
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:
														lowercase__						:Optional[int]					    =       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)]
																												)
																												lowercase__						:List[Any]					    =       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 							lowercase						:
							lowercase_			:			str			   =None
							lowercase_			:			List[str]			   =()
							lowercase_			:			List[Any]			   =()
							lowercase_			:			Any			   =True
							lowercase_			:			Optional[Any]			   =True
							lowercase_			:			Optional[int]			   =True
							lowercase_			:			Any			   =False




							def 					A__ (							self  ,A__  ,A__  ,A__=False):
												lowercase		      =		copy.deepcopy(A__)

												if model_class in get_values(A__):
																	lowercase		      =		{
																	    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__):
																						lowercase		      =		tf.ones(self.model_tester.batch_size  ,dtype=tf.intaa)
																	elif model_class in get_values(A__):
																						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(A__),
																	    *get_values(A__),
																	]:
																						lowercase		      =		tf.zeros(self.model_tester.batch_size  ,dtype=tf.intaa)
																	elif model_class in get_values(A__):
																						lowercase		      =		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__),
																	]:
																						lowercase		      =		tf.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length)  ,dtype=tf.intaa)
												return inputs_dict




							@slow
							def 					A__ (							self):
												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(A__  ,A__)
																	lowercase		      =		model_class(A__)

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

																	lowercase		      =		run_in_graph_mode()
																	self.assertIsNotNone(A__)




							@slow
							def 					A__ (							self):
												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(A__  ,A__)
																	lowercase		      =		model_class(A__)

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

																	lowercase		      =		run_in_graph_mode()
																	self.assertIsNotNone(A__)




							@slow
							def 					A__ (							self):
												# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes[:2]:
																	lowercase		      =		model_class(A__)
																	if getattr(A__  ,'''hf_compute_loss'''  ,A__):
																						# Test that model correctly compute the loss with kwargs
																						lowercase		      =		self._prepare_for_class(inputs_dict.copy()  ,A__  ,return_labels=A__)
																						# 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(A__))
																						self.assertGreater(len(A__)  ,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(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
																						lowercase		      =		model.fit(
																						    A__  ,validation_data=A__  ,steps_per_epoch=1  ,validation_steps=1  ,shuffle=A__  ,verbose=0  ,)
																						lowercase		      =		history.history['''loss'''][0]
																						self.assertTrue(not isnan(A__))
																						lowercase		      =		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.
																						lowercase		      =		model_class(A__)
																						model.compile(optimizer=tf.keras.optimizers.SGD(0.0)  ,jit_compile=A__)
																						lowercase		      =		model.fit(
																						    A__  ,A__  ,validation_data=(inputs_minus_labels, labels)  ,steps_per_epoch=1  ,validation_steps=1  ,shuffle=A__  ,verbose=0  ,)

																						lowercase		      =		history.history['''loss'''][0]
																						self.assertTrue(not isnan(A__))
																						lowercase		      =		history.history['''val_loss'''][0]
																						self.assertTrue(not isnan(A__))




							@slow
							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()
												lowercase		      =		True
												lowercase		      =		True

												if hasattr(A__  ,'''use_cache'''):
																	lowercase		      =		True

												lowercase		      =		getattr(self.model_tester  ,'''encoder_seq_length'''  ,self.model_tester.seq_length)
												lowercase		      =		getattr(self.model_tester  ,'''key_length'''  ,A__)

												for model_class in self.all_model_classes[:2]:
																	lowercase		      =		self._prepare_for_class(A__  ,A__)
																	lowercase		      =		model_class(A__)
																	model.build()
																	lowercase		      =		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:
																											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(A__  ,saved_model=A__)
																						lowercase		      =		os.path.join(A__  ,'''saved_model'''  ,'''1''')
																						lowercase		      =		tf.keras.models.load_model(A__)
																						lowercase		      =		model(A__)

																						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(A__)  ,A__)

																						lowercase		      =		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 					A__ (							self):
												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(A__  ,A__)
																						lowercase		      =		model_class(A__)
																						lowercase		      =		model(A__)

																						self.assertIsNotNone(A__)
												finally:
																	tf.keras.mixed_precision.set_global_policy('''float32''')




							@slow
							def 					A__ (							self):
												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(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
																						lowercase		      =		TFSharedEmbeddings(self.model_tester.vocab_size  ,self.model_tester.hidden_size  ,name='''shared''')
																						lowercase		      =		False
																						lowercase		      =		main_layer_class(A__  ,embed_tokens=A__)
																	else:
																						lowercase		      =		main_layer_class(A__)

																	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(A__)[0]
																	lowercase		      =		tf.keras.layers.Dense(A__  ,activation='''softmax'''  ,name='''outputs''')(A__)
																	lowercase		      =		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:
																						lowercase		      =		os.path.join(A__  ,'''keras_model.h5''')
																						model.save(A__)
																						if "T5" in main_layer_class.__name__:
																											lowercase		      =		tf.keras.models.load_model(
																											    A__  ,custom_objects={
																											        main_layer_class.__name__: main_layer_class,
																											        '''TFSharedEmbeddings''': TFSharedEmbeddings,
																											    }  ,)
																						else:
																											lowercase		      =		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 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes[:2]:
																	lowercase		      =		model_class(A__)

																	lowercase		      =		copy.deepcopy(A__)

																	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'''  ,A__)
																						del inputs["input_ids"]
																						inputs.pop('''decoder_input_ids'''  ,A__)

																	if not self.is_encoder_decoder:
																						lowercase		      =		model.get_input_embeddings()(A__)
																	else:
																						lowercase		      =		model.get_input_embeddings()(A__)
																						lowercase		      =		model.get_input_embeddings()(A__)

																	lowercase		      =		self._prepare_for_class(A__  ,A__)

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

																	lowercase		      =		run_in_graph_mode()
																	self.assertIsNotNone(A__)




							def 					A__ (							self  ,A__  ,A__):
												# special tokens cannot be bad tokens
												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(A__) < 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(A__)
												return bad_tokens




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





							def 					A__ (							self  ,A__  ,A__):
												# 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






def 			UpperCamelCase       (				lowerCAmelCase__ = 30						):
					'''simple docstring'''
					lowercase		      =		exponent + 2
					lowercase		      =		(1 + 5**0.5) / 2
					lowercase		      =		(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(lowerCAmelCase__						)


if __name__ == "__main__":
							print(F'{solution() = }')




