



"""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:
								__UpperCAmelCase      :      Dict   					=					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      :      Optional[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 	__lowerCAmelCase							:


					'''simple docstring'''


					a__        =     None
					a__        =     ()
					a__        =     ()
					a__        =     True
					a__        =     True
					a__        =     True
					a__        =     False







					def 		_a (      self    ,       a    ,       a    ,       a=False   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						copy.deepcopy(a   )

										if model_class in get_values(a   ):
															snake_case_						:Dict								=						{
															    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   ):
																				snake_case_						:List[str]								=						tf.ones(self.model_tester.batch_size    ,       dtype=tf.intaa   )
															elif model_class in get_values(a   ):
																				snake_case_						:Union[str, Any]								=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa   )
																				snake_case_						:List[Any]								=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa   )
															elif model_class in [
															    *get_values(a   ),
															    *get_values(a   ),
															]:
																				snake_case_						:Dict								=						tf.zeros(self.model_tester.batch_size    ,       dtype=tf.intaa   )
															elif model_class in get_values(a   ):
																				snake_case_						: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   ),
															]:
																				snake_case_						:Optional[Any]								=						tf.zeros(
																				    (self.model_tester.batch_size, self.model_tester.seq_length)    ,       dtype=tf.intaa   )
										return inputs_dict







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
															snake_case_						:Union[str, Any]								=						self._prepare_for_class(a    ,       a   )
															snake_case_						:Tuple								=						model_class(a   )

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

															snake_case_						:str								=						run_in_graph_mode()
															self.assertIsNotNone(a   )







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:List[Any]								=						self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
															snake_case_						:Dict								=						self._prepare_for_class(a    ,       a   )
															snake_case_						:Optional[Any]								=						model_class(a   )

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

															snake_case_						:Dict								=						run_in_graph_mode()
															self.assertIsNotNone(a   )







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





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

																				snake_case_						:Optional[Any]								=						{
																				    "labels",
																				    "label",
																				    "label_ids",
																				    "start_positions",
																				    "start_position",
																				    "end_positions",
																				    "end_position",
																				    "next_sentence_label",
																				}
																				snake_case_						:Tuple								=						possible_label_cols.intersection(set(a   )   )
																				self.assertGreater(len(a   )    ,       0    ,       msg="No matching label names found!"   )
																				snake_case_						:List[str]								=						{key: val for key, val in prepared_for_class.items() if key in label_names}
																				snake_case_						:Union[str, Any]								=						{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
																				snake_case_						:Optional[int]								=						model.fit(
																				    a    ,       validation_data=a    ,       steps_per_epoch=1    ,       validation_steps=1    ,       shuffle=a    ,       verbose=0    ,       )
																				snake_case_						:str								=						history.history["loss"][0]
																				self.assertTrue(not isnan(a   )   )
																				snake_case_						:str								=						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.
																				snake_case_						:Tuple								=						model_class(a   )
																				model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )    ,       jit_compile=a   )
																				snake_case_						:int								=						model.fit(
																				    a    ,       a    ,       validation_data=(inputs_minus_labels, labels)    ,       steps_per_epoch=1    ,       validation_steps=1    ,       shuffle=a    ,       verbose=0    ,       )

																				snake_case_						:Union[str, Any]								=						history.history["loss"][0]
																				self.assertTrue(not isnan(a   )   )
																				snake_case_						:Union[str, Any]								=						history.history["val_loss"][0]
																				self.assertTrue(not isnan(a   )   )







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_						:Any								=						True
										snake_case_						:Tuple								=						True

										if hasattr(a    ,       "use_cache"   ):
															snake_case_						:int								=						True

										snake_case_						:Optional[int]								=						getattr(self.model_tester    ,       "encoder_seq_length"    ,       self.model_tester.seq_length   )
										snake_case_						:Tuple								=						getattr(self.model_tester    ,       "key_length"    ,       a   )

										for model_class in self.all_model_classes[:2]:
															snake_case_						:int								=						self._prepare_for_class(a    ,       a   )
															snake_case_						:int								=						model_class(a   )
															model.build()
															snake_case_						:List[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:
																									snake_case_						:List[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   )
																				snake_case_						:Dict								=						os.path.join(a    ,       "saved_model"    ,       "1"   )
																				snake_case_						:Any								=						tf.keras.models.load_model(a   )
																				snake_case_						:int								=						model(a   )

																				if self.is_encoder_decoder:
																									snake_case_						:List[str]								=						outputs["encoder_hidden_states"]
																									snake_case_						:List[str]								=						outputs["encoder_attentions"]
																				else:
																									snake_case_						:Union[str, Any]								=						outputs["hidden_states"]
																									snake_case_						:Dict								=						outputs["attentions"]

																				self.assertEqual(len(a   )    ,       a   )

																				snake_case_						:Union[str, Any]								=						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   ):




										"""simple docstring"""





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

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

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







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:str								=						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"]
										snake_case_						:List[Any]								=						{
										    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
																				snake_case_						:str								=						TFSharedEmbeddings(self.model_tester.vocab_size    ,       self.model_tester.hidden_size    ,       name="shared"   )
																				snake_case_						:Tuple								=						False
																				snake_case_						:Any								=						main_layer_class(a    ,       embed_tokens=a   )
															else:
																				snake_case_						:Optional[int]								=						main_layer_class(a   )

															snake_case_						: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"   ):
																				snake_case_						:Union[str, Any]								=						self.model_tester.num_labels
															else:
																				snake_case_						:Optional[Any]								=						2

															snake_case_						:Optional[int]								=						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   )

															snake_case_						:int								=						main_layer(a   )[0]
															snake_case_						:str								=						tf.keras.layers.Dense(a    ,       activation="softmax"    ,       name="outputs"   )(a   )
															snake_case_						:List[str]								=						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:
																				snake_case_						:Tuple								=						os.path.join(a    ,       "keras_model.h5"   )
																				model.save(a   )
																				if "T5" in main_layer_class.__name__:
																									snake_case_						:Any								=						tf.keras.models.load_model(
																									    a    ,       custom_objects={
																									        main_layer_class.__name__: main_layer_class,
																									        "TFSharedEmbeddings": TFSharedEmbeddings,
																									    }    ,       )
																				else:
																									snake_case_						:Dict								=						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   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes[:2]:
															snake_case_						:List[Any]								=						model_class(a   )

															snake_case_						:int								=						copy.deepcopy(a   )

															if not self.is_encoder_decoder:
																				snake_case_						:str								=						inputs["input_ids"]
																				del inputs["input_ids"]
															else:
																				snake_case_						:Union[str, Any]								=						inputs["input_ids"]
																				snake_case_						:List[Any]								=						inputs.get("decoder_input_ids"    ,       a   )
																				del inputs["input_ids"]
																				inputs.pop("decoder_input_ids"    ,       a   )

															if not self.is_encoder_decoder:
																				snake_case_						:Tuple								=						model.get_input_embeddings()(a   )
															else:
																				snake_case_						:List[Any]								=						model.get_input_embeddings()(a   )
																				snake_case_						:Dict								=						model.get_input_embeddings()(a   )

															snake_case_						:str								=						self._prepare_for_class(a    ,       a   )

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

															snake_case_						:str								=						run_in_graph_mode()
															self.assertIsNotNone(a   )







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						: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
										snake_case_						:Dict								=						[]
										while len(a   ) < num_bad_tokens:
															snake_case_						: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 		_a (      self    ,       a   ):




										"""simple docstring"""





										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   ):




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









"""simple docstring"""






def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     ):




					"""simple docstring"""
					snake_case_						:List[Any]								=						list(_A							)
					snake_case_						:int								=						list(_A							)
					snake_case_						:List[Any]								=						len(_A							)
					snake_case_						:List[Any]								=						len(_A							)

					snake_case_						:Union[str, Any]								=						[
					    [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]
					snake_case_						:int								=						[
					    ["0" for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]

					for i in range(1,     len_source_seq + 1							):
										snake_case_						:List[str]								=						i * delete_cost
										snake_case_						:str								=						F'''D{source_seq[i - 1]:c}'''

					for i in range(1,     len_destination_seq + 1							):
										snake_case_						:int								=						i * insert_cost
										snake_case_						:Dict								=						F'''I{destination_seq[i - 1]:c}'''

					for i in range(1,     len_source_seq + 1							):
										for j in range(1,     len_destination_seq + 1							):
															if source_seq[i - 1] == destination_seq[j - 1]:
																				snake_case_						:List[Any]								=						costs[i - 1][j - 1] + copy_cost
																				snake_case_						:str								=						F'''C{source_seq[i - 1]:c}'''
															else:
																				snake_case_						:Any								=						costs[i - 1][j - 1] + replace_cost
																				snake_case_						:Any								=						F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

															if costs[i - 1][j] + delete_cost < costs[i][j]:
																				snake_case_						:str								=						costs[i - 1][j] + delete_cost
																				snake_case_						:Tuple								=						F'''D{source_seq[i - 1]:c}'''

															if costs[i][j - 1] + insert_cost < costs[i][j]:
																				snake_case_						:int								=						costs[i][j - 1] + insert_cost
																				snake_case_						:int								=						F'''I{destination_seq[j - 1]:c}'''

					return costs, ops
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					if i == 0 and j == 0:
										return []
					else:
										if ops[i][j][0] == "C" or ops[i][j][0] == "R":
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     i - 1,     j - 1							)
															seq.append(ops[i][j]							)
															return seq
										elif ops[i][j][0] == "D":
															snake_case_						:Union[str, Any]								=						assemble_transformation(_A,     i - 1,     _A							)
															seq.append(ops[i][j]							)
															return seq
										else:
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     _A,     j - 1							)
															seq.append(ops[i][j]							)
															return seq


if __name__ == "__main__":
				__UpperCAmelCase					,	__UpperCAmelCase      :      List[Any]   					=					compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				__UpperCAmelCase      :      List[Any]   					=					len(operations)
				__UpperCAmelCase      :      Dict   					=					len(operations[0])
				__UpperCAmelCase      :      List[str]   					=					assemble_transformation(operations, m - 1, n - 1)

				__UpperCAmelCase      :      Any   					=					list('Python')
				__UpperCAmelCase      :      Tuple   					=					0
				__UpperCAmelCase      :      Optional[int]   					=					0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																__UpperCAmelCase      :      Any   					=					op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))

