






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:
              a		:	Dict      					=			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)]
                            )
                            a		:	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    a   :
      a  :			Any								=     None
      a  :			Dict								=     ()
      a  :			Dict								=     ()
      a  :			Tuple								=     True
      a  :			Any								=     True
      a  :			Tuple								=     True
      a  :			int								=     False
      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      Optional[int]		,				__lowercase :      Dict		,				__lowercase :      str=False )							->				Dict:
        __UpperCAmelCase							:     int									=  copy.deepcopy(_lowerCamelCase )

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

        if return_labels:
          if model_class in get_values(_lowerCamelCase ):
            __UpperCAmelCase							:     Optional[Any]									=  tf.ones(self.model_tester.batch_size		,				dtype=tf.intaa )
          elif model_class in get_values(_lowerCamelCase ):
            __UpperCAmelCase							:     int									=  tf.zeros(self.model_tester.batch_size		,				dtype=tf.intaa )
            __UpperCAmelCase							:     Tuple									=  tf.zeros(self.model_tester.batch_size		,				dtype=tf.intaa )
          elif model_class in [
              *get_values(_lowerCamelCase ),
              *get_values(_lowerCamelCase ),
          ]:
            __UpperCAmelCase							:     str									=  tf.zeros(self.model_tester.batch_size		,				dtype=tf.intaa )
          elif model_class in get_values(_lowerCamelCase ):
            __UpperCAmelCase							:     Optional[Any]									=  tf.zeros(self.model_tester.batch_size		,				dtype=tf.intaa )
          elif model_class in [
              *get_values(_lowerCamelCase ),
              *get_values(_lowerCamelCase ),
              *get_values(_lowerCamelCase ),
              *get_values(_lowerCamelCase ),
              *get_values(_lowerCamelCase ),
          ]:
            __UpperCAmelCase							:     Union[str, Any]									=  tf.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)		,				dtype=tf.intaa )
        return inputs_dict
      @slow
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Any:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[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(_lowerCamelCase		,				_lowerCamelCase )
          __UpperCAmelCase							:     Optional[int]									=  model_class(_lowerCamelCase )

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

          __UpperCAmelCase							:     Any									=  run_in_graph_mode()
          self.assertIsNotNone(_lowerCamelCase )
      @slow
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Dict:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __UpperCAmelCase							:     List[Any]									=  self._prepare_for_class(_lowerCamelCase		,				_lowerCamelCase )
          __UpperCAmelCase							:     str									=  model_class(_lowerCamelCase )

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

          __UpperCAmelCase							:     Optional[Any]									=  run_in_graph_mode()
          self.assertIsNotNone(_lowerCamelCase )
      @slow
      def      UpperCAmelCase				(				self :      str )							->				List[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __UpperCAmelCase							:     Optional[int]									=  model_class(_lowerCamelCase )
          if getattr(_lowerCamelCase		,				"""hf_compute_loss"""		,				_lowerCamelCase ):
            # Test that model correctly compute the loss with kwargs
            __UpperCAmelCase							:     Union[str, Any]									=  self._prepare_for_class(inputs_dict.copy()		,				_lowerCamelCase		,				return_labels=_lowerCamelCase )
            # 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							:     Union[str, Any]									=  possible_label_cols.intersection(set(_lowerCamelCase ) )
            self.assertGreater(len(_lowerCamelCase )		,				0		,				msg="""No matching label names found!""" )
            __UpperCAmelCase							:     Tuple									=  {key: val for key, val in prepared_for_class.items() if key in label_names}
            __UpperCAmelCase							:     List[str]									=  {key: val for key, val in prepared_for_class.items() if key not in label_names}
            self.assertGreater(len(_lowerCamelCase )		,				0 )

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

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

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

        if hasattr(_lowerCamelCase		,				"""use_cache""" ):
          __UpperCAmelCase							:     Any									=  True

        __UpperCAmelCase							:     List[Any]									=  getattr(self.model_tester		,				"""encoder_seq_length"""		,				self.model_tester.seq_length )
        __UpperCAmelCase							:     List[str]									=  getattr(self.model_tester		,				"""key_length"""		,				_lowerCamelCase )

        for model_class in self.all_model_classes[:2]:
          __UpperCAmelCase							:     List[str]									=  self._prepare_for_class(_lowerCamelCase		,				_lowerCamelCase )
          __UpperCAmelCase							:     Union[str, Any]									=  model_class(_lowerCamelCase )
          model.build()
          __UpperCAmelCase							:     int									=  len(model(_lowerCamelCase ) )

          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							:     List[str]									=  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(_lowerCamelCase		,				saved_model=_lowerCamelCase )
            __UpperCAmelCase							:     List[Any]									=  os.path.join(_lowerCamelCase		,				"""saved_model"""		,				"""1""" )
            __UpperCAmelCase							:     Tuple									=  tf.keras.models.load_model(_lowerCamelCase )
            __UpperCAmelCase							:     Dict									=  model(_lowerCamelCase )

            if self.is_encoder_decoder:
              __UpperCAmelCase							:     Optional[int]									=  outputs["""encoder_hidden_states"""]
              __UpperCAmelCase							:     Optional[Any]									=  outputs["""encoder_attentions"""]
            else:
              __UpperCAmelCase							:     Optional[Any]									=  outputs["""hidden_states"""]
              __UpperCAmelCase							:     Union[str, Any]									=  outputs["""attentions"""]

            self.assertEqual(len(_lowerCamelCase )		,				_lowerCamelCase )

            __UpperCAmelCase							:     List[Any]									=  getattr(
                self.model_tester		,				"""expected_num_hidden_layers"""		,				self.model_tester.num_hidden_layers + 1 )

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

            self.assertEqual(len(_lowerCamelCase )		,				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 :      str )							->				Dict:
        tf.keras.mixed_precision.set_global_policy("""mixed_float16""" )

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

            self.assertIsNotNone(_lowerCamelCase )
        finally:
          tf.keras.mixed_precision.set_global_policy("""float32""" )
      @slow
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Any:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Tuple									=  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							:     Tuple									=  {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__ ),)
            for module_member_name in dir(_lowerCamelCase )
            if module_member_name.endswith("""MainLayer""" )
            for module_member in (getattr(_lowerCamelCase		,				_lowerCamelCase ),)
            if isinstance(_lowerCamelCase		,				_lowerCamelCase )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(_lowerCamelCase		,				"""_keras_serializable"""		,				_lowerCamelCase )
        }

        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							:     str									=  TFSharedEmbeddings(self.model_tester.vocab_size		,				self.model_tester.hidden_size		,				name="""shared""" )
            __UpperCAmelCase							:     Optional[int]									=  False
            __UpperCAmelCase							:     Union[str, Any]									=  main_layer_class(_lowerCamelCase		,				embed_tokens=_lowerCamelCase )
          else:
            __UpperCAmelCase							:     Union[str, Any]									=  main_layer_class(_lowerCamelCase )

          __UpperCAmelCase							:     List[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							:     Optional[int]									=  self.model_tester.num_labels
          else:
            __UpperCAmelCase							:     Dict									=  2

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

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCAmelCase							:     Union[str, Any]									=  os.path.join(_lowerCamelCase		,				"""keras_model.h5""" )
            model.save(_lowerCamelCase )
            if "T5" in main_layer_class.__name__:
              __UpperCAmelCase							:     Dict									=  tf.keras.models.load_model(
                  _lowerCamelCase		,				custom_objects={
                      main_layer_class.__name__: main_layer_class,
                      """TFSharedEmbeddings""": TFSharedEmbeddings,
                  }		,				)
            else:
              __UpperCAmelCase							:     Any									=  tf.keras.models.load_model(
                  _lowerCamelCase		,				custom_objects={main_layer_class.__name__: main_layer_class} )
            assert isinstance(_lowerCamelCase		,				tf.keras.Model )
            model(_lowerCamelCase )
      @slow
      def      UpperCAmelCase				(				self :      Optional[int] )							->				List[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
          __UpperCAmelCase							:     List[Any]									=  model_class(_lowerCamelCase )

          __UpperCAmelCase							:     int									=  copy.deepcopy(_lowerCamelCase )

          if not self.is_encoder_decoder:
            __UpperCAmelCase							:     Optional[int]									=  inputs["""input_ids"""]
            del inputs["input_ids"]
          else:
            __UpperCAmelCase							:     Optional[Any]									=  inputs["""input_ids"""]
            __UpperCAmelCase							:     List[str]									=  inputs.get("""decoder_input_ids"""		,				_lowerCamelCase )
            del inputs["input_ids"]
            inputs.pop("""decoder_input_ids"""		,				_lowerCamelCase )

          if not self.is_encoder_decoder:
            __UpperCAmelCase							:     Any									=  model.get_input_embeddings()(_lowerCamelCase )
          else:
            __UpperCAmelCase							:     List[Any]									=  model.get_input_embeddings()(_lowerCamelCase )
            __UpperCAmelCase							:     Union[str, Any]									=  model.get_input_embeddings()(_lowerCamelCase )

          __UpperCAmelCase							:     int									=  self._prepare_for_class(_lowerCamelCase		,				_lowerCamelCase )

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

          __UpperCAmelCase							:     Dict									=  run_in_graph_mode()
          self.assertIsNotNone(_lowerCamelCase )
      def      UpperCAmelCase				(				self :      int		,				__lowercase :      Any		,				__lowercase :      Union[str, Any] )							->				Any:
        __UpperCAmelCase							:     Union[str, Any]									=  []
        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							:     int									=  []
        while len(_lowerCamelCase ) < num_bad_tokens:
          __UpperCAmelCase							:     str									=  tf.squeeze(ids_tensor((1, 1)		,				self.model_tester.vocab_size )		,				0 ).numpy()[0]
          if token not in special_tokens:
            bad_tokens.append(_lowerCamelCase )
        return bad_tokens
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Optional[int] )							->				List[Any]:
        for token_id in output_ids[0].numpy().tolist():
          self.assertGreaterEqual(_lowerCamelCase		,				0 )
          self.assertLess(_lowerCamelCase		,				self.model_tester.vocab_size )




      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      Optional[int]		,				__lowercase :      List[str] )							->				List[str]:
        # 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(_lowerCamelCase )		,				len(_lowerCamelCase ) ):
              # if tokens match
              if generated_ids_slice[i - len(_lowerCamelCase ) : i] == bad_word_ids:
                return True
        return False










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


