'''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:
    __SCREAMING_SNAKE_CASE	:					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)]
        )
        __SCREAMING_SNAKE_CASE	:					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'''

      __UpperCamelCase:  Optional[Any]      =						None
      __UpperCamelCase:  Optional[Any]      =						()
      __UpperCamelCase:  Union[str, Any]      =						()
      __UpperCamelCase:  Dict      =						True
      __UpperCamelCase:  Tuple      =						True
      __UpperCamelCase:  List[str]      =						True
      __UpperCamelCase:  Any      =						False




      def 					_A     (	self						:    Optional[int]						, A						:    int						, A						:    Dict						, A						:    Optional[int]=False	):
             _UpperCAmelCase					:  Any											=   copy.deepcopy(A	)

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




      @slow
      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes[:2]:
                    _UpperCAmelCase					:  str											=   self._prepare_for_class(A						, A	)
                    _UpperCAmelCase					:  Tuple											=   model_class(A	)

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

                    _UpperCAmelCase					:  Any											=   run_in_graph_mode()
                    self.assertIsNotNone(A	)




      @slow
      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes[:2]:
                    _UpperCAmelCase					:  Union[str, Any]											=   self._prepare_for_class(A						, A	)
                    _UpperCAmelCase					:  Optional[Any]											=   model_class(A	)

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

                    _UpperCAmelCase					:  str											=   run_in_graph_mode()
                    self.assertIsNotNone(A	)




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

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

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




      @slow
      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase					:  Optional[Any]											=   True
             _UpperCAmelCase					:  str											=   True

             if hasattr(A						, "use_cache"	):
                    _UpperCAmelCase					:  Optional[int]											=   True

             _UpperCAmelCase					:  Any											=   getattr(self.model_tester						, "encoder_seq_length"						, self.model_tester.seq_length	)
             _UpperCAmelCase					:  Tuple											=   getattr(self.model_tester						, "key_length"						, A	)

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

                           if self.is_encoder_decoder:
                                  _UpperCAmelCase					:  Tuple											=   outputs["encoder_hidden_states"]
                                  _UpperCAmelCase					:  Optional[Any]											=   outputs["encoder_attentions"]
                           else:
                                  _UpperCAmelCase					:  int											=   outputs["hidden_states"]
                                  _UpperCAmelCase					:  Optional[Any]											=   outputs["attentions"]

                           self.assertEqual(len(A	)						, A	)

                           _UpperCAmelCase					:  List[str]											=   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						:    Tuple	):
             tf.keras.mixed_precision.set_global_policy("mixed_float16"	)

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

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




      @slow
      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  Optional[int]											=   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"]
             _UpperCAmelCase					:  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
                           _UpperCAmelCase					:  Optional[Any]											=   TFSharedEmbeddings(self.model_tester.vocab_size						, self.model_tester.hidden_size						, name="shared"	)
                           _UpperCAmelCase					:  Dict											=   False
                           _UpperCAmelCase					:  str											=   main_layer_class(A						, embed_tokens=A	)
                    else:
                           _UpperCAmelCase					:  Optional[Any]											=   main_layer_class(A	)

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

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

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




      @slow
      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes[:2]:
                    _UpperCAmelCase					:  List[Any]											=   model_class(A	)

                    _UpperCAmelCase					:  Optional[Any]											=   copy.deepcopy(A	)

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

                    if not self.is_encoder_decoder:
                           _UpperCAmelCase					:  str											=   model.get_input_embeddings()(A	)
                    else:
                           _UpperCAmelCase					:  str											=   model.get_input_embeddings()(A	)
                           _UpperCAmelCase					:  Union[str, Any]											=   model.get_input_embeddings()(A	)

                    _UpperCAmelCase					:  List[str]											=   self._prepare_for_class(A						, A	)

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

                    _UpperCAmelCase					:  List[Any]											=   run_in_graph_mode()
                    self.assertIsNotNone(A	)




      def 					_A     (	self						:    List[str]						, A						:    int						, A						:    int	):
             # special tokens cannot be bad tokens
             _UpperCAmelCase					:  Dict											=   []
             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
             _UpperCAmelCase					:  str											=   []
             while len(A	) < num_bad_tokens:
                    _UpperCAmelCase					:  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						:    List[Any]						, A						:    Any	):
             for token_id in output_ids[0].numpy().tolist():
                    self.assertGreaterEqual(A						, 0	)
                    self.assertLess(A						, self.model_tester.vocab_size	)





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


'''simple docstring'''




def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				List[Any]   )   ->   List[str]:


       """simple docstring"""







       if len(str(_UpperCAmelCase   )   ) == 1 or len(str(_UpperCAmelCase   )   ) == 1:
              return a * b

       _UpperCAmelCase					:  Any											=   max(len(str(_UpperCAmelCase   )   )      ,     len(str(_UpperCAmelCase   )   )   )
       _UpperCAmelCase					:  Any											=   ma // 2

       _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   divmod(_UpperCAmelCase      ,     10**ma   )
       _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   divmod(_UpperCAmelCase      ,     10**ma   )

       _UpperCAmelCase					:  List[str]											=   karatsuba(_UpperCAmelCase      ,     _UpperCAmelCase   )
       _UpperCAmelCase					:  int											=   karatsuba((aa + aa)      ,     (ba + ba)   )
       _UpperCAmelCase					:  Any											=   karatsuba(_UpperCAmelCase      ,     _UpperCAmelCase   )

       return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)

def      UpperCamelCase_							( )   ->   List[str]:


       """simple docstring"""







       print(karatsuba(15_463      ,     23_489   )   )


if __name__ == "__main__":
  main()

