





"""simple docstring"""




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 			_lowerCamelCase	:



      """simple docstring"""






      UpperCAmelCase_  : Dict				      =None
      UpperCAmelCase_  : List[str]				      =()
      UpperCAmelCase_  : Union[str, Any]				      =()
      UpperCAmelCase_  : Union[str, Any]				      =True
      UpperCAmelCase_  : Dict				      =True
      UpperCAmelCase_  : Tuple				      =True
      UpperCAmelCase_  : Any				      =False







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=False				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    copy.deepcopy(__lowerCAmelCase				)

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







      @slow
      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			Any            =    self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __snake_case				:			Optional[int]            =    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase				)
          __snake_case				:			List[Any]            =    model_class(__lowerCAmelCase				)

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

          __snake_case				:			str            =    run_in_graph_mode()
          self.assertIsNotNone(__lowerCAmelCase				)







      @slow
      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			List[str]            =    self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __snake_case				:			List[str]            =    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase				)
          __snake_case				:			List[str]            =    model_class(__lowerCAmelCase				)

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

          __snake_case				:			Optional[Any]            =    run_in_graph_mode()
          self.assertIsNotNone(__lowerCAmelCase				)







      @slow
      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			Optional[Any]            =    self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          __snake_case				:			Optional[Any]            =    model_class(__lowerCAmelCase				)
          if getattr(__lowerCAmelCase			,  "hf_compute_loss"			,  __lowerCAmelCase				):
            # Test that model correctly compute the loss with kwargs
            __snake_case				:			str            =    self._prepare_for_class(inputs_dict.copy()			,  __lowerCAmelCase			,  return_labels=__lowerCAmelCase				)
            # Is there a better way to remove these decoder inputs?
            __snake_case				:			Dict            =    {
                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				:			Dict            =    {
                "labels",
                "label",
                "label_ids",
                "start_positions",
                "start_position",
                "end_positions",
                "end_position",
                "next_sentence_label",
            }
            __snake_case				:			List[Any]            =    possible_label_cols.intersection(set(__lowerCAmelCase				)				)
            self.assertGreater(len(__lowerCAmelCase				)			,  0			,  msg="No matching label names found!"				)
            __snake_case				:			Optional[int]            =    {key: val for key, val in prepared_for_class.items() if key in label_names}
            __snake_case				:			Tuple            =    {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
            __snake_case				:			int            =    model.fit(
                __lowerCAmelCase			,  validation_data=__lowerCAmelCase			,  steps_per_epoch=1			,  validation_steps=1			,  shuffle=__lowerCAmelCase			,  verbose=0			,  )
            __snake_case				:			List[str]            =    history.history["loss"][0]
            self.assertTrue(not isnan(__lowerCAmelCase				)				)
            __snake_case				:			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.
            __snake_case				:			Any            =    model_class(__lowerCAmelCase				)
            model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)			,  jit_compile=__lowerCAmelCase				)
            __snake_case				:			Optional[Any]            =    model.fit(
                __lowerCAmelCase			,  __lowerCAmelCase			,  validation_data=(inputs_minus_labels, labels)			,  steps_per_epoch=1			,  validation_steps=1			,  shuffle=__lowerCAmelCase			,  verbose=0			,  )

            __snake_case				:			Union[str, Any]            =    history.history["loss"][0]
            self.assertTrue(not isnan(__lowerCAmelCase				)				)
            __snake_case				:			Dict            =    history.history["val_loss"][0]
            self.assertTrue(not isnan(__lowerCAmelCase				)				)







      @slow
      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			Union[str, Any]            =    self.model_tester.prepare_config_and_inputs_for_common()
        __snake_case				:			Tuple            =    True
        __snake_case				:			List[Any]            =    True

        if hasattr(__lowerCAmelCase			,  "use_cache"				):
          __snake_case				:			Tuple            =    True

        __snake_case				:			int            =    getattr(self.model_tester			,  "encoder_seq_length"			,  self.model_tester.seq_length				)
        __snake_case				:			List[Any]            =    getattr(self.model_tester			,  "key_length"			,  __lowerCAmelCase				)

        for model_class in self.all_model_classes[:2]:
          __snake_case				:			Any            =    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase				)
          __snake_case				:			List[Any]            =    model_class(__lowerCAmelCase				)
          model.build()
          __snake_case				:			Tuple            =    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:
              __snake_case				:			Tuple            =    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				)
            __snake_case				:			str            =    os.path.join(__lowerCAmelCase			,  "saved_model"			,  "1"				)
            __snake_case				:			Tuple            =    tf.keras.models.load_model(__lowerCAmelCase				)
            __snake_case				:			str            =    model(__lowerCAmelCase				)

            if self.is_encoder_decoder:
              __snake_case				:			Dict            =    outputs["encoder_hidden_states"]
              __snake_case				:			Optional[int]            =    outputs["encoder_attentions"]
            else:
              __snake_case				:			str            =    outputs["hidden_states"]
              __snake_case				:			str            =    outputs["attentions"]

            self.assertEqual(len(__lowerCAmelCase				)			,  __lowerCAmelCase				)

            __snake_case				:			Union[str, 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				)    ->				List[str]:






        '''simple docstring'''






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

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

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







      @slow
      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			Any            =    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				:			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
            __snake_case				:			Any            =    TFSharedEmbeddings(self.model_tester.vocab_size			,  self.model_tester.hidden_size			,  name="shared"				)
            __snake_case				:			Optional[Any]            =    False
            __snake_case				:			Optional[int]            =    main_layer_class(__lowerCAmelCase			,  embed_tokens=__lowerCAmelCase				)
          else:
            __snake_case				:			List[Any]            =    main_layer_class(__lowerCAmelCase				)

          __snake_case				:			Optional[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"				):
            __snake_case				:			Optional[Any]            =    self.model_tester.num_labels
          else:
            __snake_case				:			Union[str, Any]            =    2

          __snake_case				:			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				)

          __snake_case				:			Optional[Any]            =    main_layer(__lowerCAmelCase				)[0]
          __snake_case				:			Tuple            =    tf.keras.layers.Dense(__lowerCAmelCase			,  activation="softmax"			,  name="outputs"				)(__lowerCAmelCase				)
          __snake_case				:			Any            =    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:
            __snake_case				:			str            =    os.path.join(__lowerCAmelCase			,  "keras_model.h5"				)
            model.save(__lowerCAmelCase				)
            if "T5" in main_layer_class.__name__:
              __snake_case				:			Optional[Any]            =    tf.keras.models.load_model(
                  __lowerCAmelCase			,  custom_objects={
                      main_layer_class.__name__: main_layer_class,
                      "TFSharedEmbeddings": TFSharedEmbeddings,
                  }			,  )
            else:
              __snake_case				:			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[Any]:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			str            =    self.model_tester.prepare_config_and_inputs_for_common()

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

          __snake_case				:			Union[str, Any]            =    copy.deepcopy(__lowerCAmelCase				)

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

          if not self.is_encoder_decoder:
            __snake_case				:			List[Any]            =    model.get_input_embeddings()(__lowerCAmelCase				)
          else:
            __snake_case				:			List[str]            =    model.get_input_embeddings()(__lowerCAmelCase				)
            __snake_case				:			Any            =    model.get_input_embeddings()(__lowerCAmelCase				)

          __snake_case				:			str            =    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase				)

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

          __snake_case				:			Tuple            =    run_in_graph_mode()
          self.assertIsNotNone(__lowerCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			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
        __snake_case				:			str            =    []
        while len(__lowerCAmelCase				) < num_bad_tokens:
          __snake_case				:			Optional[Any]            =    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			,  UpperCAmelCase				)    ->				Optional[Any]:






        '''simple docstring'''






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







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				List[Any]:






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








def  lowerCAmelCase__(		lowercase    :      int = 10**6			)	->      int:

  if n <= 0:
    raise ValueError("Please enter an integer greater than 0"			)

  __snake_case				:			List[Any]            =    list(range(n + 1			)			)
  for number in range(2				,       n + 1			):
    if phi[number] == number:
      phi[number] -= 1
      for multiple in range(number * 2				,       n + 1				,       lowercase			):
        __snake_case				:			Optional[int]            =    (phi[multiple] // number) * (number - 1)

  __snake_case				:			List[Any]            =    1
  for number in range(1				,       n + 1			):
    if (answer / phi[answer]) < (number / phi[number]):
      __snake_case				:			List[str]            =    number

  return answer


if __name__ == "__main__":
     print(solution())

