

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__										=      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__										=      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  UpperCamelCase_      :


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Dict					    =   None
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   ()
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   ()
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   True
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   True
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   True
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   False



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					Any  ,	A_     :					str  ,	A_     :					List[str]=False						)       ->			dict:
      SCREAMING_SNAKE_CASE__         =    copy.deepcopy(A_						)

      if model_class in get_values(A_						):
          SCREAMING_SNAKE_CASE__         =    {
              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_						):
              SCREAMING_SNAKE_CASE__         =    tf.ones(self.model_tester.batch_size  ,	dtype=tf.intaa						)
          elif model_class in get_values(A_						):
              SCREAMING_SNAKE_CASE__         =    tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa						)
              SCREAMING_SNAKE_CASE__         =    tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa						)
          elif model_class in [
              *get_values(A_						),
              *get_values(A_						),
          ]:
              SCREAMING_SNAKE_CASE__         =    tf.zeros(self.model_tester.batch_size  ,	dtype=tf.intaa						)
          elif model_class in get_values(A_						):
              SCREAMING_SNAKE_CASE__         =    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_						),
          ]:
              SCREAMING_SNAKE_CASE__         =    tf.zeros(
                  (self.model_tester.batch_size, self.model_tester.seq_length)  ,	dtype=tf.intaa						)
      return inputs_dict



  @slow
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
          SCREAMING_SNAKE_CASE__         =    self._prepare_for_class(A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    model_class(A_						)

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

          SCREAMING_SNAKE_CASE__         =    run_in_graph_mode()
          self.assertIsNotNone(A_						)



  @slow
  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			int:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
          SCREAMING_SNAKE_CASE__         =    self._prepare_for_class(A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    model_class(A_						)

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

          SCREAMING_SNAKE_CASE__         =    run_in_graph_mode()
          self.assertIsNotNone(A_						)



  @slow
  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			List[str]:
      # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
          SCREAMING_SNAKE_CASE__         =    model_class(A_						)
          if getattr(A_  ,	"hf_compute_loss"  ,	A_						):
              # Test that model correctly compute the loss with kwargs
              SCREAMING_SNAKE_CASE__         =    self._prepare_for_class(inputs_dict.copy()  ,	A_  ,	return_labels=A_						)
              # Is there a better way to remove these decoder inputs?
              SCREAMING_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")
              }

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

              SCREAMING_SNAKE_CASE__         =    history.history["loss"][0]
              self.assertTrue(not isnan(A_						)						)
              SCREAMING_SNAKE_CASE__         =    history.history["val_loss"][0]
              self.assertTrue(not isnan(A_						)						)



  @slow
  def   _UpperCamelCase     (						self     :					int						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE__         =    True
      SCREAMING_SNAKE_CASE__         =    True

      if hasattr(A_  ,	"use_cache"						):
          SCREAMING_SNAKE_CASE__         =    True

      SCREAMING_SNAKE_CASE__         =    getattr(self.model_tester  ,	"encoder_seq_length"  ,	self.model_tester.seq_length						)
      SCREAMING_SNAKE_CASE__         =    getattr(self.model_tester  ,	"key_length"  ,	A_						)

      for model_class in self.all_model_classes[:2]:
          SCREAMING_SNAKE_CASE__         =    self._prepare_for_class(A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    model_class(A_						)
          model.build()
          SCREAMING_SNAKE_CASE__         =    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:
                  SCREAMING_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(A_  ,	saved_model=A_						)
              SCREAMING_SNAKE_CASE__         =    os.path.join(A_  ,	"saved_model"  ,	"1"						)
              SCREAMING_SNAKE_CASE__         =    tf.keras.models.load_model(A_						)
              SCREAMING_SNAKE_CASE__         =    model(A_						)

              if self.is_encoder_decoder:
                  SCREAMING_SNAKE_CASE__         =    outputs["encoder_hidden_states"]
                  SCREAMING_SNAKE_CASE__         =    outputs["encoder_attentions"]
              else:
                  SCREAMING_SNAKE_CASE__         =    outputs["hidden_states"]
                  SCREAMING_SNAKE_CASE__         =    outputs["attentions"]

              self.assertEqual(len(A_						)  ,	A_						)

              SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					List[Any]						)       ->			str:
      tf.keras.mixed_precision.set_global_policy("mixed_float16"						)

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

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



  @slow
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_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"]
      SCREAMING_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(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
              SCREAMING_SNAKE_CASE__         =    TFSharedEmbeddings(self.model_tester.vocab_size  ,	self.model_tester.hidden_size  ,	name="shared"						)
              SCREAMING_SNAKE_CASE__         =    False
              SCREAMING_SNAKE_CASE__         =    main_layer_class(A_  ,	embed_tokens=A_						)
          else:
              SCREAMING_SNAKE_CASE__         =    main_layer_class(A_						)

          SCREAMING_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"						):
              SCREAMING_SNAKE_CASE__         =    self.model_tester.num_labels
          else:
              SCREAMING_SNAKE_CASE__         =    2

          SCREAMING_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						)

          SCREAMING_SNAKE_CASE__         =    main_layer(A_						)[0]
          SCREAMING_SNAKE_CASE__         =    tf.keras.layers.Dense(A_  ,	activation="softmax"  ,	name="outputs"						)(A_						)
          SCREAMING_SNAKE_CASE__         =    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:
              SCREAMING_SNAKE_CASE__         =    os.path.join(A_  ,	"keras_model.h5"						)
              model.save(A_						)
              if "T5" in main_layer_class.__name__:
                  SCREAMING_SNAKE_CASE__         =    tf.keras.models.load_model(
                      A_  ,	custom_objects={
                          main_layer_class.__name__: main_layer_class,
                          "TFSharedEmbeddings": TFSharedEmbeddings,
                      }  ,	)
              else:
                  SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					Tuple						)       ->			int:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:2]:
          SCREAMING_SNAKE_CASE__         =    model_class(A_						)

          SCREAMING_SNAKE_CASE__         =    copy.deepcopy(A_						)

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

          if not self.is_encoder_decoder:
              SCREAMING_SNAKE_CASE__         =    model.get_input_embeddings()(A_						)
          else:
              SCREAMING_SNAKE_CASE__         =    model.get_input_embeddings()(A_						)
              SCREAMING_SNAKE_CASE__         =    model.get_input_embeddings()(A_						)

          SCREAMING_SNAKE_CASE__         =    self._prepare_for_class(A_  ,	A_						)

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

          SCREAMING_SNAKE_CASE__         =    run_in_graph_mode()
          self.assertIsNotNone(A_						)



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					List[str]  ,	A_     :					Dict						)       ->			Optional[Any]:
      # special tokens cannot be bad tokens
      SCREAMING_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
      SCREAMING_SNAKE_CASE__         =    []
      while len(A_						) < num_bad_tokens:
          SCREAMING_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(A_						)
      return bad_tokens



  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	A_     :					Any						)       ->			List[str]:
      for token_id in output_ids[0].numpy().tolist():
          self.assertGreaterEqual(A_  ,	0						)
          self.assertLess(A_  ,	self.model_tester.vocab_size						)







  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					List[str]  ,	A_     :					int						)       ->			Any:
      # 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(A_						)  ,	len(A_						)						):
                  # if tokens match
                  if generated_ids_slice[i - len(A_						) : i] == bad_word_ids:
                      return True
      return False





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Any  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    key
      SCREAMING_SNAKE_CASE__         =    freq







  def __str__(						self     :					Union[str, Any]						)       ->			List[str]:
      return f"Node(key={self.key}, freq={self.freq})"





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A						):
    if i > j or i < 0 or j > len(__A						) - 1:
        return

    SCREAMING_SNAKE_CASE__         =    root[i][j]
    if parent == -1:  # root does not have a parent
        print(f"{key[node]} is the root of the binary search tree."						)
    elif is_left:
        print(f"{key[node]} is the left child of key {parent}."						)
    else:
        print(f"{key[node]} is the right child of key {parent}."						)

    print_binary_search_tree(__A			,							__A			,							__A			,							node - 1			,							key[node]			,							__A						)
    print_binary_search_tree(__A			,							__A			,							node + 1			,							__A			,							key[node]			,							__A						)





def 				__a							(       __A						):
    nodes.sort(key=lambda __A						: node.key						)

    SCREAMING_SNAKE_CASE__         =    len(__A						)

    SCREAMING_SNAKE_CASE__         =    [nodes[i].key for i in range(__A						)]
    SCREAMING_SNAKE_CASE__         =    [nodes[i].freq for i in range(__A						)]

    # This 2D array stores the overall tree cost (which's as minimized as possible);
    # for a single key, cost is equal to frequency of the key.
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
    # array
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # stores tree roots that will be used later for constructing binary search tree
    SCREAMING_SNAKE_CASE__         =    [[i if i == j else 0 for j in range(__A						)] for i in range(__A						)]

    for interval_length in range(2			,							n + 1						):
        for i in range(n - interval_length + 1						):
            SCREAMING_SNAKE_CASE__         =    i + interval_length - 1

            SCREAMING_SNAKE_CASE__         =    sys.maxsize  # set the value to "infinity"
            SCREAMING_SNAKE_CASE__         =    total[i][j - 1] + freqs[j]

            # Apply Knuth's optimization
            # Loop without optimization: for r in range(i, j + 1):
            for r in range(root[i][j - 1]			,							root[i + 1][j] + 1						):  # r is a temporal root
                SCREAMING_SNAKE_CASE__         =    dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                SCREAMING_SNAKE_CASE__         =    dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                SCREAMING_SNAKE_CASE__         =    left + total[i][j] + right

                if dp[i][j] > cost:
                    SCREAMING_SNAKE_CASE__         =    cost
                    SCREAMING_SNAKE_CASE__         =    r

    print("Binary search tree nodes:"						)
    for node in nodes:
        print(__A						)

    print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."						)
    print_binary_search_tree(__A			,							__A			,							0			,							n - 1			,							-1			,							__A						)





def 				__a							(       ):
    # A sample binary search tree
    SCREAMING_SNAKE_CASE__         =    [Node(__A			,							randint(1			,							50						)						) for i in range(10			,							0			,							-1						)]
    find_optimal_binary_search_tree(__A						)


if __name__ == "__main__":
  main()


