


'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   __UpperCamelCase:   List[Any]    =	'''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import AutoTokenizer
   from transformers.models.mbart.modeling_flax_mbart import (
       FlaxMBartForConditionalGeneration,
       FlaxMBartForQuestionAnswering,
       FlaxMBartForSequenceClassification,
       FlaxMBartModel,
       shift_tokens_right,
   )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     str   ,_UpperCAmelCase     :     List[Any]=None   ,_UpperCAmelCase     :     Dict=None   ,_UpperCAmelCase     :     Optional[int]=None   ,_UpperCAmelCase     :     Dict=None   ,_UpperCAmelCase     :     Optional[Any]=None   ,_UpperCAmelCase     :     Dict=None   ,)  ->      List[Any]:




       if attention_mask is None:
              _a   :			Any					 =np.where(input_ids != config.pad_token_id   ,1   ,0  )
       if decoder_attention_mask is None:
              _a   :			List[str]					 =np.where(decoder_input_ids != config.pad_token_id   ,1   ,0  )
       if head_mask is None:
              _a   :			List[Any]					 =np.ones((config.encoder_layers, config.encoder_attention_heads)  )
       if decoder_head_mask is None:
              _a   :			List[str]					 =np.ones((config.decoder_layers, config.decoder_attention_heads)  )
       if cross_attn_head_mask is None:
              _a   :			List[Any]					 =np.ones((config.decoder_layers, config.decoder_attention_heads)  )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }







class A__	(						unittest.TestCase				):






      def __init__(			self      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :List[str]=1_3					,						SCREAMING_SNAKE_CASE      :List[Any]=7					,						SCREAMING_SNAKE_CASE      :List[Any]=True					,						SCREAMING_SNAKE_CASE      :Tuple=False					,						SCREAMING_SNAKE_CASE      :int=9_9					,						SCREAMING_SNAKE_CASE      :Optional[Any]=1_6					,						SCREAMING_SNAKE_CASE      :Tuple=2					,						SCREAMING_SNAKE_CASE      :List[str]=4					,						SCREAMING_SNAKE_CASE      :Optional[int]=4					,						SCREAMING_SNAKE_CASE      :Optional[int]="gelu"					,						SCREAMING_SNAKE_CASE      :Dict=0.1					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=0.1					,						SCREAMING_SNAKE_CASE      :Optional[int]=3_2					,						SCREAMING_SNAKE_CASE      :Optional[int]=2					,						SCREAMING_SNAKE_CASE      :Optional[int]=1					,						SCREAMING_SNAKE_CASE      :Dict=0					,						SCREAMING_SNAKE_CASE      :int=2					,						SCREAMING_SNAKE_CASE      :List[Any]=0.02					,						)	-> Any:




             '''simple docstring'''
             _a   :			Any					 =parent
             _a   :			Dict					 =batch_size
             _a   :			Union[str, Any]					 =seq_length
             _a   :			Optional[Any]					 =is_training
             _a   :			Union[str, Any]					 =use_labels
             _a   :			List[str]					 =vocab_size
             _a   :			str					 =hidden_size
             _a   :			Dict					 =num_hidden_layers
             _a   :			str					 =num_attention_heads
             _a   :			Union[str, Any]					 =intermediate_size
             _a   :			Optional[int]					 =hidden_act
             _a   :			Dict					 =hidden_dropout_prob
             _a   :			Any					 =attention_probs_dropout_prob
             _a   :			List[str]					 =max_position_embeddings
             _a   :			Optional[int]					 =eos_token_id
             _a   :			List[Any]					 =pad_token_id
             _a   :			Dict					 =bos_token_id
             _a   :			List[Any]					 =decoder_start_token_id
             _a   :			Any					 =initializer_range






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




             '''simple docstring'''
             _a   :			int					 =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,						self.vocab_size     )					,						3					,						self.vocab_size     )
             _a   :			Optional[int]					 =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,						dtype=np.intaa     ))					,						-1     )

             _a   :			Any					 =shift_tokens_right(lowercase_					,						1     )

             _a   :			int					 =MBartConfig(
                 vocab_size=self.vocab_size					,						d_model=self.hidden_size					,						encoder_layers=self.num_hidden_layers					,						decoder_layers=self.num_hidden_layers					,						encoder_attention_heads=self.num_attention_heads					,						decoder_attention_heads=self.num_attention_heads					,						encoder_ffn_dim=self.intermediate_size					,						decoder_ffn_dim=self.intermediate_size					,						dropout=self.hidden_dropout_prob					,						attention_dropout=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						eos_token_id=self.eos_token_id					,						bos_token_id=self.bos_token_id					,						pad_token_id=self.pad_token_id					,						decoder_start_token_id=self.decoder_start_token_id					,						initializer_range=self.initializer_range					,						use_cache=lowercase_					,						)
             _a   :			List[Any]					 =prepare_mbart_inputs_dict(lowercase_					,						lowercase_					,						lowercase_     )
             return config, inputs_dict






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




             '''simple docstring'''
             _a ,   _a   :			int					 =self.prepare_config_and_inputs()
             return config, inputs_dict






      def __UpperCAmelCase	(			self      :List[str]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Any     )	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			int					 =2_0
             _a   :			List[str]					 =model_class_name(lowercase_     )

             _a   :			List[str]					 =model.encode(inputs_dict["""input_ids"""]     )

             _a ,   _a   :			Dict					 =(
                 inputs_dict["""decoder_input_ids"""],
                 inputs_dict["""decoder_attention_mask"""],
             )

             _a   :			Optional[Any]					 =model.init_cache(decoder_input_ids.shape[0]					,						lowercase_					,						lowercase_     )
             _a   :			List[str]					 =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,						dtype="""i4"""     )

             _a   :			Dict					 =jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,						)
             _a   :			List[str]					 =model.decode(
                 decoder_input_ids[:, :-1]					,						lowercase_					,						decoder_attention_mask=lowercase_					,						past_key_values=lowercase_					,						decoder_position_ids=lowercase_					,						)

             _a   :			Tuple					 =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype="""i4"""     )
             _a   :			str					 =model.decode(
                 decoder_input_ids[:, -1:]					,						lowercase_					,						decoder_attention_mask=lowercase_					,						past_key_values=outputs_cache.past_key_values					,						decoder_position_ids=lowercase_					,						)

             _a   :			Dict					 =model.decode(lowercase_					,						lowercase_     )

             _a   :			Tuple					 =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
             self.parent.assertTrue(diff < 1e-3					,						msg=f"Max diff is {diff}"     )






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> Optional[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =2_0
             _a   :			Any					 =model_class_name(lowercase_     )

             _a   :			Any					 =model.encode(inputs_dict["""input_ids"""]     )

             _a ,   _a   :			Tuple					 =(
                 inputs_dict["""decoder_input_ids"""],
                 inputs_dict["""decoder_attention_mask"""],
             )

             _a   :			Union[str, Any]					 =jnp.concatenate(
                 [
                     decoder_attention_mask,
                     jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
                 ]					,						axis=-1					,						)

             _a   :			Any					 =model.init_cache(decoder_input_ids.shape[0]					,						lowercase_					,						lowercase_     )
             _a   :			Dict					 =jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,						)

             _a   :			List[Any]					 =model.decode(
                 decoder_input_ids[:, :-1]					,						lowercase_					,						decoder_attention_mask=lowercase_					,						past_key_values=lowercase_					,						decoder_position_ids=lowercase_					,						)
             _a   :			Tuple					 =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype="""i4"""     )
             _a   :			int					 =model.decode(
                 decoder_input_ids[:, -1:]					,						lowercase_					,						past_key_values=outputs_cache.past_key_values					,						decoder_attention_mask=lowercase_					,						decoder_position_ids=lowercase_					,						)

             _a   :			Dict					 =model.decode(lowercase_					,						lowercase_					,						decoder_attention_mask=lowercase_     )

             _a   :			Optional[int]					 =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
             self.parent.assertTrue(diff < 1e-3					,						msg=f"Max diff is {diff}"     )







@require_flax
class A__	(						unittest.TestCase				):
      __UpperCamelCase	:  List[Any]     	=							99






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




             '''simple docstring'''
             _a   :			str					 =np.array(
                 [
                     [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                     [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                     [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                     [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                     [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                     [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                     [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                     [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                     [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                     [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                     [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                     [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                     [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
                 ]					,						dtype=np.intaa					,						)

             _a   :			Union[str, Any]					 =input_ids.shape[0]
             _a   :			List[Any]					 =MBartConfig(
                 vocab_size=self.vocab_size					,						d_model=2_4					,						encoder_layers=2					,						decoder_layers=2					,						encoder_attention_heads=2					,						decoder_attention_heads=2					,						encoder_ffn_dim=3_2					,						decoder_ffn_dim=3_2					,						max_position_embeddings=4_8					,						eos_token_id=2					,						pad_token_id=1					,						bos_token_id=0					,						)
             return config, input_ids, batch_size






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




             '''simple docstring'''
             _a ,   _a ,   _a   :			Tuple					 =self._get_config_and_data()
             _a   :			Dict					 =FlaxMBartForSequenceClassification(lowercase_     )
             _a   :			Dict					 =model(input_ids=lowercase_					,						decoder_input_ids=lowercase_     )
             _a   :			Dict					 =(batch_size, config.num_labels)
             self.assertEqual(outputs["""logits"""].shape					,						lowercase_     )






      def __UpperCAmelCase	(			self      :Dict     )	-> Optional[int]:




             '''simple docstring'''
             _a ,   _a ,   _a   :			Tuple					 =self._get_config_and_data()
             _a   :			List[str]					 =FlaxMBartForQuestionAnswering(lowercase_     )
             _a   :			Optional[Any]					 =model(input_ids=lowercase_     )

             self.assertEqual(outputs["""start_logits"""].shape					,						input_ids.shape     )
             self.assertEqual(outputs["""end_logits"""].shape					,						input_ids.shape     )






      def __UpperCAmelCase	(			self      :Dict     )	-> int:




             '''simple docstring'''
             _a ,   _a ,   _a   :			Any					 =self._get_config_and_data()
             _a   :			Dict					 =FlaxMBartForConditionalGeneration(lowercase_     )
             _a   :			int					 =lm_model(input_ids=lowercase_     )
             _a   :			Union[str, Any]					 =(batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["""logits"""].shape					,						lowercase_     )






      def __UpperCAmelCase	(			self      :Any     )	-> Dict:




             '''simple docstring'''
             _a   :			Optional[Any]					 =MBartConfig(
                 vocab_size=self.vocab_size					,						d_model=1_4					,						encoder_layers=2					,						decoder_layers=2					,						encoder_attention_heads=2					,						decoder_attention_heads=2					,						encoder_ffn_dim=8					,						decoder_ffn_dim=8					,						max_position_embeddings=4_8					,						)
             _a   :			Any					 =FlaxMBartForConditionalGeneration(lowercase_     )
             _a   :			Union[str, Any]					 =np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,						dtype=np.intaa     )
             _a   :			List[Any]					 =np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,						dtype=np.intaa     )
             _a   :			Union[str, Any]					 =lm_model(input_ids=lowercase_					,						decoder_input_ids=lowercase_     )
             _a   :			List[str]					 =(*summary.shape, config.vocab_size)
             self.assertEqual(outputs["""logits"""].shape					,						lowercase_     )






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Dict					 =np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,						dtype=np.intaa     )
             _a   :			Optional[int]					 =shift_tokens_right(lowercase_					,						1     )
             _a   :			Union[str, Any]					 =np.equal(lowercase_					,						1     ).astype(np.floataa     ).sum()
             _a   :			Optional[int]					 =np.equal(lowercase_					,						1     ).astype(np.floataa     ).sum()
             self.assertEqual(shifted.shape					,						input_ids.shape     )
             self.assertEqual(lowercase_					,						n_pad_before - 1     )
             self.assertTrue(np.equal(shifted[:, 0]					,						2     ).all()     )







@require_flax
class A__	(						SCREAMING_SNAKE_CASE_			,					unittest.TestCase			,					SCREAMING_SNAKE_CASE_				):
      __UpperCamelCase	:  Any     	=							True
      __UpperCamelCase	:  Optional[int]     	=							(
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      __UpperCamelCase	:  List[Any]     	=							(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






      def __UpperCAmelCase	(			self      :List[str]     )	-> Dict:




             '''simple docstring'''
             _a   :			str					 =FlaxMBartModelTester(self     )






      def __UpperCAmelCase	(			self      :str     )	-> Tuple:




             '''simple docstring'''
             _a ,   _a   :			Union[str, Any]					 =self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(lowercase_					,						lowercase_					,						lowercase_     )






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




             '''simple docstring'''
             _a ,   _a   :			Union[str, Any]					 =self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_					,						lowercase_					,						lowercase_     )






      def __UpperCAmelCase	(			self      :Dict     )	-> Tuple:




             '''simple docstring'''
             _a ,   _a   :			Any					 =self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__     ):
                           _a   :			Union[str, Any]					 =self._prepare_for_class(lowercase_					,						lowercase_     )
                           _a   :			int					 =model_class(lowercase_     )

                           @jax.jit
                           def encode_jitted(SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :Tuple=None					,						**SCREAMING_SNAKE_CASE      :Any     ):
                                  return model.encode(input_ids=lowercase_					,						attention_mask=lowercase_     )

                           with self.subTest("""JIT Enabled"""     ):
                                  _a   :			Optional[Any]					 =encode_jitted(**lowercase_     ).to_tuple()

                           with self.subTest("""JIT Disabled"""     ):
                                  with jax.disable_jit():
                                         _a   :			Tuple					 =encode_jitted(**lowercase_     ).to_tuple()

                           self.assertEqual(len(lowercase_     )					,						len(lowercase_     )     )
                           for jitted_output, output in zip(lowercase_					,						lowercase_     ):
                                  self.assertEqual(jitted_output.shape					,						output.shape     )






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




             '''simple docstring'''
             _a ,   _a   :			Tuple					 =self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__     ):
                           _a   :			Any					 =model_class(lowercase_     )
                           _a   :			Optional[Any]					 =model.encode(inputs_dict["""input_ids"""]					,						inputs_dict["""attention_mask"""]     )

                           _a   :			Any					 ={
                               """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                               """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                               """encoder_outputs""": encoder_outputs,
                           }

                           @jax.jit
                           def decode_jitted(SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Union[str, Any]     ):
                                  return model.decode(
                                      decoder_input_ids=lowercase_					,						decoder_attention_mask=lowercase_					,						encoder_outputs=lowercase_					,						)

                           with self.subTest("""JIT Enabled"""     ):
                                  _a   :			int					 =decode_jitted(**lowercase_     ).to_tuple()

                           with self.subTest("""JIT Disabled"""     ):
                                  with jax.disable_jit():
                                         _a   :			Dict					 =decode_jitted(**lowercase_     ).to_tuple()

                           self.assertEqual(len(lowercase_     )					,						len(lowercase_     )     )
                           for jitted_output, output in zip(lowercase_					,						lowercase_     ):
                                  self.assertEqual(jitted_output.shape					,						output.shape     )






      @slow
      def __UpperCAmelCase	(			self      :List[Any]     )	-> Tuple:




             '''simple docstring'''
             for model_class_name in self.all_model_classes:
                    _a   :			Optional[Any]					 =model_class_name.from_pretrained("""facebook/mbart-large-cc25"""					,						from_pt=lowercase_     )
                    # FlaxMBartForSequenceClassification expects eos token in input_ids
                    _a   :			Any					 =np.ones((1, 1)     ) * model.config.eos_token_id
                    _a   :			Any					 =model(lowercase_     )
                    self.assertIsNotNone(lowercase_     )







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						unittest.TestCase				):
      __UpperCamelCase	:  Union[str, Any]     	=							[
          " UN Chief Says There Is No Military Solution in Syria",
      ]
      __UpperCamelCase	:  List[Any]     	=							[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
      ]
      __UpperCamelCase	:  str     	=							"facebook/mbart-large-en-ro"






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




             '''simple docstring'''
             return AutoTokenizer.from_pretrained(self.model_name     )






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




             '''simple docstring'''
             _a   :			str					 =FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					,						from_pt=lowercase_     )
             return model






      def __UpperCAmelCase	(			self      :Dict					,						**SCREAMING_SNAKE_CASE      :List[str]     )	-> str:




             '''simple docstring'''
             _a   :			Dict					 =self.translate_src_text(**lowercase_     )
             self.assertListEqual(self.expected_text					,						lowercase_     )






      def __UpperCAmelCase	(			self      :Any					,						**SCREAMING_SNAKE_CASE      :Optional[Any]     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer(self.src_text					,						**lowercase_					,						return_tensors="""np"""     )
             _a   :			Dict					 =self.model.generate(
                 model_inputs.input_ids					,						attention_mask=model_inputs.attention_mask					,						decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]					,						early_stopping=lowercase_					,						num_beams=2					,						).sequences
             _a   :			Dict					 =self.tokenizer.batch_decode(lowercase_					,						skip_special_tokens=lowercase_     )
             return generated_words






      @slow
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             self._assert_generated_batch_equal_expected()






'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							RePaintPipeline
      __UpperCamelCase	:  Dict     	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      __UpperCamelCase	:  List[str]     	=							PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __UpperCamelCase	:  List[Any]     	=							IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase	:  Tuple     	=							False






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




             '''simple docstring'''
             torch.manual_seed(0     )
             torch.manual_seed(0     )
             _a   :			Tuple					 =UNetaDModel(
                 block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=3					,						out_channels=3					,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,						)
             _a   :			Any					 =RePaintScheduler()
             _a   :			Optional[Any]					 ={"""unet""": unet, """scheduler""": scheduler}
             return components






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0     )	-> Any:




             '''simple docstring'''
             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    _a   :			Optional[Any]					 =torch.manual_seed(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Any					 =torch.Generator(device=SCREAMING_SNAKE_CASE     ).manual_seed(SCREAMING_SNAKE_CASE     )
             _a   :			int					 =np.random.RandomState(SCREAMING_SNAKE_CASE     ).standard_normal((1, 3, 3_2, 3_2)     )
             _a   :			Union[str, Any]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			List[Any]					 =(image > 0).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			str					 ={
                 """image""": image,
                 """mask_image""": mask,
                 """generator""": generator,
                 """num_inference_steps""": 5,
                 """eta""": 0.0,
                 """jump_length""": 2,
                 """jump_n_sample""": 2,
                 """output_type""": """numpy""",
             }
             return inputs






      def __UpperCAmelCase	(			self      :Dict     )	-> Dict:




             '''simple docstring'''
             _a   :			Tuple					 ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
             _a   :			Tuple					 =self.get_dummy_components()
             _a   :			str					 =RePaintPipeline(**SCREAMING_SNAKE_CASE     )
             _a   :			int					 =sd_pipe.to(SCREAMING_SNAKE_CASE     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )

             _a   :			Optional[int]					 =self.get_dummy_inputs(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =sd_pipe(**SCREAMING_SNAKE_CASE     ).images
             _a   :			Any					 =image[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             _a   :			str					 =np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






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




             '''simple docstring'''
             return super().test_save_load_local()






      @unittest.skip("""non-deterministic pipeline"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> int:




             '''simple docstring'''
             return super().test_inference_batch_single_identical()






      @skip_mps
      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




             '''simple docstring'''
             return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def __UpperCAmelCase	(			self      :str     )	-> Optional[Any]:




             '''simple docstring'''
             return super().test_save_load_optional_components()






      @skip_mps
      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''simple docstring'''
             return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class A__	(						unittest.TestCase				):






      def __UpperCAmelCase	(			self      :Tuple     )	-> Optional[int]:




             '''simple docstring'''
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Optional[int]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256.png"""     )
             _a   :			Optional[Any]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""     )
             _a   :			Union[str, Any]					 =load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256_result.npy"""     )

             _a   :			str					 ="""google/ddpm-ema-celebahq-256"""
             _a   :			Union[str, Any]					 =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE     )

             _a   :			str					 =RePaintPipeline(unet=SCREAMING_SNAKE_CASE					,						scheduler=SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE     )
             repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )
             repaint.enable_attention_slicing()

             _a   :			Dict					 =torch.manual_seed(0     )
             _a   :			Any					 =repaint(
                 SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						num_inference_steps=2_5_0					,						eta=0.0					,						jump_length=1_0					,						jump_n_sample=1_0					,						generator=SCREAMING_SNAKE_CASE					,						output_type="""np"""					,						)
             _a   :			Any					 =output.images[0]

             assert image.shape == (2_5_6, 2_5_6, 3)
             assert np.abs(expected_image - image     ).mean() < 1e-2


