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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False    ):
            __snake_case						       =   copy.deepcopy(SCREAMING_SNAKE_CASE_    )

            if model_class in get_values(SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   {
                       k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_							,       1    )							,       (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)    )
                       if isinstance(SCREAMING_SNAKE_CASE_							,       tf.Tensor    ) and v.ndim > 0
                       else v
                       for k, v in inputs_dict.items()
                   }

            if return_labels:
                   if model_class in get_values(SCREAMING_SNAKE_CASE_    ):
                          __snake_case						       =   tf.ones(self.model_tester.batch_size							,       dtype=tf.intaa    )
                   elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
                          __snake_case						       =   tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa    )
                          __snake_case						       =   tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa    )
                   elif model_class in [
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                   ]:
                          __snake_case						       =   tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa    )
                   elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
                          __snake_case						       =   tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa    )
                   elif model_class in [
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                       *get_values(SCREAMING_SNAKE_CASE_    ),
                   ]:
                          __snake_case						       =   tf.zeros(
                              (self.model_tester.batch_size, self.model_tester.seq_length)							,       dtype=tf.intaa    )
            return inputs_dict

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                   __snake_case						       =   self._prepare_for_class(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   model_class(SCREAMING_SNAKE_CASE_    )

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

                   __snake_case						       =   run_in_graph_mode()
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                   __snake_case						       =   self._prepare_for_class(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   model_class(SCREAMING_SNAKE_CASE_    )

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

                   __snake_case						       =   run_in_graph_mode()
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
            __snake_case						       =   self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                   __snake_case						       =   model_class(SCREAMING_SNAKE_CASE_    )
                   if getattr(SCREAMING_SNAKE_CASE_							,       'hf_compute_loss'							,       SCREAMING_SNAKE_CASE_    ):
                          # Test that model correctly compute the loss with kwargs
                          __snake_case						       =   self._prepare_for_class(inputs_dict.copy()							,       SCREAMING_SNAKE_CASE_							,       return_labels=SCREAMING_SNAKE_CASE_    )
                          # Is there a better way to remove these decoder inputs?
                          __snake_case						       =   {
                              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						       =   {
                              'labels',
                              'label',
                              'label_ids',
                              'start_positions',
                              'start_position',
                              'end_positions',
                              'end_position',
                              'next_sentence_label',
                          }
                          __snake_case						       =   possible_label_cols.intersection(set(SCREAMING_SNAKE_CASE_    )    )
                          self.assertGreater(len(SCREAMING_SNAKE_CASE_    )							,       0							,       msg='No matching label names found!'    )
                          __snake_case						       =   {key: val for key, val in prepared_for_class.items() if key in label_names}
                          __snake_case						       =   {key: val for key, val in prepared_for_class.items() if key not in label_names}
                          self.assertGreater(len(SCREAMING_SNAKE_CASE_    )							,       0    )

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

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

                          __snake_case						       =   history.history['loss'][0]
                          self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_    )    )
                          __snake_case						       =   history.history['val_loss'][0]
                          self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_    )    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case						       =   True
            __snake_case						       =   True

            if hasattr(SCREAMING_SNAKE_CASE_							,       'use_cache'    ):
                   __snake_case						       =   True

            __snake_case						       =   getattr(self.model_tester							,       'encoder_seq_length'							,       self.model_tester.seq_length    )
            __snake_case						       =   getattr(self.model_tester							,       'key_length'							,       SCREAMING_SNAKE_CASE_    )

            for model_class in self.all_model_classes[:2]:
                   __snake_case						       =   self._prepare_for_class(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   model_class(SCREAMING_SNAKE_CASE_    )
                   model.build()
                   __snake_case						       =   len(model(SCREAMING_SNAKE_CASE_    )    )

                   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						       =   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(SCREAMING_SNAKE_CASE_							,       saved_model=SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       'saved_model'							,       '1'    )
                          __snake_case						       =   tf.keras.models.load_model(SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   model(SCREAMING_SNAKE_CASE_    )

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

                          self.assertEqual(len(SCREAMING_SNAKE_CASE_    )							,       SCREAMING_SNAKE_CASE_    )

                          __snake_case						       =   getattr(
                              self.model_tester							,       'expected_num_hidden_layers'							,       self.model_tester.num_hidden_layers + 1    )

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

                          self.assertEqual(len(SCREAMING_SNAKE_CASE_    )							,       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 						__lowerCamelCase				(  self    ):
            tf.keras.mixed_precision.set_global_policy('mixed_float16'    )

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

                          self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )
            finally:
                   tf.keras.mixed_precision.set_global_policy('float32'    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   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						       =   {
                module_member
                for model_class in self.all_model_classes
                for module in (import_module(model_class.__module__    ),)
                for module_member_name in dir(SCREAMING_SNAKE_CASE_    )
                if module_member_name.endswith('MainLayer'    )
                for module_member in (getattr(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ),)
                if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                and tf.keras.layers.Layer in module_member.__bases__
                and getattr(SCREAMING_SNAKE_CASE_							,       '_keras_serializable'							,       SCREAMING_SNAKE_CASE_    )
            }

            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						       =   TFSharedEmbeddings(self.model_tester.vocab_size							,       self.model_tester.hidden_size							,       name='shared'    )
                          __snake_case						       =   False
                          __snake_case						       =   main_layer_class(SCREAMING_SNAKE_CASE_							,       embed_tokens=SCREAMING_SNAKE_CASE_    )
                   else:
                          __snake_case						       =   main_layer_class(SCREAMING_SNAKE_CASE_    )

                   __snake_case						       =   {
                       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						       =   self.model_tester.num_labels
                   else:
                          __snake_case						       =   2

                   __snake_case						       =   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						       =   main_layer(SCREAMING_SNAKE_CASE_    )[0]
                   __snake_case						       =   tf.keras.layers.Dense(SCREAMING_SNAKE_CASE_							,       activation='softmax'							,       name='outputs'    )(SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   tf.keras.models.Model(inputs=SCREAMING_SNAKE_CASE_							,       outputs=[outputs]    )

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

                   with tempfile.TemporaryDirectory() as tmpdirname:
                          __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       'keras_model.h5'    )
                          model.save(SCREAMING_SNAKE_CASE_    )
                          if "T5" in main_layer_class.__name__:
                                 __snake_case						       =   tf.keras.models.load_model(
                                     SCREAMING_SNAKE_CASE_							,       custom_objects={
                                         main_layer_class.__name__: main_layer_class,
                                         'TFSharedEmbeddings': TFSharedEmbeddings,
                                     }							,       )
                          else:
                                 __snake_case						       =   tf.keras.models.load_model(
                                     SCREAMING_SNAKE_CASE_							,       custom_objects={main_layer_class.__name__: main_layer_class}    )
                          assert isinstance(SCREAMING_SNAKE_CASE_							,       tf.keras.Model    )
                          model(SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes[:2]:
                   __snake_case						       =   model_class(SCREAMING_SNAKE_CASE_    )

                   __snake_case						       =   copy.deepcopy(SCREAMING_SNAKE_CASE_    )

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

                   if not self.is_encoder_decoder:
                          __snake_case						       =   model.get_input_embeddings()(SCREAMING_SNAKE_CASE_    )
                   else:
                          __snake_case						       =   model.get_input_embeddings()(SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   model.get_input_embeddings()(SCREAMING_SNAKE_CASE_    )

                   __snake_case						       =   self._prepare_for_class(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

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

                   __snake_case						       =   run_in_graph_mode()
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            # special tokens cannot be bad tokens
            __snake_case						       =   []
            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						       =   []
            while len(SCREAMING_SNAKE_CASE_    ) < num_bad_tokens:
                   __snake_case						       =   tf.squeeze(ids_tensor((1, 1)							,       self.model_tester.vocab_size    )							,       0    ).numpy()[0]
                   if token not in special_tokens:
                          bad_tokens.append(SCREAMING_SNAKE_CASE_    )
            return bad_tokens

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            # 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(SCREAMING_SNAKE_CASE_    )							,       len(SCREAMING_SNAKE_CASE_    )    ):
                                 # if tokens match
                                 if generated_ids_slice[i - len(SCREAMING_SNAKE_CASE_    ) : i] == bad_word_ids:
                                        return True
            return False





"""simple docstring"""







from .stack import Stack

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str		)		->  bool:

  """simple docstring"""




  lowerCAmelCase_  :							Stack[str]								=					Stack()
  lowerCAmelCase_  :							List[str]								=					{'(': ')', '[': ']', '{': '}'}
  for bracket in parentheses:
    if bracket in bracket_pairs:
      stack.push(lowerCAmelCase__		)
    elif bracket in (")", "]", "}"):
      if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
        return False
  return stack.is_empty()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     lowercase__						:							List[str]        =      ["""((()))""", """((())""", """(()))"""]
     print("""Balanced parentheses demonstration:\n""")
     for example in examples:
          lowercase__						:							List[Any]        =      """""" if balanced_parentheses(example) else """not """
          print(f'{example} is {not_str}balanced')





