (
		    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		_a     :  Optional[int]           =     False
		_a     :  Optional[Any]           =     False
		_a     :  int           =     False
		_a     :  Union[str, Any]           =     False
		_a     :  str           =     False
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFResNetModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE      ,   has_text_modality=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									return
		@unittest.skip(reason='ResNet does not use inputs_embeds'						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='ResNet does not support input and output embeddings'						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      inspect.signature(model.call						)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase					=      [*signature.parameters.keys()]

																_UpperCAmelCase					=      ['pixel_values']
																self.assertListEqual(arg_names[:1]      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)

																_UpperCAmelCase					=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																_UpperCAmelCase					=      self.model_tester.num_stages
																self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   expected_num_stages + 1						)

																# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]						)      ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,   )

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase					=      ['basic', 'bottleneck']
									for model_class in self.all_model_classes:
																for layer_type in layers_type:
																							_UpperCAmelCase					=      layer_type
																							_UpperCAmelCase					=      True
																							check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																							# check that output_hidden_states also work using config
																							del inputs_dict["output_hidden_states"]
																							_UpperCAmelCase					=      True

																							check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



def 	lowerCAmelCase__			(      )  ->		str:


							'''simple docstring'''





							_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
							return image







@require_tf
@require_vision
class 		__a				(    unittest.TestCase      ):
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									return (
									    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
									    if is_vision_available()
									    else None
									)






		@slow
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

									_UpperCAmelCase					=      self.default_image_processor
									_UpperCAmelCase					=      prepare_img()
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='tf'						)

									# forward pass
									_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)

									# verify the logits
									_UpperCAmelCase					=      tf.TensorShape((1, 1000)						)
									self.assertEqual(outputs.logits.shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      tf.constant([-11.1069, -9.7877, -8.3777]						)

									self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)




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__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :str             =							[], []

		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])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
