



from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TFSharedEmbeddings,
		)

		if _tf_gpu_memory_limit is not None:
				__UpperCAmelCase    	=  tf.config.list_physical_devices('''GPU''')
				for gpu in gpus:
						# Restrict TensorFlow to only allocate x GB of memory on the GPUs
						try:
								tf.config.set_logical_device_configuration(
								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
								)
								__UpperCAmelCase    	=  tf.config.list_logical_devices('''GPU''')
								print('''Logical GPUs''', logical_gpus)
						except RuntimeError as e:
								# Virtual devices must be set before GPUs have been initialized
								print(e)


@require_tf
class 						lowerCamelCase__			:
						_lowerCAmelCase             =			None
						_lowerCAmelCase             =			()
						_lowerCAmelCase             =			()
						_lowerCAmelCase             =			True
						_lowerCAmelCase             =			True
						_lowerCAmelCase             =			True
						_lowerCAmelCase             =			False


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	Dict   ,							_a    :	int   ,							_a    :	Union[str, Any]=False      ):
													a__:							Optional[Any]									=copy.deepcopy(_a      )

													if model_class in get_values(_a      ):
																				a__:							Optional[int]									={
																				    k: tf.tile(tf.expand_dims(_a   ,							1      )   ,							(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
																				    if isinstance(_a   ,							tf.Tensor      ) and v.ndim > 0
																				    else v
																				    for k, v in inputs_dict.items()
																				}

													if return_labels:
																				if model_class in get_values(_a      ):
																											a__:							Tuple									=tf.ones(self.model_tester.batch_size   ,							dtype=tf.intaa      )
																				elif model_class in get_values(_a      ):
																											a__:							Optional[Any]									=tf.zeros(self.model_tester.batch_size   ,							dtype=tf.intaa      )
																											a__:							List[Any]									=tf.zeros(self.model_tester.batch_size   ,							dtype=tf.intaa      )
																				elif model_class in [
																				    *get_values(_a      ),
																				    *get_values(_a      ),
																				]:
																											a__:							str									=tf.zeros(self.model_tester.batch_size   ,							dtype=tf.intaa      )
																				elif model_class in get_values(_a      ):
																											a__:							Any									=tf.zeros(self.model_tester.batch_size   ,							dtype=tf.intaa      )
																				elif model_class in [
																				    *get_values(_a      ),
																				    *get_values(_a      ),
																				    *get_values(_a      ),
																				    *get_values(_a      ),
																				    *get_values(_a      ),
																				]:
																											a__:							int									=tf.zeros(
																											    (self.model_tester.batch_size, self.model_tester.seq_length)   ,							dtype=tf.intaa      )
													return inputs_dict


						@slow
						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__				,      a__:							Any									=self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																				a__:							List[Any]									=self._prepare_for_class(_a   ,							_a      )
																				a__:							Optional[Any]									=model_class(_a      )

																				@tf.function
																				def run_in_graph_mode():
																											return model(_a      )

																				a__:							str									=run_in_graph_mode()
																				self.assertIsNotNone(_a      )


						@slow
						def 	_lowerCamelCase      (			self    :	int      ):
													a__				,      a__:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																				a__:							Union[str, Any]									=self._prepare_for_class(_a   ,							_a      )
																				a__:							Optional[Any]									=model_class(_a      )

																				@tf.function(experimental_compile=_a      )
																				def run_in_graph_mode():
																											return model(_a      )

																				a__:							Optional[int]									=run_in_graph_mode()
																				self.assertIsNotNone(_a      )


						@slow
						def 	_lowerCamelCase      (			self    :	Any      ):
													# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
													a__				,      a__:							Tuple									=self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																				a__:							str									=model_class(_a      )
																				if getattr(_a   ,							"hf_compute_loss"   ,							_a      ):
																											# Test that model correctly compute the loss with kwargs
																											a__:							Tuple									=self._prepare_for_class(inputs_dict.copy()   ,							_a   ,							return_labels=_a      )
																											# Is there a better way to remove these decoder inputs?
																											a__:							List[Any]									={
																											    key: val
																											    for key, val in prepared_for_class.items()
																											    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
																											}

																											a__:							Dict									={
																											    "labels",
																											    "label",
																											    "label_ids",
																											    "start_positions",
																											    "start_position",
																											    "end_positions",
																											    "end_position",
																											    "next_sentence_label",
																											}
																											a__:							Dict									=possible_label_cols.intersection(set(_a      )      )
																											self.assertGreater(len(_a      )   ,							0   ,							msg="No matching label names found!"      )
																											a__:							List[Any]									={key: val for key, val in prepared_for_class.items() if key in label_names}
																											a__:							Tuple									={key: val for key, val in prepared_for_class.items() if key not in label_names}
																											self.assertGreater(len(_a      )   ,							0      )

																											# Make sure it works with XLA!
																											model.compile(optimizer=tf.keras.optimizers.SGD(0.0      )   ,							jit_compile=_a      )
																											# Make sure the model fits without crashing regardless of where we pass the labels
																											a__:							Optional[int]									=model.fit(
																											    _a   ,							validation_data=_a   ,							steps_per_epoch=1   ,							validation_steps=1   ,							shuffle=_a   ,							verbose=0   ,							)
																											a__:							Optional[Any]									=history.history["loss"][0]
																											self.assertTrue(not isnan(_a      )      )
																											a__:							Tuple									=history.history["val_loss"][0]
																											self.assertTrue(not isnan(_a      )      )

																											# Now test it with separate labels, to make sure that path works in XLA too.
																											a__:							Union[str, Any]									=model_class(_a      )
																											model.compile(optimizer=tf.keras.optimizers.SGD(0.0      )   ,							jit_compile=_a      )
																											a__:							Optional[Any]									=model.fit(
																											    _a   ,							_a   ,							validation_data=(inputs_minus_labels, labels)   ,							steps_per_epoch=1   ,							validation_steps=1   ,							shuffle=_a   ,							verbose=0   ,							)

																											a__:							str									=history.history["loss"][0]
																											self.assertTrue(not isnan(_a      )      )
																											a__:							int									=history.history["val_loss"][0]
																											self.assertTrue(not isnan(_a      )      )


						@slow
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__				,      a__:							List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
													a__:							Union[str, Any]									=True
													a__:							int									=True

													if hasattr(_a   ,							"use_cache"      ):
																				a__:							Any									=True

													a__:							Union[str, Any]									=getattr(self.model_tester   ,							"encoder_seq_length"   ,							self.model_tester.seq_length      )
													a__:							Optional[int]									=getattr(self.model_tester   ,							"key_length"   ,							_a      )

													for model_class in self.all_model_classes[:2]:
																				a__:							List[Any]									=self._prepare_for_class(_a   ,							_a      )
																				a__:							int									=model_class(_a      )
																				model.build()
																				a__:							str									=len(model(_a      )      )

																				for key in list(class_inputs_dict.keys()      ):
																											# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																											if key not in model.input_signature:
																																		del class_inputs_dict[key]
																											# Check it's a tensor, in case the inputs dict has some bools in it too
																											elif isinstance(class_inputs_dict[key]   ,							tf.Tensor      ) and class_inputs_dict[key].dtype.is_integer:
																																		a__:							Optional[Any]									=tf.cast(class_inputs_dict[key]   ,							tf.intaa      )

																				if set(class_inputs_dict.keys()      ) != set(model.input_signature.keys()      ):
																											continue  # Some models have inputs that the preparation functions don't create, we skip those

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(_a   ,							saved_model=_a      )
																											a__:							Any									=os.path.join(_a   ,							"saved_model"   ,							"1"      )
																											a__:							List[str]									=tf.keras.models.load_model(_a      )
																											a__:							Tuple									=model(_a      )

																											if self.is_encoder_decoder:
																																		a__:							List[str]									=outputs["encoder_hidden_states"]
																																		a__:							List[str]									=outputs["encoder_attentions"]
																											else:
																																		a__:							int									=outputs["hidden_states"]
																																		a__:							Dict									=outputs["attentions"]

																											self.assertEqual(len(_a      )   ,							_a      )

																											a__:							Optional[int]									=getattr(
																											    self.model_tester   ,							"expected_num_hidden_layers"   ,							self.model_tester.num_hidden_layers + 1      )

																											self.assertEqual(len(_a      )   ,							_a      )
																											self.assertListEqual(
																											    list(output_hidden_states[0].shape[-2:]      )   ,							[self.model_tester.seq_length, self.model_tester.hidden_size]   ,							)

																											self.assertEqual(len(_a      )   ,							self.model_tester.num_hidden_layers      )
																											self.assertListEqual(
																											    list(output_attentions[0].shape[-3:]      )   ,							[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]   ,							)


						@slow
						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													tf.keras.mixed_precision.set_global_policy("mixed_float16"      )

													# try/finally block to ensure subsequent tests run in float32
													try:
																				a__				,      a__:							List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
																				for model_class in self.all_model_classes[:2]:
																											a__:							Union[str, Any]									=self._prepare_for_class(_a   ,							_a      )
																											a__:							Dict									=model_class(_a      )
																											a__:							Dict									=model(_a      )

																											self.assertIsNotNone(_a      )
													finally:
																				tf.keras.mixed_precision.set_global_policy("float32"      )


						@slow
						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__				,      a__:							List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
													# head_mask and decoder_head_mask has different shapes than other input args
													if "head_mask" in inputs_dict:
																				del inputs_dict["head_mask"]
													if "decoder_head_mask" in inputs_dict:
																				del inputs_dict["decoder_head_mask"]
													if "cross_attn_head_mask" in inputs_dict:
																				del inputs_dict["cross_attn_head_mask"]
													a__:							Dict									={
													    module_member
													    for model_class in self.all_model_classes
													    for module in (import_module(model_class.__module__      ),)
													    for module_member_name in dir(_a      )
													    if module_member_name.endswith("MainLayer"      )
													    for module_member in (getattr(_a   ,							_a      ),)
													    if isinstance(_a   ,							_a      )
													    and tf.keras.layers.Layer in module_member.__bases__
													    and getattr(_a   ,							"_keras_serializable"   ,							_a      )
													}

													for main_layer_class in tf_main_layer_classes:
																				# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
																				if "T5" in main_layer_class.__name__:
																											# Take the same values than in TFT5ModelTester for this shared layer
																											a__:							Tuple									=TFSharedEmbeddings(self.model_tester.vocab_size   ,							self.model_tester.hidden_size   ,							name="shared"      )
																											a__:							List[Any]									=False
																											a__:							str									=main_layer_class(_a   ,							embed_tokens=_a      )
																				else:
																											a__:							List[str]									=main_layer_class(_a      )

																				a__:							List[Any]									={
																				    name: tf.keras.Input(tensor.shape[1:]   ,							dtype=tensor.dtype      ) for name, tensor in inputs_dict.items()
																				}

																				if hasattr(self.model_tester   ,							"num_labels"      ):
																											a__:							Any									=self.model_tester.num_labels
																				else:
																											a__:							Optional[int]									=2

																				a__:							Union[str, Any]									=tf.data.Dataset.from_tensor_slices(
																				    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)      ))      ).batch(1      )

																				a__:							int									=main_layer(_a      )[0]
																				a__:							int									=tf.keras.layers.Dense(_a   ,							activation="softmax"   ,							name="outputs"      )(_a      )
																				a__:							Optional[int]									=tf.keras.models.Model(inputs=_a   ,							outputs=[outputs]      )

																				model.compile(loss="binary_crossentropy"   ,							optimizer="adam"   ,							metrics=["binary_accuracy"]      )
																				model.fit(_a   ,							epochs=1      )

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											a__:							Optional[Any]									=os.path.join(_a   ,							"keras_model.h5"      )
																											model.save(_a      )
																											if "T5" in main_layer_class.__name__:
																																		a__:							List[str]									=tf.keras.models.load_model(
																																		    _a   ,							custom_objects={
																																		        main_layer_class.__name__: main_layer_class,
																																		        "TFSharedEmbeddings": TFSharedEmbeddings,
																																		    }   ,							)
																											else:
																																		a__:							List[str]									=tf.keras.models.load_model(
																																		    _a   ,							custom_objects={main_layer_class.__name__: main_layer_class}      )
																											assert isinstance(_a   ,							tf.keras.Model      )
																											model(_a      )


						@slow
						def 	_lowerCamelCase      (			self    :	Any      ):
													a__				,      a__:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes[:2]:
																				a__:							List[Any]									=model_class(_a      )

																				a__:							List[str]									=copy.deepcopy(_a      )

																				if not self.is_encoder_decoder:
																											a__:							Optional[Any]									=inputs["input_ids"]
																											del inputs["input_ids"]
																				else:
																											a__:							str									=inputs["input_ids"]
																											a__:							str									=inputs.get("decoder_input_ids"   ,							_a      )
																											del inputs["input_ids"]
																											inputs.pop("decoder_input_ids"   ,							_a      )

																				if not self.is_encoder_decoder:
																											a__:							Dict									=model.get_input_embeddings()(_a      )
																				else:
																											a__:							int									=model.get_input_embeddings()(_a      )
																											a__:							List[str]									=model.get_input_embeddings()(_a      )

																				a__:							Optional[Any]									=self._prepare_for_class(_a   ,							_a      )

																				@tf.function
																				def run_in_graph_mode():
																											return model(_a      )

																				a__:							Tuple									=run_in_graph_mode()
																				self.assertIsNotNone(_a      )


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	str   ,							_a    :	Any      ):
													# special tokens cannot be bad tokens
													a__:							List[str]									=[]
													if model.config.bos_token_id is not None:
																				special_tokens.append(model.config.bos_token_id      )
													if model.config.pad_token_id is not None:
																				special_tokens.append(model.config.pad_token_id      )
													if model.config.eos_token_id is not None:
																				special_tokens.append(model.config.eos_token_id      )

													# create random bad tokens that are not special tokens
													a__:							Tuple									=[]
													while len(_a      ) < num_bad_tokens:
																				a__:							List[Any]									=tf.squeeze(ids_tensor((1, 1)   ,							self.model_tester.vocab_size      )   ,							0      ).numpy()[0]
																				if token not in special_tokens:
																											bad_tokens.append(_a      )
													return bad_tokens


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	str      ):
													for token_id in output_ids[0].numpy().tolist():
																				self.assertGreaterEqual(_a   ,							0      )
																				self.assertLess(_a   ,							self.model_tester.vocab_size      )







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													# for all bad word tokens
													for bad_word_ids in bad_words_ids:
																				# for all slices in batch
																				for generated_ids_slice in generated_ids:
																											# for all word idx
																											for i in range(len(_a      )   ,							len(_a      )      ):
																																		# if tokens match
																																		if generated_ids_slice[i - len(_a      ) : i] == bad_word_ids:
																																									return True
													return False










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





