
"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


__UpperCAmelCase 					=    logging.get_logger(__name__)




class      _SCREAMING_SNAKE_CASE   (     UpperCAmelCase__				):






			def __init__(   self			,  *__A			,  **__A			)     ->	None:
						warnings.warn(
						    """The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
						    """ Please use MaskFormerImageProcessor instead."""			,  __lowerCAmelCase			,  )
						super().__init__(*__lowerCAmelCase			,  **__lowerCAmelCase			)











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_		:  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)]
                    )
                    A_		:  List[Any]										=  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 		_lowercase		:
   _UpperCAmelCase  						=      None
   _UpperCAmelCase  						=      ()
   _UpperCAmelCase  						=      ()
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Union[str, Any]=False				)							->					dict:


    """simple docstring"""






    a							=		copy.deepcopy(__lowerCAmelCase				)

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

   @slow
   def 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






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

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

     a							=		run_in_graph_mode()
     self.assertIsNotNone(__lowerCAmelCase				)

   @slow
   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






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

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

     a							=		run_in_graph_mode()
     self.assertIsNotNone(__lowerCAmelCase				)

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


    """simple docstring"""






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

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

      a							=		history.history["loss"][0]
      self.assertTrue(not isnan(__lowerCAmelCase				)				)
      a							=		history.history["val_loss"][0]
      self.assertTrue(not isnan(__lowerCAmelCase				)				)

   @slow
   def 		A				(   self      :							int				)							->					List[str]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()
    a							=		True
    a							=		True

    if hasattr(__lowerCAmelCase				, "use_cache"				):
     a							=		True

    a							=		getattr(self.model_tester				, "encoder_seq_length"				, self.model_tester.seq_length				)
    a							=		getattr(self.model_tester				, "key_length"				, __lowerCAmelCase				)

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

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

      self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

      a							=		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 		A				(   self      :							Optional[int]				)							->					Tuple:


    """simple docstring"""






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

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

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

   @slow
   def 		A				(   self      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    a		,	a							=		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"]
    a							=		{
        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
      a							=		TFSharedEmbeddings(self.model_tester.vocab_size				, self.model_tester.hidden_size				, name="shared"				)
      a							=		False
      a							=		main_layer_class(__lowerCAmelCase				, embed_tokens=__lowerCAmelCase				)
     else:
      a							=		main_layer_class(__lowerCAmelCase				)

     a							=		{
         name: tf.keras.Input(tensor.shape[1:]				, dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
     }

     if hasattr(self.model_tester				, "num_labels"				):
      a							=		self.model_tester.num_labels
     else:
      a							=		2

     a							=		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				)

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


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

     a							=		copy.deepcopy(__lowerCAmelCase				)

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

     if not self.is_encoder_decoder:
      a							=		model.get_input_embeddings()(__lowerCAmelCase				)
     else:
      a							=		model.get_input_embeddings()(__lowerCAmelCase				)
      a							=		model.get_input_embeddings()(__lowerCAmelCase				)

     a							=		self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)

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

     a							=		run_in_graph_mode()
     self.assertIsNotNone(__lowerCAmelCase				)

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[str]				)							->					List[str]:


    """simple docstring"""






    a							=		[]
    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
    a							=		[]
    while len(__lowerCAmelCase				) < num_bad_tokens:
     a							=		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 		A				(   self      :							int				, __lowerCAmelCase      :							int				)							->					int:


    """simple docstring"""






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




   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Any				)							->					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(__lowerCAmelCase				)				, len(__lowerCAmelCase				)				):
       # if tokens match
       if generated_ids_slice[i - len(__lowerCAmelCase				) : i] == bad_word_ids:
        return True
    return False

