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													=		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													=		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_ :
   __A			:	Optional[Any]        =       None
   __A			:	int        =       ()
   __A			:	List[Any]        =       ()
   __A			:	Optional[Any]        =       True
   __A			:	int        =       True
   __A			:	List[Any]        =       True
   __A			:	Any        =       False






   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  List[str]			,		lowercase_       :  int			,		lowercase_       :  int=False						)      ->		Optional[Any]:
       lowercase__				:     List[str]					      =       copy.deepcopy(lowercase_						)

       if model_class in get_values(lowercase_						):
           lowercase__				:     Union[str, Any]					      =       {
               k: tf.tile(tf.expand_dims(lowercase_			,		1						)			,		(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)						)
               if isinstance(lowercase_			,		tf.Tensor						) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

       if return_labels:
           if model_class in get_values(lowercase_						):
               lowercase__				:     str					      =       tf.ones(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in get_values(lowercase_						):
               lowercase__				:     str					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
               lowercase__				:     Union[str, Any]					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in [
               *get_values(lowercase_						),
               *get_values(lowercase_						),
           ]:
               lowercase__				:     List[str]					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in get_values(lowercase_						):
               lowercase__				:     List[Any]					      =       tf.zeros(self.model_tester.batch_size			,		dtype=tf.intaa						)
           elif model_class in [
               *get_values(lowercase_						),
               *get_values(lowercase_						),
               *get_values(lowercase_						),
               *get_values(lowercase_						),
               *get_values(lowercase_						),
           ]:
               lowercase__				:     Optional[int]					      =       tf.zeros(
                   (self.model_tester.batch_size, self.model_tester.seq_length)			,		dtype=tf.intaa						)
       return inputs_dict






   @slow
   def    __UpperCamelCase  (   self       :  int						)      ->		List[Any]:
       lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
           lowercase__				:     str					      =       self._prepare_for_class(lowercase_			,		lowercase_						)
           lowercase__				:     Optional[Any]					      =       model_class(lowercase_						)

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

           lowercase__				:     int					      =       run_in_graph_mode()
           self.assertIsNotNone(lowercase_						)






   @slow
   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Union[str, Any]:
       lowercase__				:     Dict					      =       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
           lowercase__				:     List[Any]					      =       self._prepare_for_class(lowercase_			,		lowercase_						)
           lowercase__				:     int					      =       model_class(lowercase_						)

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

           lowercase__				:     List[Any]					      =       run_in_graph_mode()
           self.assertIsNotNone(lowercase_						)






   @slow
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		str:
       lowercase__				:     Optional[int]					      =       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
           lowercase__				:     Dict					      =       model_class(lowercase_						)
           if getattr(lowercase_			,		"hf_compute_loss"			,		lowercase_						):
               # Test that model correctly compute the loss with kwargs
               lowercase__				:     Dict					      =       self._prepare_for_class(inputs_dict.copy()			,		lowercase_			,		return_labels=lowercase_						)
               # Is there a better way to remove these decoder inputs?
               lowercase__				:     Optional[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''')
               }

               lowercase__				:     int					      =       {
                   '''labels''',
                   '''label''',
                   '''label_ids''',
                   '''start_positions''',
                   '''start_position''',
                   '''end_positions''',
                   '''end_position''',
                   '''next_sentence_label''',
               }
               lowercase__				:     List[Any]					      =       possible_label_cols.intersection(set(lowercase_						)						)
               self.assertGreater(len(lowercase_						)			,		0			,		msg="No matching label names found!"						)
               lowercase__				:     Union[str, Any]					      =       {key: val for key, val in prepared_for_class.items() if key in label_names}
               lowercase__				:     List[Any]					      =       {key: val for key, val in prepared_for_class.items() if key not in label_names}
               self.assertGreater(len(lowercase_						)			,		0						)

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

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

               lowercase__				:     int					      =       history.history['''loss'''][0]
               self.assertTrue(not isnan(lowercase_						)						)
               lowercase__				:     Any					      =       history.history['''val_loss'''][0]
               self.assertTrue(not isnan(lowercase_						)						)






   @slow
   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Dict:
       lowercase__				:     Dict					      =       self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__				:     Any					      =       True
       lowercase__				:     Dict					      =       True

       if hasattr(lowercase_			,		"use_cache"						):
           lowercase__				:     Optional[Any]					      =       True

       lowercase__				:     Union[str, Any]					      =       getattr(self.model_tester			,		"encoder_seq_length"			,		self.model_tester.seq_length						)
       lowercase__				:     List[Any]					      =       getattr(self.model_tester			,		"key_length"			,		lowercase_						)

       for model_class in self.all_model_classes[:2]:
           lowercase__				:     Any					      =       self._prepare_for_class(lowercase_			,		lowercase_						)
           lowercase__				:     Union[str, Any]					      =       model_class(lowercase_						)
           model.build()
           lowercase__				:     Union[str, Any]					      =       len(model(lowercase_						)						)

           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__				:     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(lowercase_			,		saved_model=lowercase_						)
               lowercase__				:     List[str]					      =       os.path.join(lowercase_			,		"saved_model"			,		"1"						)
               lowercase__				:     int					      =       tf.keras.models.load_model(lowercase_						)
               lowercase__				:     Optional[Any]					      =       model(lowercase_						)

               if self.is_encoder_decoder:
                   lowercase__				:     str					      =       outputs['''encoder_hidden_states''']
                   lowercase__				:     str					      =       outputs['''encoder_attentions''']
               else:
                   lowercase__				:     Optional[int]					      =       outputs['''hidden_states''']
                   lowercase__				:     Tuple					      =       outputs['''attentions''']

               self.assertEqual(len(lowercase_						)			,		lowercase_						)

               lowercase__				:     Dict					      =       getattr(
                   self.model_tester			,		"expected_num_hidden_layers"			,		self.model_tester.num_hidden_layers + 1						)

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

               self.assertEqual(len(lowercase_						)			,		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[Any]:
       tf.keras.mixed_precision.set_global_policy("mixed_float16"						)

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

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






   @slow
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Dict:
       lowercase__				:     Dict					      =       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__				:     str					      =       {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__						),)
           for module_member_name in dir(lowercase_						)
           if module_member_name.endswith("MainLayer"						)
           for module_member in (getattr(lowercase_			,		lowercase_						),)
           if isinstance(lowercase_			,		lowercase_						)
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(lowercase_			,		"_keras_serializable"			,		lowercase_						)
       }

       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__				:     Tuple					      =       TFSharedEmbeddings(self.model_tester.vocab_size			,		self.model_tester.hidden_size			,		name="shared"						)
               lowercase__				:     List[Any]					      =       False
               lowercase__				:     Any					      =       main_layer_class(lowercase_			,		embed_tokens=lowercase_						)
           else:
               lowercase__				:     Optional[Any]					      =       main_layer_class(lowercase_						)

           lowercase__				:     int					      =       {
               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__				:     Optional[Any]					      =       self.model_tester.num_labels
           else:
               lowercase__				:     Tuple					      =       2

           lowercase__				:     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						)

           lowercase__				:     Union[str, Any]					      =       main_layer(lowercase_						)[0]
           lowercase__				:     str					      =       tf.keras.layers.Dense(lowercase_			,		activation="softmax"			,		name="outputs"						)(lowercase_						)
           lowercase__				:     Dict					      =       tf.keras.models.Model(inputs=lowercase_			,		outputs=[outputs]						)

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

           with tempfile.TemporaryDirectory() as tmpdirname:
               lowercase__				:     Optional[int]					      =       os.path.join(lowercase_			,		"keras_model.h5"						)
               model.save(lowercase_						)
               if "T5" in main_layer_class.__name__:
                   lowercase__				:     Optional[Any]					      =       tf.keras.models.load_model(
                       lowercase_			,		custom_objects={
                           main_layer_class.__name__: main_layer_class,
                           "TFSharedEmbeddings": TFSharedEmbeddings,
                       }			,		)
               else:
                   lowercase__				:     Tuple					      =       tf.keras.models.load_model(
                       lowercase_			,		custom_objects={main_layer_class.__name__: main_layer_class}						)
               assert isinstance(lowercase_			,		tf.keras.Model						)
               model(lowercase_						)






   @slow
   def    __UpperCamelCase  (   self       :  Dict						)      ->		str:
       lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes[:2]:
           lowercase__				:     int					      =       model_class(lowercase_						)

           lowercase__				:     Tuple					      =       copy.deepcopy(lowercase_						)

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

           if not self.is_encoder_decoder:
               lowercase__				:     str					      =       model.get_input_embeddings()(lowercase_						)
           else:
               lowercase__				:     Dict					      =       model.get_input_embeddings()(lowercase_						)
               lowercase__				:     str					      =       model.get_input_embeddings()(lowercase_						)

           lowercase__				:     List[str]					      =       self._prepare_for_class(lowercase_			,		lowercase_						)

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

           lowercase__				:     int					      =       run_in_graph_mode()
           self.assertIsNotNone(lowercase_						)






   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  str			,		lowercase_       :  str						)      ->		int:
       lowercase__				:     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
       lowercase__				:     Optional[Any]					      =       []
       while len(lowercase_						) < num_bad_tokens:
           lowercase__				:     Dict					      =       tf.squeeze(ids_tensor((1, 1)			,		self.model_tester.vocab_size						)			,		0						).numpy()[0]
           if token not in special_tokens:
               bad_tokens.append(lowercase_						)
       return bad_tokens






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  Tuple						)      ->		Union[str, Any]:



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







   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  List[str]			,		lowercase_       :  Optional[int]						)      ->		Dict:



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




def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: Optional[int]):
    if len(str(_lowerCamelCase)) == 1 or len(str(_lowerCamelCase)) == 1:
        return a * b

    lowercase__				:     Tuple					      =       max(len(str(_lowerCamelCase))	,			len(str(_lowerCamelCase)))
    lowercase__				:     Union[str, Any]					      =       ma // 2

    lowercase__      ,     lowercase__				:     Union[str, Any]					      =       divmod(_lowerCamelCase	,			10**ma)
    lowercase__      ,     lowercase__				:     Tuple					      =       divmod(_lowerCamelCase	,			10**ma)

    lowercase__				:     int					      =       karatsuba(_lowerCamelCase	,			_lowerCamelCase)
    lowercase__				:     Dict					      =       karatsuba((aa + aa)	,			(ba + ba))
    lowercase__				:     Optional[int]					      =       karatsuba(_lowerCamelCase	,			_lowerCamelCase)

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

def     lowercase_       (    ):
    print(karatsuba(1_5463	,			2_3489))


if __name__ == "__main__":
      main()



