



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:
        __lowerCamelCase								=   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)]
                )
                __lowerCamelCase								=   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 						_snake_case :





 """simple docstring"""



 lowerCamelCase_       =     None
 lowerCamelCase_       =     ()
 lowerCamelCase_       =     ()
 lowerCamelCase_       =     True
 lowerCamelCase_       =     True
 lowerCamelCase_       =     True
 lowerCamelCase_       =     False
 def lowercase_						(  self		,      a		,      a		,      a=False   )		->							dict:




   """simple docstring"""







   _A          =      copy.deepcopy(a   )

   if model_class in get_values(a   ):
     _A          =      {
         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   ):
       _A          =      tf.ones(self.model_tester.batch_size		,      dtype=tf.intaa   )
     elif model_class in get_values(a   ):
       _A          =      tf.zeros(self.model_tester.batch_size		,      dtype=tf.intaa   )
       _A          =      tf.zeros(self.model_tester.batch_size		,      dtype=tf.intaa   )
     elif model_class in [
         *get_values(a   ),
         *get_values(a   ),
     ]:
       _A          =      tf.zeros(self.model_tester.batch_size		,      dtype=tf.intaa   )
     elif model_class in get_values(a   ):
       _A          =      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   ),
     ]:
       _A          =      tf.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)		,      dtype=tf.intaa   )
   return inputs_dict
 @slow
 def lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     _A          =      self._prepare_for_class(a		,      a   )
     _A          =      model_class(a   )

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

     _A          =      run_in_graph_mode()
     self.assertIsNotNone(a   )
 @slow
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     _A          =      self._prepare_for_class(a		,      a   )
     _A          =      model_class(a   )

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

     _A          =      run_in_graph_mode()
     self.assertIsNotNone(a   )
 @slow
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







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

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

       _A          =      history.history['''loss'''][0]
       self.assertTrue(not isnan(a   )   )
       _A          =      history.history['''val_loss'''][0]
       self.assertTrue(not isnan(a   )   )
 @slow
 def lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







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

   if hasattr(a		,      '''use_cache'''   ):
     _A          =      True

   _A          =      getattr(self.model_tester		,      '''encoder_seq_length'''		,      self.model_tester.seq_length   )
   _A          =      getattr(self.model_tester		,      '''key_length'''		,      a   )

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

       if self.is_encoder_decoder:
         _A          =      outputs['''encoder_hidden_states''']
         _A          =      outputs['''encoder_attentions''']
       else:
         _A          =      outputs['''hidden_states''']
         _A          =      outputs['''attentions''']

       self.assertEqual(len(a   )		,      a   )

       _A          =      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 lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







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

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

       self.assertIsNotNone(a   )
   finally:
     tf.keras.mixed_precision.set_global_policy('''float32'''   )
 @slow
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A      ,				_A          =      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"]
   _A          =      {
       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
       _A          =      TFSharedEmbeddings(self.model_tester.vocab_size		,      self.model_tester.hidden_size		,      name='''shared'''   )
       _A          =      False
       _A          =      main_layer_class(a		,      embed_tokens=a   )
     else:
       _A          =      main_layer_class(a   )

     _A          =      {
         name: tf.keras.Input(tensor.shape[1:]		,      dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
     }

     if hasattr(self.model_tester		,      '''num_labels'''   ):
       _A          =      self.model_tester.num_labels
     else:
       _A          =      2

     _A          =      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   )

     _A          =      main_layer(a   )[0]
     _A          =      tf.keras.layers.Dense(a		,      activation='''softmax'''		,      name='''outputs'''   )(a   )
     _A          =      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:
       _A          =      os.path.join(a		,      '''keras_model.h5'''   )
       model.save(a   )
       if "T5" in main_layer_class.__name__:
         _A          =      tf.keras.models.load_model(
             a		,      custom_objects={
                 main_layer_class.__name__: main_layer_class,
                 '''TFSharedEmbeddings''': TFSharedEmbeddings,
             }		,      )
       else:
         _A          =      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 lowercase_						(  self   )		->							Any:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes[:2]:
     _A          =      model_class(a   )

     _A          =      copy.deepcopy(a   )

     if not self.is_encoder_decoder:
       _A          =      inputs['''input_ids''']
       del inputs["input_ids"]
     else:
       _A          =      inputs['''input_ids''']
       _A          =      inputs.get('''decoder_input_ids'''		,      a   )
       del inputs["input_ids"]
       inputs.pop('''decoder_input_ids'''		,      a   )

     if not self.is_encoder_decoder:
       _A          =      model.get_input_embeddings()(a   )
     else:
       _A          =      model.get_input_embeddings()(a   )
       _A          =      model.get_input_embeddings()(a   )

     _A          =      self._prepare_for_class(a		,      a   )

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

     _A          =      run_in_graph_mode()
     self.assertIsNotNone(a   )
 def lowercase_						(  self		,      a		,      a   )		->							List[str]:




   """simple docstring"""







   _A          =      []
   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
   _A          =      []
   while len(a   ) < num_bad_tokens:
     _A          =      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 lowercase_						(  self		,      a   )		->							Tuple:




   """simple docstring"""







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



 def lowercase_						(  self		,      a		,      a   )		->							List[str]:




   """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




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:

  # Base Case
  if counter == 0 or capacity == 0:
    return 0

  # If weight of the nth item is more than Knapsack of capacity,
  #   then this item cannot be included in the optimal solution,
  # else return the maximum of two cases:
  #   (1) nth item included
  #   (2) not included
  if weights[counter - 1] > capacity:
    return knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()