


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    lowerCamelCase__     ( lowerCAmelCase     ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__       =    RePaintPipeline
     SCREAMING_SNAKE_CASE__       =    IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     SCREAMING_SNAKE_CASE__       =    PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     SCREAMING_SNAKE_CASE__       =    IMAGE_INPAINTING_BATCH_PARAMS
     SCREAMING_SNAKE_CASE__       =    False





     def   __A      (self )  ->     Optional[int]:
      torch.manual_seed(0 )
      torch.manual_seed(0 )
      _lowercase							   =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''')	,  )
      _lowercase							   =RePaintScheduler()
      _lowercase							   ={'''unet''': unet, '''scheduler''': scheduler}
      return components





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase=0 )  ->     List[Any]:
      if str(UpperCAmelCase ).startswith('''mps''' ):
       _lowercase							   =torch.manual_seed(UpperCAmelCase )
      else:
       _lowercase							   =torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
      _lowercase							   =np.random.RandomState(UpperCAmelCase ).standard_normal((1, 3, 3_2, 3_2) )
      _lowercase							   =torch.from_numpy(UpperCAmelCase ).to(device=UpperCAmelCase	,  dtype=torch.floataa )
      _lowercase							   =(image > 0).to(device=UpperCAmelCase	,  dtype=torch.floataa )
      _lowercase							   ={
          '''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   __A      (self )  ->     int:
      _lowercase							   ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
      _lowercase							   =self.get_dummy_components()
      _lowercase							   =RePaintPipeline(**UpperCAmelCase )
      _lowercase							   =sd_pipe.to(UpperCAmelCase )
      sd_pipe.set_progress_bar_config(disable=UpperCAmelCase )

      _lowercase							   =self.get_dummy_inputs(UpperCAmelCase )
      _lowercase							   =sd_pipe(**UpperCAmelCase ).images
      _lowercase							   =image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      _lowercase							   =np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274] )

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





     @skip_mps
     def   __A      (self )  ->     Optional[int]:
      return super().test_save_load_local()





     @unittest.skip('''non-deterministic pipeline''' )
     def   __A      (self )  ->     int:
      return super().test_inference_batch_single_identical()





     @skip_mps
     def   __A      (self )  ->     Union[str, Any]:
      return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def   __A      (self )  ->     Optional[int]:
      return super().test_save_load_optional_components()





     @skip_mps
     def   __A      (self )  ->     Union[str, Any]:
      return super().test_attention_slicing_forward_pass()







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





     def   __A      (self )  ->     Union[str, Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def   __A      (self )  ->     int:
      _lowercase							   =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
          '''repaint/celeba_hq_256.png''' )
      _lowercase							   =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png''' )
      _lowercase							   =load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
          '''repaint/celeba_hq_256_result.npy''' )

      _lowercase							   ='''google/ddpm-ema-celebahq-256'''
      _lowercase							   =UNetaDModel.from_pretrained(UpperCAmelCase )
      _lowercase							   =RePaintScheduler.from_pretrained(UpperCAmelCase )

      _lowercase							   =RePaintPipeline(unet=UpperCAmelCase	,  scheduler=UpperCAmelCase ).to(UpperCAmelCase )
      repaint.set_progress_bar_config(disable=UpperCAmelCase )
      repaint.enable_attention_slicing()

      _lowercase							   =torch.manual_seed(0 )
      _lowercase							   =repaint(
          UpperCAmelCase	,  UpperCAmelCase	,  num_inference_steps=2_5_0	,  eta=0.0	,  jump_length=1_0	,  jump_n_sample=1_0	,  generator=UpperCAmelCase	,  output_type='''np'''	,  )
      _lowercase							   =output.images[0]

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





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch







class    lowerCamelCase__     :





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase=1_4	,  UpperCAmelCase=7	,  UpperCAmelCase=True	,  UpperCAmelCase=True	,  UpperCAmelCase=False	,  UpperCAmelCase=True	,  UpperCAmelCase=9_9	,  UpperCAmelCase=3_2	,  UpperCAmelCase=5	,  UpperCAmelCase=4	,  UpperCAmelCase=3_7	,  UpperCAmelCase="gelu"	,  UpperCAmelCase=0.1	,  UpperCAmelCase=0.1	,  UpperCAmelCase=5_1_2	,  UpperCAmelCase=0.02	,  )  ->     Any:
      _lowercase							   =parent
      _lowercase							   =batch_size
      _lowercase							   =seq_length
      _lowercase							   =is_training
      _lowercase							   =use_input_mask
      _lowercase							   =use_token_type_ids
      _lowercase							   =use_labels
      _lowercase							   =vocab_size
      _lowercase							   =hidden_size
      _lowercase							   =num_hidden_layers
      _lowercase							   =num_attention_heads
      _lowercase							   =intermediate_size
      _lowercase							   =hidden_act
      _lowercase							   =hidden_dropout_prob
      _lowercase							   =attention_probs_dropout_prob
      _lowercase							   =max_position_embeddings
      _lowercase							   =initializer_range
      _lowercase							   =None
      _lowercase							   =vocab_size - 1
      _lowercase							   =vocab_size - 1
      _lowercase							   =vocab_size - 1





     def   __A      (self )  ->     int:
      _lowercase							   =ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

      _lowercase							   =None
      if self.use_input_mask:
       _lowercase							   =random_attention_mask([self.batch_size, self.seq_length] )

      _lowercase							   =GPTaConfig(
          vocab_size=self.vocab_size	,  n_embd=self.hidden_size	,  n_layer=self.num_hidden_layers	,  n_head=self.num_attention_heads	,  n_positions=self.max_position_embeddings	,  use_cache=UpperCAmelCase	,  bos_token_id=self.bos_token_id	,  eos_token_id=self.eos_token_id	,  pad_token_id=self.pad_token_id	,  )

      return (config, input_ids, input_mask)





     def   __A      (self )  ->     List[Any]:
      _lowercase							   =self.prepare_config_and_inputs()
      _lowercase   ,					_lowercase   ,					_lowercase							   =config_and_inputs
      _lowercase							   ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict





     def   __A      (self )  ->     Tuple:
      _lowercase   ,					_lowercase   ,					_lowercase							   =self.prepare_config_and_inputs()

      _lowercase							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      _lowercase							   =ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Dict:
      _lowercase							   =2_0
      _lowercase							   =model_class_name(UpperCAmelCase )

      _lowercase							   =model.init_cache(input_ids.shape[0]	,  UpperCAmelCase )
      _lowercase							   =jnp.ones((input_ids.shape[0], max_decoder_length)	,  dtype='''i4''' )

      _lowercase							   =jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
      _lowercase							   =model(
          input_ids[:, :-1]	,  attention_mask=UpperCAmelCase	,  past_key_values=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,  dtype='''i4''' )
      _lowercase							   =model(
          input_ids[:, -1:]	,  attention_mask=UpperCAmelCase	,  past_key_values=outputs_cache.past_key_values	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =model(UpperCAmelCase )

      _lowercase							   =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   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     List[str]:
      _lowercase							   =2_0
      _lowercase							   =model_class_name(UpperCAmelCase )

      _lowercase							   =jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]	,  axis=-1	,  )

      _lowercase							   =model.init_cache(input_ids.shape[0]	,  UpperCAmelCase )
      _lowercase							   =jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

      _lowercase							   =model(
          input_ids[:, :-1]	,  attention_mask=UpperCAmelCase	,  past_key_values=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )
      _lowercase							   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,  dtype='''i4''' )
      _lowercase							   =model(
          input_ids[:, -1:]	,  past_key_values=outputs_cache.past_key_values	,  attention_mask=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =model(UpperCAmelCase	,  attention_mask=UpperCAmelCase )

      _lowercase							   =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    lowerCamelCase__     ( lowerCAmelCase     ,							lowerCAmelCase     ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__       =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE__       =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()





     def   __A      (self )  ->     str:
      _lowercase							   =FlaxGPTaModelTester(self )





     def   __A      (self )  ->     Optional[int]:
      for model_class_name in self.all_model_classes:
       _lowercase   ,					_lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self )  ->     Dict:
      for model_class_name in self.all_model_classes:
       _lowercase   ,					_lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     @slow
     def   __A      (self )  ->     Dict:
      _lowercase							   =GPTaTokenizer.from_pretrained('''gpt2'''	,  pad_token='''</s>'''	,  padding_side='''left''' )
      _lowercase							   =tokenizer(['''Hello this is a long string''', '''Hey''']	,  return_tensors='''np'''	,  padding=UpperCAmelCase	,  truncation=UpperCAmelCase )

      _lowercase							   =FlaxGPTaLMHeadModel.from_pretrained('''gpt2''' )
      _lowercase							   =False
      _lowercase							   =model.config.eos_token_id

      _lowercase							   =jax.jit(model.generate )

      _lowercase							   =jit_generate(inputs['''input_ids''']	,  attention_mask=inputs['''attention_mask'''] ).sequences

      _lowercase							   =tokenizer.batch_decode(UpperCAmelCase	,  skip_special_tokens=UpperCAmelCase )

      _lowercase							   =[
          '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
          '''Hey, I\'m not sure if I\'m going to be able to do''',
      ]

      self.assertListEqual(UpperCAmelCase	,  UpperCAmelCase )





     @is_pt_flax_cross_test
     def   __A      (self )  ->     Optional[Any]:
      _lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        # prepare inputs
        _lowercase							   =self._prepare_for_class(UpperCAmelCase	,  UpperCAmelCase )
        _lowercase							   ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        _lowercase							   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
        _lowercase							   =getattr(UpperCAmelCase	,  UpperCAmelCase )

        _lowercase   ,					_lowercase							   =pt_inputs['''input_ids'''].shape
        _lowercase							   =np.random.randint(0	,  seq_length - 1	,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(UpperCAmelCase ):
         _lowercase							   =0
         _lowercase							   =1
         _lowercase							   =0
         _lowercase							   =1
        _lowercase							   =pt_model_class(UpperCAmelCase ).eval()
        _lowercase							   =model_class(UpperCAmelCase	,  dtype=jnp.floataa )

        _lowercase							   =convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,  UpperCAmelCase )
        _lowercase							   =fx_state

        with torch.no_grad():
         _lowercase							   =pt_model(**UpperCAmelCase ).to_tuple()

        _lowercase							   =fx_model(**UpperCAmelCase ).to_tuple()
        self.assertEqual(len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(UpperCAmelCase )
         _lowercase							   =model_class.from_pretrained(UpperCAmelCase	,  from_pt=UpperCAmelCase )

        _lowercase							   =fx_model_loaded(**UpperCAmelCase ).to_tuple()
        self.assertEqual(
            len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output_loaded, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output_loaded[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )





     @is_pt_flax_cross_test
     def   __A      (self )  ->     Optional[Any]:
      _lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        # prepare inputs
        _lowercase							   =self._prepare_for_class(UpperCAmelCase	,  UpperCAmelCase )
        _lowercase							   ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        _lowercase							   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
        _lowercase							   =getattr(UpperCAmelCase	,  UpperCAmelCase )

        _lowercase							   =pt_model_class(UpperCAmelCase ).eval()
        _lowercase							   =model_class(UpperCAmelCase	,  dtype=jnp.floataa )

        _lowercase							   =load_flax_weights_in_pytorch_model(UpperCAmelCase	,  fx_model.params )
        _lowercase   ,					_lowercase							   =pt_inputs['''input_ids'''].shape
        _lowercase							   =np.random.randint(0	,  seq_length - 1	,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(UpperCAmelCase ):
         _lowercase							   =0
         _lowercase							   =1
         _lowercase							   =0
         _lowercase							   =1

        # make sure weights are tied in PyTorch
        pt_model.tie_weights()

        with torch.no_grad():
         _lowercase							   =pt_model(**UpperCAmelCase ).to_tuple()

        _lowercase							   =fx_model(**UpperCAmelCase ).to_tuple()
        self.assertEqual(len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(UpperCAmelCase )
         _lowercase							   =pt_model_class.from_pretrained(UpperCAmelCase	,  from_flax=UpperCAmelCase )

        with torch.no_grad():
         _lowercase							   =pt_model_loaded(**UpperCAmelCase ).to_tuple()

        self.assertEqual(
            len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )





     @slow
     def   __A      (self )  ->     Union[str, Any]:
      for model_class_name in self.all_model_classes:
       _lowercase							   =model_class_name.from_pretrained('''gpt2'''	,  from_pt=UpperCAmelCase )
       _lowercase							   =model(np.ones((1, 1) ) )
       self.assertIsNotNone(UpperCAmelCase )

