
'''simple docstring'''



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:
								__A       =      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)]
																)
																__A       =      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      A							:
			lowerCamelCase							:							Dict    						=					None
			lowerCamelCase							:							Optional[Any]    						=					()
			lowerCamelCase							:							List[str]    						=					()
			lowerCamelCase							:							Optional[Any]    						=					True
			lowerCamelCase							:							Dict    						=					True
			lowerCamelCase							:							int    						=					True
			lowerCamelCase							:							Optional[Any]    						=					False



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__=False    )     ->						dict:




									'''simple docstring'''



									lowercase__								=    copy.deepcopy(lowerCamelCase__    )

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

									if return_labels:
															if model_class in get_values(lowerCamelCase__    ):
																					lowercase__								=    tf.ones(self.model_tester.batch_size    ,       dtype=tf.intaa    )
															elif model_class in get_values(lowerCamelCase__    ):
																					lowercase__								=    tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa    )
																					lowercase__								=    tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa    )
															elif model_class in [
															    *get_values(lowerCamelCase__    ),
															    *get_values(lowerCamelCase__    ),
															]:
																					lowercase__								=    tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa    )
															elif model_class in get_values(lowerCamelCase__    ):
																					lowercase__								=    tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa    )
															elif model_class in [
															    *get_values(lowerCamelCase__    ),
															    *get_values(lowerCamelCase__    ),
															    *get_values(lowerCamelCase__    ),
															    *get_values(lowerCamelCase__    ),
															    *get_values(lowerCamelCase__    ),
															]:
																					lowercase__								=    tf.zeros(
																					    (self.model_tester.batch_size, self.model_tester.seq_length)    ,       dtype=tf.intaa    )
									return inputs_dict



			@slow
			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
															lowercase__								=    model_class(lowerCamelCase__    )

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

															lowercase__								=    run_in_graph_mode()
															self.assertIsNotNone(lowerCamelCase__    )



			@slow
			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
															lowercase__								=    model_class(lowerCamelCase__    )

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

															lowercase__								=    run_in_graph_mode()
															self.assertIsNotNone(lowerCamelCase__    )



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




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															lowercase__								=    model_class(lowerCamelCase__    )
															if getattr(lowerCamelCase__    ,       """hf_compute_loss"""    ,       lowerCamelCase__    ):
																					# Test that model correctly compute the loss with kwargs
																					lowercase__								=    self._prepare_for_class(inputs_dict.copy()    ,       lowerCamelCase__    ,       return_labels=lowerCamelCase__    )
																					# Is there a better way to remove these decoder inputs?
																					lowercase__								=    {
																					    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""")
																					}

																					lowercase__								=    {
																					    """labels""",
																					    """label""",
																					    """label_ids""",
																					    """start_positions""",
																					    """start_position""",
																					    """end_positions""",
																					    """end_position""",
																					    """next_sentence_label""",
																					}
																					lowercase__								=    possible_label_cols.intersection(set(lowerCamelCase__    )    )
																					self.assertGreater(len(lowerCamelCase__    )    ,       0    ,       msg="""No matching label names found!"""    )
																					lowercase__								=    {key: val for key, val in prepared_for_class.items() if key in label_names}
																					lowercase__								=    {key: val for key, val in prepared_for_class.items() if key not in label_names}
																					self.assertGreater(len(lowerCamelCase__    )    ,       0    )

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

																					# Now test it with separate labels, to make sure that path works in XLA too.
																					lowercase__								=    model_class(lowerCamelCase__    )
																					model.compile(optimizer=tf.keras.optimizers.SGD(0.0    )    ,       jit_compile=lowerCamelCase__    )
																					lowercase__								=    model.fit(
																					    lowerCamelCase__    ,       lowerCamelCase__    ,       validation_data=(inputs_minus_labels, labels)    ,       steps_per_epoch=1    ,       validation_steps=1    ,       shuffle=lowerCamelCase__    ,       verbose=0    ,       )

																					lowercase__								=    history.history["""loss"""][0]
																					self.assertTrue(not isnan(lowerCamelCase__    )    )
																					lowercase__								=    history.history["""val_loss"""][0]
																					self.assertTrue(not isnan(lowerCamelCase__    )    )



			@slow
			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()
									lowercase__								=    True
									lowercase__								=    True

									if hasattr(lowerCamelCase__    ,       """use_cache"""    ):
															lowercase__								=    True

									lowercase__								=    getattr(self.model_tester    ,       """encoder_seq_length"""    ,       self.model_tester.seq_length    )
									lowercase__								=    getattr(self.model_tester    ,       """key_length"""    ,       lowerCamelCase__    )

									for model_class in self.all_model_classes[:2]:
															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
															lowercase__								=    model_class(lowerCamelCase__    )
															model.build()
															lowercase__								=    len(model(lowerCamelCase__    )    )

															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:
																											lowercase__								=    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(lowerCamelCase__    ,       saved_model=lowerCamelCase__    )
																					lowercase__								=    os.path.join(lowerCamelCase__    ,       """saved_model"""    ,       """1"""    )
																					lowercase__								=    tf.keras.models.load_model(lowerCamelCase__    )
																					lowercase__								=    model(lowerCamelCase__    )

																					if self.is_encoder_decoder:
																											lowercase__								=    outputs["""encoder_hidden_states"""]
																											lowercase__								=    outputs["""encoder_attentions"""]
																					else:
																											lowercase__								=    outputs["""hidden_states"""]
																											lowercase__								=    outputs["""attentions"""]

																					self.assertEqual(len(lowerCamelCase__    )    ,       lowerCamelCase__    )

																					lowercase__								=    getattr(
																					    self.model_tester    ,       """expected_num_hidden_layers"""    ,       self.model_tester.num_hidden_layers + 1    )

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

																					self.assertEqual(len(lowerCamelCase__    )    ,       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 A__	(   self    )     ->						int:




									'''simple docstring'''



									tf.keras.mixed_precision.set_global_policy("""mixed_float16"""    )

									# try/finally block to ensure subsequent tests run in float32
									try:
															lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()
															for model_class in self.all_model_classes[:2]:
																					lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
																					lowercase__								=    model_class(lowerCamelCase__    )
																					lowercase__								=    model(lowerCamelCase__    )

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



			@slow
			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    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"]
									lowercase__								=    {
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__    ),)
									    for module_member_name in dir(lowerCamelCase__    )
									    if module_member_name.endswith("""MainLayer"""    )
									    for module_member in (getattr(lowerCamelCase__    ,       lowerCamelCase__    ),)
									    if isinstance(lowerCamelCase__    ,       lowerCamelCase__    )
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(lowerCamelCase__    ,       """_keras_serializable"""    ,       lowerCamelCase__    )
									}

									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
																					lowercase__								=    TFSharedEmbeddings(self.model_tester.vocab_size    ,       self.model_tester.hidden_size    ,       name="""shared"""    )
																					lowercase__								=    False
																					lowercase__								=    main_layer_class(lowerCamelCase__    ,       embed_tokens=lowerCamelCase__    )
															else:
																					lowercase__								=    main_layer_class(lowerCamelCase__    )

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

															if hasattr(self.model_tester    ,       """num_labels"""    ):
																					lowercase__								=    self.model_tester.num_labels
															else:
																					lowercase__								=    2

															lowercase__								=    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    )

															lowercase__								=    main_layer(lowerCamelCase__    )[0]
															lowercase__								=    tf.keras.layers.Dense(lowerCamelCase__    ,       activation="""softmax"""    ,       name="""outputs"""    )(lowerCamelCase__    )
															lowercase__								=    tf.keras.models.Model(inputs=lowerCamelCase__    ,       outputs=[outputs]    )

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																					lowercase__								=    os.path.join(lowerCamelCase__    ,       """keras_model.h5"""    )
																					model.save(lowerCamelCase__    )
																					if "T5" in main_layer_class.__name__:
																											lowercase__								=    tf.keras.models.load_model(
																											    lowerCamelCase__    ,       custom_objects={
																											        main_layer_class.__name__: main_layer_class,
																											        """TFSharedEmbeddings""": TFSharedEmbeddings,
																											    }    ,       )
																					else:
																											lowercase__								=    tf.keras.models.load_model(
																											    lowerCamelCase__    ,       custom_objects={main_layer_class.__name__: main_layer_class}    )
																					assert isinstance(lowerCamelCase__    ,       tf.keras.Model    )
																					model(lowerCamelCase__    )



			@slow
			def A__	(   self    )     ->						int:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes[:2]:
															lowercase__								=    model_class(lowerCamelCase__    )

															lowercase__								=    copy.deepcopy(lowerCamelCase__    )

															if not self.is_encoder_decoder:
																					lowercase__								=    inputs["""input_ids"""]
																					del inputs["input_ids"]
															else:
																					lowercase__								=    inputs["""input_ids"""]
																					lowercase__								=    inputs.get("""decoder_input_ids"""    ,       lowerCamelCase__    )
																					del inputs["input_ids"]
																					inputs.pop("""decoder_input_ids"""    ,       lowerCamelCase__    )

															if not self.is_encoder_decoder:
																					lowercase__								=    model.get_input_embeddings()(lowerCamelCase__    )
															else:
																					lowercase__								=    model.get_input_embeddings()(lowerCamelCase__    )
																					lowercase__								=    model.get_input_embeddings()(lowerCamelCase__    )

															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )

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

															lowercase__								=    run_in_graph_mode()
															self.assertIsNotNone(lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    []
									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
									lowercase__								=    []
									while len(lowerCamelCase__    ) < num_bad_tokens:
															lowercase__								=    tf.squeeze(ids_tensor((1, 1)    ,       self.model_tester.vocab_size    )    ,       0    ).numpy()[0]
															if token not in special_tokens:
																					bad_tokens.append(lowerCamelCase__    )
									return bad_tokens



			def A__	(   self    ,       lowerCamelCase__    )     ->						List[str]:




									'''simple docstring'''



									for token_id in output_ids[0].numpy().tolist():
															self.assertGreaterEqual(lowerCamelCase__    ,       0    )
															self.assertLess(lowerCamelCase__    ,       self.model_tester.vocab_size    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



									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(lowerCamelCase__    )    ,       len(lowerCamelCase__    )    ):
																											# if tokens match
																											if generated_ids_slice[i - len(lowerCamelCase__    ) : i] == bad_word_ids:
																																	return True
									return False








'''simple docstring'''



def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if right <= left:
												return
						lowercase__								=    lowercase__								=    left
						lowercase__								=    right
						lowercase__								=    sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__	,  lowercase__								=    sorting[store_index], sorting[right]
						return store_index





def 	_A						(							lowercase__		):
						if len(lowercase__		) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]		)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]		)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)






