

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'''







   lowerCamelCase__          =None
   lowerCamelCase__          =()
   lowerCamelCase__          =()
   lowerCamelCase__          =True
   lowerCamelCase__          =True
   lowerCamelCase__          =True
   lowerCamelCase__          =False
   def 							__UpperCamelCase	(     self							:	Tuple , a							:	Optional[Any] , a							:	List[str] , a							:	List[Any]=False		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Dict			       =    copy.deepcopy(a		)

    if model_class in get_values(a		):
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    {
         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				: int			       =    tf.ones(self.model_tester.batch_size , dtype=tf.intaa		)
     elif model_class in get_values(a		):
      SCREAMING_SNAKE_CASE				: Tuple			       =    tf.zeros(self.model_tester.batch_size , dtype=tf.intaa		)
      SCREAMING_SNAKE_CASE				: Tuple			       =    tf.zeros(self.model_tester.batch_size , dtype=tf.intaa		)
     elif model_class in [
         *get_values(a		),
         *get_values(a		),
     ]:
      SCREAMING_SNAKE_CASE				: int			       =    tf.zeros(self.model_tester.batch_size , dtype=tf.intaa		)
     elif model_class in get_values(a		):
      SCREAMING_SNAKE_CASE				: Tuple			       =    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				: Dict			       =    tf.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length) , dtype=tf.intaa		)
    return inputs_dict
   @slow
   def 							__UpperCamelCase	(     self							:	int		)							->    Dict:

    """simple docstring"""

    SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Any			       =    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self._prepare_for_class(a , a		)
     SCREAMING_SNAKE_CASE				: str			       =    model_class(a		)

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

     SCREAMING_SNAKE_CASE				: Any			       =    run_in_graph_mode()
     self.assertIsNotNone(a		)
   @slow
   def 							__UpperCamelCase	(     self							:	Tuple		)							->    List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
     SCREAMING_SNAKE_CASE				: List[str]			       =    self._prepare_for_class(a , a		)
     SCREAMING_SNAKE_CASE				: Dict			       =    model_class(a		)

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

     SCREAMING_SNAKE_CASE				: Dict			       =    run_in_graph_mode()
     self.assertIsNotNone(a		)
   @slow
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Union[str, Any]:

    """simple docstring"""

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

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

      SCREAMING_SNAKE_CASE				: int			       =    history.history["loss"][0]
      self.assertTrue(not isnan(a		)		)
      SCREAMING_SNAKE_CASE				: Tuple			       =    history.history["val_loss"][0]
      self.assertTrue(not isnan(a		)		)
   @slow
   def 							__UpperCamelCase	(     self							:	str		)							->    Tuple:

    """simple docstring"""

    SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE				: str			       =    True
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    True

    if hasattr(a , "use_cache"		):
     SCREAMING_SNAKE_CASE				: Tuple			       =    True

    SCREAMING_SNAKE_CASE				: int			       =    getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length		)
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    getattr(self.model_tester , "key_length" , a		)

    for model_class in self.all_model_classes[:2]:
     SCREAMING_SNAKE_CASE				: int			       =    self._prepare_for_class(a , a		)
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model_class(a		)
     model.build()
     SCREAMING_SNAKE_CASE				: int			       =    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				: int			       =    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				: Union[str, Any]			       =    os.path.join(a , "saved_model" , "1"		)
      SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    tf.keras.models.load_model(a		)
      SCREAMING_SNAKE_CASE				: Optional[int]			       =    model(a		)

      if self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE				: Dict			       =    outputs["encoder_hidden_states"]
       SCREAMING_SNAKE_CASE				: List[Any]			       =    outputs["encoder_attentions"]
      else:
       SCREAMING_SNAKE_CASE				: Optional[int]			       =    outputs["hidden_states"]
       SCREAMING_SNAKE_CASE				: Tuple			       =    outputs["attentions"]

      self.assertEqual(len(a		) , a		)

      SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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							:	Dict		)							->    List[str]:

    """simple docstring"""

    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				: Union[str, Any]			       =    self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
      SCREAMING_SNAKE_CASE				: List[Any]			       =    self._prepare_for_class(a , a		)
      SCREAMING_SNAKE_CASE				: List[Any]			       =    model_class(a		)
      SCREAMING_SNAKE_CASE				: List[str]			       =    model(a		)

      self.assertIsNotNone(a		)
    finally:
     tf.keras.mixed_precision.set_global_policy("float32"		)
   @slow
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Dict			       =    self.model_tester.prepare_config_and_inputs_for_common()
    # head_mask and decoder_head_mask has different shapes than other input args
    if "head_mask" in inputs_dict:
     del inputs_dict["head_mask"]
    if "decoder_head_mask" in inputs_dict:
     del inputs_dict["decoder_head_mask"]
    if "cross_attn_head_mask" in inputs_dict:
     del inputs_dict["cross_attn_head_mask"]
    SCREAMING_SNAKE_CASE				: Any			       =    {
        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				: str			       =    TFSharedEmbeddings(self.model_tester.vocab_size , self.model_tester.hidden_size , name="shared"		)
      SCREAMING_SNAKE_CASE				: str			       =    False
      SCREAMING_SNAKE_CASE				: Tuple			       =    main_layer_class(a , embed_tokens=a		)
     else:
      SCREAMING_SNAKE_CASE				: List[Any]			       =    main_layer_class(a		)

     SCREAMING_SNAKE_CASE				: Dict			       =    {
         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				: str			       =    self.model_tester.num_labels
     else:
      SCREAMING_SNAKE_CASE				: Tuple			       =    2

     SCREAMING_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		)

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

    """simple docstring"""

    SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes[:2]:
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model_class(a		)

     SCREAMING_SNAKE_CASE				: Any			       =    copy.deepcopy(a		)

     if not self.is_encoder_decoder:
      SCREAMING_SNAKE_CASE				: List[str]			       =    inputs["input_ids"]
      del inputs["input_ids"]
     else:
      SCREAMING_SNAKE_CASE				: Tuple			       =    inputs["input_ids"]
      SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    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				: Dict			       =    model.get_input_embeddings()(a		)
     else:
      SCREAMING_SNAKE_CASE				: str			       =    model.get_input_embeddings()(a		)
      SCREAMING_SNAKE_CASE				: Any			       =    model.get_input_embeddings()(a		)

     SCREAMING_SNAKE_CASE				: Dict			       =    self._prepare_for_class(a , a		)

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

     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    run_in_graph_mode()
     self.assertIsNotNone(a		)
   def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	Dict , a							:	Optional[Any]		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: 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
    SCREAMING_SNAKE_CASE				: str			       =    []
    while len(a		) < num_bad_tokens:
     SCREAMING_SNAKE_CASE				: List[str]			       =    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							:	int , a							:	List[str]		)							->    Union[str, Any]:

    """simple docstring"""

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






   def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Dict , a							:	Tuple		)							->    int:

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


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)