



import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							List[str]=13				, __lowerCAmelCase      :							Dict=64				, __lowerCAmelCase      :							List[str]=4				, __lowerCAmelCase      :							int=3				, __lowerCAmelCase      :							Dict=16				, __lowerCAmelCase      :							Any=[1, 2, 1]				, __lowerCAmelCase      :							Optional[Any]=[2, 4, 8]				, __lowerCAmelCase      :							str=3				, __lowerCAmelCase      :							Any=2.0				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Optional[Any]=0.0				, __lowerCAmelCase      :							List[Any]=0.0				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Any="gelu"				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Union[str, Any]=0.0_2				, __lowerCAmelCase      :							Any=1E-5				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Optional[int]=None				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							Optional[int]=10				, __lowerCAmelCase      :							List[str]=["stage1", "stage2"]				, __lowerCAmelCase      :							List[str]=[1, 2]				, )							->					Any:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		image_size
    a							=		patch_size
    a							=		num_channels
    a							=		embed_dim
    a							=		depths
    a							=		num_heads
    a							=		kernel_size
    a							=		mlp_ratio
    a							=		qkv_bias
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		drop_path_rate
    a							=		hidden_act
    a							=		patch_norm
    a							=		layer_norm_eps
    a							=		initializer_range
    a							=		is_training
    a							=		scope
    a							=		use_labels
    a							=		num_labels
    a							=		out_features
    a							=		out_indices

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    a							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

    a							=		None
    if self.use_labels:
     a							=		ids_tensor([self.batch_size]				, self.num_labels				)

    a							=		self.get_config()

    return config, pixel_values, labels

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return NatConfig(
        num_labels=self.num_labels				, image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, embed_dim=self.embed_dim				, depths=self.depths				, num_heads=self.num_heads				, kernel_size=self.kernel_size				, mlp_ratio=self.mlp_ratio				, qkv_bias=self.qkv_bias				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, drop_path_rate=self.drop_path_rate				, hidden_act=self.hidden_act				, patch_norm=self.patch_norm				, layer_norm_eps=self.layer_norm_eps				, initializer_range=self.initializer_range				, out_features=self.out_features				, out_indices=self.out_indices				, )

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


    """simple docstring"""






    a							=		NatModel(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    a							=		a							=		(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
    a							=		int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

    self.parent.assertEqual(
        result.last_hidden_state.shape				, (self.batch_size, expected_height, expected_width, expected_dim)				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Any				)							->					Optional[int]:


    """simple docstring"""






    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				, labels=__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

    # test greyscale images
    a							=		1
    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

    a							=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    a							=		model(__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

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


    """simple docstring"""






    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps				)				, len(config.out_features				)				)
    self.parent.assertListEqual(list(result.feature_maps[0].shape				)				, [self.batch_size, model.channels[0], 16, 16]				)

    # verify channels
    self.parent.assertEqual(len(model.channels				)				, len(config.out_features				)				)

    # verify backbone works with out_features=None
    a							=		None
    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps				)				, 1				)
    self.parent.assertListEqual(list(result.feature_maps[0].shape				)				, [self.batch_size, model.channels[-1], 4, 4]				)

    # verify channels
    self.parent.assertEqual(len(model.channels				)				, 1				)




   def 		A				(   self      :							int				)							->					int:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    a		,	a		,	a							=		config_and_inputs
    a							=		{"pixel_values": pixel_values}
    return config, inputs_dict



@require_natten
@require_torch
class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase  						=      (
       {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase  						=      False

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    a							=		NatModelTester(self				)
    a							=		ConfigTester(self				, config_class=__lowerCAmelCase				, embed_dim=37				)

   def 		A				(   self      :							Optional[Any]				)							->					Dict:


    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

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


    """simple docstring"""






    return

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase				)

   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*__lowerCAmelCase				)

   @unittest.skip(reason="Nat does not use inputs_embeds"				)
   def 		A				(   self      :							int				)							->					Optional[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="Nat does not use feedforward chunking"				)
   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    pass

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


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a							=		model_class(__lowerCAmelCase				)
     self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)				)
     a							=		model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(__lowerCAmelCase				, nn.Linear				)				)

   def 		A				(   self      :							Any				)							->					Tuple:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a							=		model_class(__lowerCAmelCase				)
     a							=		inspect.signature(model.forward				)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     a							=		[*signature.parameters.keys()]

     a							=		["pixel_values"]
     self.assertListEqual(arg_names[:1]				, __lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Optional[int]:


    """simple docstring"""






    self.skipTest("Nat's attention operation is handled entirely by NATTEN."				)

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


    """simple docstring"""






    a							=		model_class(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

    with torch.no_grad():
     a							=		model(**self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)				)

    a							=		outputs.hidden_states

    a							=		getattr(
        self.model_tester				, "expected_num_hidden_layers"				, len(self.model_tester.depths				) + 1				)
    self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

    # Nat has a different seq_length
    a							=		(
        config.patch_size
        if isinstance(config.patch_size				, collections.abc.Iterable				)
        else (config.patch_size, config.patch_size)
    )

    a							=		image_size[0] // patch_size[0]
    a							=		image_size[1] // patch_size[1]

    self.assertListEqual(
        list(hidden_states[0].shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

    if model_class.__name__ != "NatBackbone":
     a							=		outputs.reshaped_hidden_states
     self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

     a		,	a		,	a		,	a							=		reshaped_hidden_states[0].shape
     a							=		(
         reshaped_hidden_states[0].view(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				).permute(0				, 2				, 3				, 1				)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size				, collections.abc.Iterable				)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
     a							=		True
     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     a							=		True

     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

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


    """simple docstring"""






    for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a							=		NatModel.from_pretrained(__lowerCAmelCase				)
     self.assertIsNotNone(__lowerCAmelCase				)




   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		_config_zero_init(__lowerCAmelCase				)
    for model_class in self.all_model_classes:
     a							=		model_class(config=__lowerCAmelCase				)
     for name, param in model.named_parameters():
      if "embeddings" not in name and param.requires_grad:
       self.assertIn(
           ((param.data.mean() * 1E9).round() / 1E9).item()				, [0.0, 1.0]				, msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""				, )



@require_natten
@require_vision
@require_torch
class 		_lowercase		(							unittest.TestCase						):

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


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"				) if is_vision_available() else None




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


    """simple docstring"""






    a							=		NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"				).to(__lowerCAmelCase				)
    a							=		self.default_image_processor

    a							=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    a							=		image_processor(images=__lowerCAmelCase				, return_tensors="pt"				).to(__lowerCAmelCase				)

    # forward pass
    with torch.no_grad():
     a							=		model(**__lowerCAmelCase				)

    # verify the logits
    a							=		torch.Size((1, 1000)				)
    self.assertEqual(outputs.logits.shape				, __lowerCAmelCase				)
    a							=		torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]				).to(__lowerCAmelCase				)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]				, __lowerCAmelCase				, atol=1E-4				)				)



@require_torch
@require_natten
class 		_lowercase		(							unittest.TestCase, UpperCAmelCase__						):
   _UpperCAmelCase  						=      (NatBackbone,) if is_torch_available() else ()
   _UpperCAmelCase  						=      NatConfig

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		NatModelTester(self				)






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

