'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 							lowercase_    (     SCREAMING_SNAKE_CASE__ ,  unittest.TestCase						):
    a_             =   PaintByExamplePipeline
    a_             =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
    a_             =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    a_             =   frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







    def   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

     torch.manual_seed(0				)
     UpperCAmelCase_				       =					UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)       ,     layers_per_block=2       ,     sample_size=3_2       ,     in_channels=9       ,     out_channels=4       ,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,     cross_attention_dim=3_2       ,     )
     UpperCAmelCase_				       =					PNDMScheduler(skip_prk_steps=UpperCamelCase__				)
     torch.manual_seed(0				)
     UpperCAmelCase_				       =					AutoencoderKL(
         block_out_channels=[3_2, 6_4]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,     latent_channels=4       ,     )
     torch.manual_seed(0				)
     UpperCAmelCase_				       =					CLIPVisionConfig(
         hidden_size=3_2       ,     projection_dim=3_2       ,     intermediate_size=3_7       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     image_size=3_2       ,     patch_size=4       ,     )
     UpperCAmelCase_				       =					PaintByExampleImageEncoder(UpperCamelCase__       ,     proj_size=3_2				)
     UpperCAmelCase_				       =					CLIPImageProcessor(crop_size=3_2       ,     size=3_2				)

     UpperCAmelCase_				       =					{
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "image_encoder": image_encoder,
         "safety_checker": None,
         "feature_extractor": feature_extractor,
     }
     return components







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Dict:





     """simple docstring"""

     UpperCAmelCase_				       =					np.array(image.convert("RGB"				)				)
     UpperCAmelCase_				       =					image[None].transpose(0       ,     3       ,     1       ,     2				)
     UpperCAmelCase_				       =					torch.from_numpy(UpperCamelCase__				).to(dtype=torch.floataa				) / 1_2_7.5 - 1.0
     return image







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__="cpu"       ,     UpperCamelCase__=0				)				->       Optional[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					floats_tensor((1, 3, 3_2, 3_2)       ,     rng=random.Random(UpperCamelCase__				)				).to(UpperCamelCase__				)
     UpperCAmelCase_				       =					image.cpu().permute(0       ,     2       ,     3       ,     1				)[0]
     UpperCAmelCase_				       =					Image.fromarray(np.uinta(UpperCamelCase__				)				).convert("RGB"				).resize((6_4, 6_4)				)
     UpperCAmelCase_				       =					Image.fromarray(np.uinta(image + 4				)				).convert("RGB"				).resize((6_4, 6_4)				)
     UpperCAmelCase_				       =					Image.fromarray(np.uinta(UpperCamelCase__				)				).convert("RGB"				).resize((3_2, 3_2)				)

     if str(UpperCamelCase__				).startswith("mps"				):
      UpperCAmelCase_				       =					torch.manual_seed(UpperCamelCase__				)
     else:
      UpperCAmelCase_				       =					torch.Generator(device=UpperCamelCase__				).manual_seed(UpperCamelCase__				)
     UpperCAmelCase_				       =					{
         "example_image": example_image,
         "image": init_image,
         "mask_image": mask_image,
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }
     return inputs







    def   lowerCamelCase_    (	self				)				->       Tuple:





     """simple docstring"""

     UpperCAmelCase_				       =					self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     UpperCAmelCase_				       =					PaintByExamplePipeline(**UpperCamelCase__				)
     UpperCAmelCase_				       =					pipe.to("cpu"				)
     pipe.set_progress_bar_config(disable=UpperCamelCase__				)

     UpperCAmelCase_				       =					self.get_dummy_inputs()
     UpperCAmelCase_				       =					pipe(**UpperCamelCase__				)
     UpperCAmelCase_				       =					output.images

     UpperCAmelCase_				       =					image[0, -3:, -3:, -1]

     assert image.shape == (1, 6_4, 6_4, 3)
     UpperCAmelCase_				       =					np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]				)

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2







    def   lowerCamelCase_    (	self				)				->       Optional[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					"cpu"
     UpperCAmelCase_				       =					self.get_dummy_inputs()
     inputs.pop("mask_image"				)
     UpperCAmelCase_				       =					self.convert_to_pt(inputs.pop("image"				)				)
     UpperCAmelCase_				       =					image.clamp(0       ,     1				) / 2

     # make sure here that pndm scheduler skips prk
     UpperCAmelCase_				       =					PaintByExamplePipeline(**self.get_dummy_components()				)
     UpperCAmelCase_				       =					pipe.to(UpperCamelCase__				)
     pipe.set_progress_bar_config(disable=UpperCamelCase__				)

     UpperCAmelCase_				       =					pipe(image=UpperCamelCase__       ,     mask_image=mask_image[:, 0]       ,     **UpperCamelCase__				)
     UpperCAmelCase_				       =					output.images

     UpperCAmelCase_				       =					image.cpu().permute(0       ,     2       ,     3       ,     1				)[0]
     UpperCAmelCase_				       =					mask_image.cpu().permute(0       ,     2       ,     3       ,     1				)[0]

     UpperCAmelCase_				       =					Image.fromarray(np.uinta(UpperCamelCase__				)				).convert("RGB"				)
     UpperCAmelCase_				       =					Image.fromarray(np.uinta(UpperCamelCase__				)				).convert("RGB"				)

     UpperCAmelCase_				       =					pipe(**self.get_dummy_inputs()				)
     UpperCAmelCase_				       =					output.images

     assert out_a.shape == (1, 6_4, 6_4, 3)
     assert np.abs(out_a.flatten() - out_a.flatten()				).max() < 5e-2







    def   lowerCamelCase_    (	self				)				->       List[Any]:





     """simple docstring"""

     super().test_inference_batch_single_identical(expected_max_diff=3e-3				)


@slow
@require_torch_gpu
class 							lowercase_    (     unittest.TestCase						):







    def   lowerCamelCase_    (	self				)				->       List[Any]:





     """simple docstring"""

     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







    def   lowerCamelCase_    (	self				)				->       List[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/paint_by_example/dog_in_bucket.png"				)
     UpperCAmelCase_				       =					load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/paint_by_example/mask.png"				)
     UpperCAmelCase_				       =					load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/paint_by_example/panda.jpg"				)

     UpperCAmelCase_				       =					PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"				)
     UpperCAmelCase_				       =					pipe.to(UpperCamelCase__				)
     pipe.set_progress_bar_config(disable=UpperCamelCase__				)

     UpperCAmelCase_				       =					torch.manual_seed(3_2_1				)
     UpperCAmelCase_				       =					pipe(
         image=UpperCamelCase__       ,     mask_image=UpperCamelCase__       ,     example_image=UpperCamelCase__       ,     generator=UpperCamelCase__       ,     guidance_scale=5.0       ,     num_inference_steps=5_0       ,     output_type="np"       ,     )

     UpperCAmelCase_				       =					output.images

     UpperCAmelCase_				       =					image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase_				       =					np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]				)

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2

'''simple docstring'''

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            __snake_case       :   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)]
                        )
                        __snake_case       :   Optional[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_    :
    a_             =   None
    a_             =   ()
    a_             =   ()
    a_             =   True
    a_             =   True
    a_             =   True
    a_             =   False







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__=False				)				->       dict:





     """simple docstring"""

     UpperCAmelCase_				       =					copy.deepcopy(UpperCamelCase__				)

     if model_class in get_values(UpperCamelCase__				):
      UpperCAmelCase_				       =					{
          k: tf.tile(tf.expand_dims(UpperCamelCase__       ,     1				)       ,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
          if isinstance(UpperCamelCase__       ,     tf.Tensor				) and v.ndim > 0
          else v
          for k, v in inputs_dict.items()
      }

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







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





     """simple docstring"""

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

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

      UpperCAmelCase_				       =					run_in_graph_mode()
      self.assertIsNotNone(UpperCamelCase__				)







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





     """simple docstring"""

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

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

      UpperCAmelCase_				       =					run_in_graph_mode()
      self.assertIsNotNone(UpperCamelCase__				)







    @slow
    def   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

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

       UpperCAmelCase_				       =					{
           "labels",
           "label",
           "label_ids",
           "start_positions",
           "start_position",
           "end_positions",
           "end_position",
           "next_sentence_label",
       }
       UpperCAmelCase_				       =					possible_label_cols.intersection(set(UpperCamelCase__				)				)
       self.assertGreater(len(UpperCamelCase__				)       ,     0       ,     msg="No matching label names found!"				)
       UpperCAmelCase_				       =					{key: val for key, val in prepared_for_class.items() if key in label_names}
       UpperCAmelCase_				       =					{key: val for key, val in prepared_for_class.items() if key not in label_names}
       self.assertGreater(len(UpperCamelCase__				)       ,     0				)

       # Make sure it works with XLA!
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)       ,     jit_compile=UpperCamelCase__				)
       # Make sure the model fits without crashing regardless of where we pass the labels
       UpperCAmelCase_				       =					model.fit(
           UpperCamelCase__       ,     validation_data=UpperCamelCase__       ,     steps_per_epoch=1       ,     validation_steps=1       ,     shuffle=UpperCamelCase__       ,     verbose=0       ,     )
       UpperCAmelCase_				       =					history.history["loss"][0]
       self.assertTrue(not isnan(UpperCamelCase__				)				)
       UpperCAmelCase_				       =					history.history["val_loss"][0]
       self.assertTrue(not isnan(UpperCamelCase__				)				)

       # Now test it with separate labels, to make sure that path works in XLA too.
       UpperCAmelCase_				       =					model_class(UpperCamelCase__				)
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)       ,     jit_compile=UpperCamelCase__				)
       UpperCAmelCase_				       =					model.fit(
           UpperCamelCase__       ,     UpperCamelCase__       ,     validation_data=(inputs_minus_labels, labels)       ,     steps_per_epoch=1       ,     validation_steps=1       ,     shuffle=UpperCamelCase__       ,     verbose=0       ,     )

       UpperCAmelCase_				       =					history.history["loss"][0]
       self.assertTrue(not isnan(UpperCamelCase__				)				)
       UpperCAmelCase_				       =					history.history["val_loss"][0]
       self.assertTrue(not isnan(UpperCamelCase__				)				)







    @slow
    def   lowerCamelCase_    (	self				)				->       Tuple:





     """simple docstring"""

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

     if hasattr(UpperCamelCase__       ,     "use_cache"				):
      UpperCAmelCase_				       =					True

     UpperCAmelCase_				       =					getattr(self.model_tester       ,     "encoder_seq_length"       ,     self.model_tester.seq_length				)
     UpperCAmelCase_				       =					getattr(self.model_tester       ,     "key_length"       ,     UpperCamelCase__				)

     for model_class in self.all_model_classes[:2]:
      UpperCAmelCase_				       =					self._prepare_for_class(UpperCamelCase__       ,     UpperCamelCase__				)
      UpperCAmelCase_				       =					model_class(UpperCamelCase__				)
      model.build()
      UpperCAmelCase_				       =					len(model(UpperCamelCase__				)				)

      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:
        UpperCAmelCase_				       =					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(UpperCamelCase__       ,     saved_model=UpperCamelCase__				)
       UpperCAmelCase_				       =					os.path.join(UpperCamelCase__       ,     "saved_model"       ,     "1"				)
       UpperCAmelCase_				       =					tf.keras.models.load_model(UpperCamelCase__				)
       UpperCAmelCase_				       =					model(UpperCamelCase__				)

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

       self.assertEqual(len(UpperCamelCase__				)       ,     UpperCamelCase__				)

       UpperCAmelCase_				       =					getattr(
           self.model_tester       ,     "expected_num_hidden_layers"       ,     self.model_tester.num_hidden_layers + 1				)

       self.assertEqual(len(UpperCamelCase__				)       ,     UpperCamelCase__				)
       self.assertListEqual(
           list(output_hidden_states[0].shape[-2:]				)       ,     [self.model_tester.seq_length, self.model_tester.hidden_size]       ,     )

       self.assertEqual(len(UpperCamelCase__				)       ,     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   lowerCamelCase_    (	self				)				->       Optional[int]:





     """simple docstring"""

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

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

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







    @slow
    def   lowerCamelCase_    (	self				)				->       Dict:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_				       =					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"]
     UpperCAmelCase_				       =					{
         module_member
         for model_class in self.all_model_classes
         for module in (import_module(model_class.__module__				),)
         for module_member_name in dir(UpperCamelCase__				)
         if module_member_name.endswith("MainLayer"				)
         for module_member in (getattr(UpperCamelCase__       ,     UpperCamelCase__				),)
         if isinstance(UpperCamelCase__       ,     UpperCamelCase__				)
         and tf.keras.layers.Layer in module_member.__bases__
         and getattr(UpperCamelCase__       ,     "_keras_serializable"       ,     UpperCamelCase__				)
     }

     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
       UpperCAmelCase_				       =					TFSharedEmbeddings(self.model_tester.vocab_size       ,     self.model_tester.hidden_size       ,     name="shared"				)
       UpperCAmelCase_				       =					False
       UpperCAmelCase_				       =					main_layer_class(UpperCamelCase__       ,     embed_tokens=UpperCamelCase__				)
      else:
       UpperCAmelCase_				       =					main_layer_class(UpperCamelCase__				)

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

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

      UpperCAmelCase_				       =					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				)

      UpperCAmelCase_				       =					main_layer(UpperCamelCase__				)[0]
      UpperCAmelCase_				       =					tf.keras.layers.Dense(UpperCamelCase__       ,     activation="softmax"       ,     name="outputs"				)(UpperCamelCase__				)
      UpperCAmelCase_				       =					tf.keras.models.Model(inputs=UpperCamelCase__       ,     outputs=[outputs]				)

      model.compile(loss="binary_crossentropy"       ,     optimizer="adam"       ,     metrics=["binary_accuracy"]				)
      model.fit(UpperCamelCase__       ,     epochs=1				)

      with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCAmelCase_				       =					os.path.join(UpperCamelCase__       ,     "keras_model.h5"				)
       model.save(UpperCamelCase__				)
       if "T5" in main_layer_class.__name__:
        UpperCAmelCase_				       =					tf.keras.models.load_model(
            UpperCamelCase__       ,     custom_objects={
                main_layer_class.__name__: main_layer_class,
                "TFSharedEmbeddings": TFSharedEmbeddings,
            }       ,     )
       else:
        UpperCAmelCase_				       =					tf.keras.models.load_model(
            UpperCamelCase__       ,     custom_objects={main_layer_class.__name__: main_layer_class}				)
       assert isinstance(UpperCamelCase__       ,     tf.keras.Model				)
       model(UpperCamelCase__				)







    @slow
    def   lowerCamelCase_    (	self				)				->       str:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_				       =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:2]:
      UpperCAmelCase_				       =					model_class(UpperCamelCase__				)

      UpperCAmelCase_				       =					copy.deepcopy(UpperCamelCase__				)

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

      if not self.is_encoder_decoder:
       UpperCAmelCase_				       =					model.get_input_embeddings()(UpperCamelCase__				)
      else:
       UpperCAmelCase_				       =					model.get_input_embeddings()(UpperCamelCase__				)
       UpperCAmelCase_				       =					model.get_input_embeddings()(UpperCamelCase__				)

      UpperCAmelCase_				       =					self._prepare_for_class(UpperCamelCase__       ,     UpperCamelCase__				)

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

      UpperCAmelCase_				       =					run_in_graph_mode()
      self.assertIsNotNone(UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       Dict:





     """simple docstring"""

     UpperCAmelCase_				       =					[]
     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
     UpperCAmelCase_				       =					[]
     while len(UpperCamelCase__				) < num_bad_tokens:
      UpperCAmelCase_				       =					tf.squeeze(ids_tensor((1, 1)       ,     self.model_tester.vocab_size				)       ,     0				).numpy()[0]
      if token not in special_tokens:
       bad_tokens.append(UpperCamelCase__				)
     return bad_tokens







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       List[str]:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       Any:





     """simple docstring"""

     for bad_word_ids in bad_words_ids:
      # for all slices in batch
      for generated_ids_slice in generated_ids:
       # for all word idx
       for i in range(len(UpperCamelCase__				)       ,     len(UpperCamelCase__				)				):
        # if tokens match
        if generated_ids_slice[i - len(UpperCamelCase__				) : i] == bad_word_ids:
         return True
     return False
