
'''simple docstring'''
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
    )
    from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class       _lowerCAmelCase   (   __a				):







       '''simple docstring'''

       def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=32  ,  UpperCAmelCase=5  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase="None"  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  )					->       Dict:
              _snake_case       =  parent
              _snake_case       =  batch_size
              _snake_case       =  seq_length
              _snake_case       =  is_training
              _snake_case       =  use_input_mask
              _snake_case       =  use_token_type_ids
              _snake_case       =  use_labels
              _snake_case       =  vocab_size
              _snake_case       =  hidden_size
              _snake_case       =  num_hidden_layers
              _snake_case       =  num_attention_heads
              _snake_case       =  intermediate_size
              _snake_case       =  hidden_act
              _snake_case       =  hidden_dropout_prob
              _snake_case       =  attention_probs_dropout_prob
              _snake_case       =  max_position_embeddings
              _snake_case       =  type_vocab_size
              _snake_case       =  type_sequence_label_size
              _snake_case       =  initializer_range
              _snake_case       =  num_labels
              _snake_case       =  num_choices
              _snake_case       =  relative_attention
              _snake_case       =  position_biased_input
              _snake_case       =  pos_att_type
              _snake_case       =  scope

       def 	lowercase							(self  )					->       Optional[Any]:
              _snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

              _snake_case       =  None
              if self.use_input_mask:
                     _snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2  )

              _snake_case       =  None
              if self.use_token_type_ids:
                     _snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size  )

              _snake_case       =  None
              _snake_case       =  None
              _snake_case       =  None
              if self.use_labels:
                     _snake_case       =  ids_tensor([self.batch_size]  ,  self.type_sequence_label_size  )
                     _snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels  )
                     _snake_case       =  ids_tensor([self.batch_size]  ,  self.num_choices  )

              _snake_case       =  self.get_config()

              return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

       def 	lowercase							(self  )					->       Optional[int]:
              return DebertaConfig(
                  vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  type_vocab_size=self.type_vocab_size  ,  initializer_range=self.initializer_range  ,  relative_attention=self.relative_attention  ,  position_biased_input=self.position_biased_input  ,  pos_att_type=self.pos_att_type  ,  )

       def 	lowercase							(self  )					->       str:
              _snake_case       =  self.get_config()
              _snake_case       =  300
              return config

       def 	lowercase							(self  ,  UpperCAmelCase  )					->       str:
              self.parent.assertListEqual(list(result.loss.size()  )  ,  []  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
              _snake_case       =  DebertaModel(config=UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              _snake_case       =  model(UpperCamelCase__  ,  attention_mask=UpperCamelCase__  ,  token_type_ids=UpperCamelCase__  )[0]
              _snake_case       =  model(UpperCamelCase__  ,  token_type_ids=UpperCamelCase__  )[0]
              _snake_case       =  model(UpperCamelCase__  )[0]

              self.parent.assertListEqual(list(sequence_output.size()  )  ,  [self.batch_size, self.seq_length, self.hidden_size]  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
              _snake_case       =  DebertaForMaskedLM(config=UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              _snake_case       =  model(UpperCamelCase__  ,  attention_mask=UpperCamelCase__  ,  token_type_ids=UpperCamelCase__  ,  labels=UpperCamelCase__  )

              self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
              _snake_case       =  self.num_labels
              _snake_case       =  DebertaForSequenceClassification(UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              _snake_case       =  model(UpperCamelCase__  ,  attention_mask=UpperCamelCase__  ,  token_type_ids=UpperCamelCase__  ,  labels=UpperCamelCase__  )
              self.parent.assertListEqual(list(result.logits.size()  )  ,  [self.batch_size, self.num_labels]  )
              self.check_loss_output(UpperCamelCase__  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
              _snake_case       =  self.num_labels
              _snake_case       =  DebertaForTokenClassification(config=UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              _snake_case       =  model(UpperCamelCase__  ,  attention_mask=UpperCamelCase__  ,  token_type_ids=UpperCamelCase__  ,  labels=UpperCamelCase__  )
              self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
              _snake_case       =  DebertaForQuestionAnswering(config=UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              _snake_case       =  model(
                  UpperCamelCase__  ,  attention_mask=UpperCamelCase__  ,  token_type_ids=UpperCamelCase__  ,  start_positions=UpperCamelCase__  ,  end_positions=UpperCamelCase__  ,  )
              self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)  )
              self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)  )





       def 	lowercase							(self  )					->       Any:
              _snake_case       =  self.prepare_config_and_inputs()
              (
                  (
                  _snake_case     
              ),							(
                  _snake_case     
              ),							(
                  _snake_case     
              ),							(
                  _snake_case     
              ),							(
                  _snake_case     
              ),							(
                  _snake_case     
              ),							(
                  _snake_case     
              ),							     
              )       =  config_and_inputs
              _snake_case       =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
              return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   __a					,							__a					,							unittest.TestCase				):







       '''simple docstring'''

       lowerCAmelCase_ 						=						(
           (
               DebertaModel,
               DebertaForMaskedLM,
               DebertaForSequenceClassification,
               DebertaForTokenClassification,
               DebertaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_ 						=						(
           {
               "feature-extraction": DebertaModel,
               "fill-mask": DebertaForMaskedLM,
               "question-answering": DebertaForQuestionAnswering,
               "text-classification": DebertaForSequenceClassification,
               "token-classification": DebertaForTokenClassification,
               "zero-shot": DebertaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase_ 						=						True
       lowerCAmelCase_ 						=						False
       lowerCAmelCase_ 						=						False
       lowerCAmelCase_ 						=						False
       lowerCAmelCase_ 						=						False

       def 	lowercase							(self  )					->       Any:
              _snake_case       =  DebertaModelTester(self  )
              _snake_case       =  ConfigTester(self  ,  config_class=UpperCamelCase__  ,  hidden_size=37  )

       def 	lowercase							(self  )					->       List[str]:
              self.config_tester.run_common_tests()

       def 	lowercase							(self  )					->       Tuple:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_deberta_model(*UpperCamelCase__  )

       def 	lowercase							(self  )					->       List[str]:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase__  )

       def 	lowercase							(self  )					->       Optional[int]:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase__  )

       def 	lowercase							(self  )					->       Optional[int]:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_deberta_for_question_answering(*UpperCamelCase__  )

       def 	lowercase							(self  )					->       Optional[Any]:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_deberta_for_token_classification(*UpperCamelCase__  )





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



              for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _snake_case       =  DebertaModel.from_pretrained(UpperCamelCase__  )
                     self.assertIsNotNone(UpperCamelCase__  )


@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (   unittest.TestCase				):







       '''simple docstring'''

       @unittest.skip(reason="""Model not available yet"""  )
       def 	lowercase							(self  )					->       Tuple:
              pass





       @slow
       def 	lowercase							(self  )					->       Optional[Any]:
              _snake_case       =  DebertaModel.from_pretrained("""microsoft/deberta-base"""  )

              _snake_case       =  torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
              _snake_case       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
              with torch.no_grad():
                     _snake_case       =  model(UpperCamelCase__  ,  attention_mask=UpperCamelCase__  )[0]
              # compare the actual values for a slice.
              _snake_case       =  torch.tensor(
                  [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]  )
              self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,  UpperCamelCase__  ,  atol=1e-4  )  ,  f"""{output[:, 1:4, 1:4]}"""  )

'''simple docstring'''
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard


@slow
@require_flax
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([canny_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([pose_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2