


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:
            _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)]
                        )
                        _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_ :







 """simple docstring"""





 _UpperCamelCase					:			Optional[Any]     	=       None
 _UpperCamelCase					:			Optional[int]     	=       ()
 _UpperCamelCase					:			List[str]     	=       ()
 _UpperCamelCase					:			Union[str, Any]     	=       True
 _UpperCamelCase					:			List[str]     	=       True
 _UpperCamelCase					:			Any     	=       True
 _UpperCamelCase					:			Optional[int]     	=       False
 def 	_UpperCamelCase     (      self       :			Dict							,     _a       :			int							,     _a       :			List[str]							,     _a       :			int=False					)     ->      dict:
      UpperCAmelCase    :					List[str]			=      copy.deepcopy(_a					)

      if model_class in get_values(_a					):
           UpperCAmelCase    :					int			=      {
               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    :					List[str]			=      tf.ones(self.model_tester.batch_size							,     dtype=tf.intaa					)
           elif model_class in get_values(_a					):
                UpperCAmelCase    :					List[str]			=      tf.zeros(self.model_tester.batch_size							,     dtype=tf.intaa					)
                UpperCAmelCase    :					Optional[int]			=      tf.zeros(self.model_tester.batch_size							,     dtype=tf.intaa					)
           elif model_class in [
               *get_values(_a					),
               *get_values(_a					),
           ]:
                UpperCAmelCase    :					Union[str, Any]			=      tf.zeros(self.model_tester.batch_size							,     dtype=tf.intaa					)
           elif model_class in get_values(_a					):
                UpperCAmelCase    :					str			=      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    :					List[Any]			=      tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length)							,     dtype=tf.intaa					)
      return inputs_dict
 @slow
 def 	_UpperCamelCase     (      self       :			int					)     ->      Optional[int]:
      UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
           UpperCAmelCase    :					Any			=      self._prepare_for_class(_a							,     _a					)
           UpperCAmelCase    :					Any			=      model_class(_a					)

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

           UpperCAmelCase    :					Dict			=      run_in_graph_mode()
           self.assertIsNotNone(_a					)
 @slow
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      int:
      UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      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[int]			=      model_class(_a					)

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

           UpperCAmelCase    :					Optional[int]			=      run_in_graph_mode()
           self.assertIsNotNone(_a					)
 @slow
 def 	_UpperCamelCase     (      self       :			Optional[Any]					)     ->      List[str]:
      # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[Any]			=      self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
           UpperCAmelCase    :					str			=      model_class(_a					)
           if getattr(_a							,     "hf_compute_loss"							,     _a					):
                # Test that model correctly compute the loss with kwargs
                UpperCAmelCase    :					List[Any]			=      self._prepare_for_class(inputs_dict.copy()							,     _a							,     return_labels=_a					)
                # Is there a better way to remove these decoder inputs?
                UpperCAmelCase    :					Optional[int]			=      {
                    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    :					Optional[Any]			=      {
                    "labels",
                    "label",
                    "label_ids",
                    "start_positions",
                    "start_position",
                    "end_positions",
                    "end_position",
                    "next_sentence_label",
                }
                UpperCAmelCase    :					Optional[int]			=      possible_label_cols.intersection(set(_a					)					)
                self.assertGreater(len(_a					)							,     0							,     msg="No matching label names found!"					)
                UpperCAmelCase    :					List[str]			=      {key: val for key, val in prepared_for_class.items() if key in label_names}
                UpperCAmelCase    :					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
                UpperCAmelCase    :					Optional[Any]			=      model.fit(
                    _a							,     validation_data=_a							,     steps_per_epoch=1							,     validation_steps=1							,     shuffle=_a							,     verbose=0							,     )
                UpperCAmelCase    :					List[str]			=      history.history["loss"][0]
                self.assertTrue(not isnan(_a					)					)
                UpperCAmelCase    :					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    :					Any			=      model_class(_a					)
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0					)							,     jit_compile=_a					)
                UpperCAmelCase    :					Optional[Any]			=      model.fit(
                    _a							,     _a							,     validation_data=(inputs_minus_labels, labels)							,     steps_per_epoch=1							,     validation_steps=1							,     shuffle=_a							,     verbose=0							,     )

                UpperCAmelCase    :					Optional[Any]			=      history.history["loss"][0]
                self.assertTrue(not isnan(_a					)					)
                UpperCAmelCase    :					Dict			=      history.history["val_loss"][0]
                self.assertTrue(not isnan(_a					)					)
 @slow
 def 	_UpperCamelCase     (      self       :			Union[str, Any]					)     ->      Optional[Any]:
      UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      self.model_tester.prepare_config_and_inputs_for_common()
      UpperCAmelCase    :					str			=      True
      UpperCAmelCase    :					int			=      True

      if hasattr(_a							,     "use_cache"					):
           UpperCAmelCase    :					str			=      True

      UpperCAmelCase    :					Dict			=      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    :					Dict			=      self._prepare_for_class(_a							,     _a					)
           UpperCAmelCase    :					Dict			=      model_class(_a					)
           model.build()
           UpperCAmelCase    :					Optional[int]			=      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    :					Union[str, 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    :					str			=      os.path.join(_a							,     "saved_model"							,     "1"					)
                UpperCAmelCase    :					Union[str, Any]			=      tf.keras.models.load_model(_a					)
                UpperCAmelCase    :					int			=      model(_a					)

                if self.is_encoder_decoder:
                     UpperCAmelCase    :					Dict			=      outputs["encoder_hidden_states"]
                     UpperCAmelCase    :					Optional[Any]			=      outputs["encoder_attentions"]
                else:
                     UpperCAmelCase    :					List[str]			=      outputs["hidden_states"]
                     UpperCAmelCase    :					List[str]			=      outputs["attentions"]

                self.assertEqual(len(_a					)							,     _a					)

                UpperCAmelCase    :					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 	_UpperCamelCase     (      self       :			Optional[Any]					)     ->      List[str]:
      tf.keras.mixed_precision.set_global_policy("mixed_float16"					)

      # try/finally block to ensure subsequent tests run in float32
      try:
           UpperCAmelCase	,			UpperCAmelCase    :					Union[str, Any]			=      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    :					Union[str, Any]			=      model_class(_a					)
                UpperCAmelCase    :					Union[str, Any]			=      model(_a					)

                self.assertIsNotNone(_a					)
      finally:
           tf.keras.mixed_precision.set_global_policy("float32"					)
 @slow
 def 	_UpperCamelCase     (      self       :			int					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					List[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"]
      UpperCAmelCase    :					Optional[int]			=      {
          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    :					List[Any]			=      False
                UpperCAmelCase    :					Union[str, Any]			=      main_layer_class(_a							,     embed_tokens=_a					)
           else:
                UpperCAmelCase    :					List[Any]			=      main_layer_class(_a					)

           UpperCAmelCase    :					Optional[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    :					int			=      self.model_tester.num_labels
           else:
                UpperCAmelCase    :					str			=      2

           UpperCAmelCase    :					Optional[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    :					Union[str, Any]			=      main_layer(_a					)[0]
           UpperCAmelCase    :					Tuple			=      tf.keras.layers.Dense(_a							,     activation="softmax"							,     name="outputs"					)(_a					)
           UpperCAmelCase    :					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:
                UpperCAmelCase    :					int			=      os.path.join(_a							,     "keras_model.h5"					)
                model.save(_a					)
                if "T5" in main_layer_class.__name__:
                     UpperCAmelCase    :					Any			=      tf.keras.models.load_model(
                         _a							,     custom_objects={
                             main_layer_class.__name__: main_layer_class,
                             "TFSharedEmbeddings": TFSharedEmbeddings,
                         }							,     )
                else:
                     UpperCAmelCase    :					int			=      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 	_UpperCamelCase     (      self       :			List[str]					)     ->      Tuple:
      UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:2]:
           UpperCAmelCase    :					Tuple			=      model_class(_a					)

           UpperCAmelCase    :					Optional[int]			=      copy.deepcopy(_a					)

           if not self.is_encoder_decoder:
                UpperCAmelCase    :					Tuple			=      inputs["input_ids"]
                del inputs["input_ids"]
           else:
                UpperCAmelCase    :					List[Any]			=      inputs["input_ids"]
                UpperCAmelCase    :					int			=      inputs.get("decoder_input_ids"							,     _a					)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids"							,     _a					)

           if not self.is_encoder_decoder:
                UpperCAmelCase    :					Union[str, Any]			=      model.get_input_embeddings()(_a					)
           else:
                UpperCAmelCase    :					Optional[int]			=      model.get_input_embeddings()(_a					)
                UpperCAmelCase    :					Any			=      model.get_input_embeddings()(_a					)

           UpperCAmelCase    :					Union[str, Any]			=      self._prepare_for_class(_a							,     _a					)

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

           UpperCAmelCase    :					Union[str, Any]			=      run_in_graph_mode()
           self.assertIsNotNone(_a					)
 def 	_UpperCamelCase     (      self       :			List[str]							,     _a       :			List[str]							,     _a       :			Any					)     ->      Any:
      # special tokens cannot be bad tokens
      UpperCAmelCase    :					Optional[int]			=      []
      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    :					Tuple			=      []
      while len(_a					) < num_bad_tokens:
           UpperCAmelCase    :					Dict			=      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 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			Optional[Any]					)     ->      Union[str, Any]:
      for token_id in output_ids[0].numpy().tolist():
           self.assertGreaterEqual(_a							,     0					)
           self.assertLess(_a							,     self.model_tester.vocab_size					)

 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Any							,     _a       :			List[Any]					)     ->      Union[str, Any]:
      # 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 	A (		A__			):
     UpperCAmelCase    :					Dict			=      9

     while num > 0:
          if num % 1_0 != digit:
               return False
          num //= 1_0_0
          digit -= 1

     return True







def 	A (		):
     UpperCAmelCase    :					Optional[Any]			=      1_3_8_9_0_2_6_6_3

     while not is_square_form(num * num			):
          if num % 1_0 == 3:
               num -= 6  # (3 - 6) % 10 = 7
          else:
               num -= 4  # (7 - 4) % 10 = 3

     return num * 1_0


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



