
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:
														SCREAMING_SNAKE_CASE__				=							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)]
																												)
																												SCREAMING_SNAKE_CASE__				=							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__						:		Optional[Any]            =				None
							lowerCAmelCase__						:		Tuple            =				()
							lowerCAmelCase__						:		Any            =				()
							lowerCAmelCase__						:		Union[str, Any]            =				True
							lowerCAmelCase__						:		Any            =				True
							lowerCAmelCase__						:		int            =				True
							lowerCAmelCase__						:		List[Any]            =				False





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  Dict=False							)		->						dict:



											"""simple docstring"""


											__lowercase								=   copy.deepcopy(_UpperCAmelCase							)

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

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





							@slow
							def      a__					(					self    :  Tuple							)		->						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								=   self._prepare_for_class(_UpperCAmelCase	,					_UpperCAmelCase							)
															__lowercase								=   model_class(_UpperCAmelCase							)

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

															__lowercase								=   run_in_graph_mode()
															self.assertIsNotNone(_UpperCAmelCase							)





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



											"""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(_UpperCAmelCase	,					_UpperCAmelCase							)
															__lowercase								=   model_class(_UpperCAmelCase							)

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

															__lowercase								=   run_in_graph_mode()
															self.assertIsNotNone(_UpperCAmelCase							)





							@slow
							def      a__					(					self    :  Optional[int]							)		->						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(_UpperCAmelCase							)
															if getattr(_UpperCAmelCase	,					'hf_compute_loss'	,					_UpperCAmelCase							):
																			# Test that model correctly compute the loss with kwargs
																			__lowercase								=   self._prepare_for_class(inputs_dict.copy()	,					_UpperCAmelCase	,					return_labels=_UpperCAmelCase							)
																			# 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(_UpperCAmelCase							)							)
																			self.assertGreater(len(_UpperCAmelCase							)	,					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(_UpperCAmelCase							)	,					0							)

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

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

																			__lowercase								=   history.history['loss'][0]
																			self.assertTrue(not isnan(_UpperCAmelCase							)							)
																			__lowercase								=   history.history['val_loss'][0]
																			self.assertTrue(not isnan(_UpperCAmelCase							)							)





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



											"""simple docstring"""


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

											if hasattr(_UpperCAmelCase	,					'use_cache'							):
															__lowercase								=   True

											__lowercase								=   getattr(self.model_tester	,					'encoder_seq_length'	,					self.model_tester.seq_length							)
											__lowercase								=   getattr(self.model_tester	,					'key_length'	,					_UpperCAmelCase							)

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

															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(_UpperCAmelCase	,					saved_model=_UpperCAmelCase							)
																			__lowercase								=   os.path.join(_UpperCAmelCase	,					'saved_model'	,					'1'							)
																			__lowercase								=   tf.keras.models.load_model(_UpperCAmelCase							)
																			__lowercase								=   model(_UpperCAmelCase							)

																			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(_UpperCAmelCase							)	,					_UpperCAmelCase							)

																			__lowercase								=   getattr(
																			    self.model_tester	,					'expected_num_hidden_layers'	,					self.model_tester.num_hidden_layers + 1							)

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

																			self.assertEqual(len(_UpperCAmelCase							)	,					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							)		->						str:



											"""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(_UpperCAmelCase	,					_UpperCAmelCase							)
																			__lowercase								=   model_class(_UpperCAmelCase							)
																			__lowercase								=   model(_UpperCAmelCase							)

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





							@slow
							def      a__					(					self    :  int							)		->						Tuple:



											"""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(_UpperCAmelCase							)
											    if module_member_name.endswith('MainLayer'							)
											    for module_member in (getattr(_UpperCAmelCase	,					_UpperCAmelCase							),)
											    if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							)
											    and tf.keras.layers.Layer in module_member.__bases__
											    and getattr(_UpperCAmelCase	,					'_keras_serializable'	,					_UpperCAmelCase							)
											}

											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(_UpperCAmelCase	,					embed_tokens=_UpperCAmelCase							)
															else:
																			__lowercase								=   main_layer_class(_UpperCAmelCase							)

															__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(_UpperCAmelCase							)[0]
															__lowercase								=   tf.keras.layers.Dense(_UpperCAmelCase	,					activation='softmax'	,					name='outputs'							)(_UpperCAmelCase							)
															__lowercase								=   tf.keras.models.Model(inputs=_UpperCAmelCase	,					outputs=[outputs]							)

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

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





							@slow
							def      a__					(					self    :  Dict							)		->						Optional[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(_UpperCAmelCase							)

															__lowercase								=   copy.deepcopy(_UpperCAmelCase							)

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

															if not self.is_encoder_decoder:
																			__lowercase								=   model.get_input_embeddings()(_UpperCAmelCase							)
															else:
																			__lowercase								=   model.get_input_embeddings()(_UpperCAmelCase							)
																			__lowercase								=   model.get_input_embeddings()(_UpperCAmelCase							)

															__lowercase								=   self._prepare_for_class(_UpperCAmelCase	,					_UpperCAmelCase							)

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

															__lowercase								=   run_in_graph_mode()
															self.assertIsNotNone(_UpperCAmelCase							)





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  Tuple							)		->						int:



											"""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(_UpperCAmelCase							) < 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(_UpperCAmelCase							)
											return bad_tokens





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


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





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  List[str]	,					_UpperCAmelCase    :  Optional[Any]							)		->						Any:



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







def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





