




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				StableDiffusionPanoramaPipeline
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_IMAGE_PARAMS


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





           '''simple docstring'''

           torch.manual_seed(0)
           A__							=   UNetaDConditionModel(
               block_out_channels=(32, 64) ,			layers_per_block=1 ,			sample_size=32 ,			in_channels=4 ,			out_channels=4 ,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,			cross_attention_dim=32 ,			)
           A__							=   DDIMScheduler()
           torch.manual_seed(0)
           A__							=   AutoencoderKL(
               block_out_channels=[32, 64] ,			in_channels=3 ,			out_channels=3 ,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,			latent_channels=4 ,			)
           torch.manual_seed(0)
           A__							=   CLIPTextConfig(
               bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=32 ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1_000 ,			)
           A__							=   CLIPTextModel(UpperCAmelCase__)
           A__							=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__							=   {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components


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





           '''simple docstring'''

           A__							=   torch.manual_seed(UpperCAmelCase__)
           A__							=   {
               '''prompt''': '''a photo of the dolomites''',
               '''generator''': generator,
               # Setting height and width to None to prevent OOMs on CPU.
               '''height''': None,
               '''width''': None,
               '''num_inference_steps''': 1,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   sd_pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])

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


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





           '''simple docstring'''

           super().test_inference_batch_consistent(batch_sizes=[1, 2])


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





           '''simple docstring'''

           super().test_inference_batch_single_identical(batch_size=2 ,			expected_max_diff=3.25e-3)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->int:





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   '''french fries'''
           A__							=   sd_pipe(**UpperCAmelCase__ ,			negative_prompt=UpperCAmelCase__)
           A__							=   output.images
           A__							=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])

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


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





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   sd_pipe(**UpperCAmelCase__ ,			view_batch_size=2)
           A__							=   output.images
           A__							=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])

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


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





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   EulerAncestralDiscreteScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''')
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

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

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952])

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   PNDMScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''' ,			skip_prk_steps=UpperCAmelCase__)
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

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

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])

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


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

    '''simple docstring'''






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





           '''simple docstring'''

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


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





           '''simple docstring'''

           A__							=   torch.manual_seed(UpperCAmelCase__)
           A__							=   {
               '''prompt''': '''a photo of the dolomites''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs


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





           '''simple docstring'''

           A__							=   '''stabilityai/stable-diffusion-2-base'''
           A__							=   DDIMScheduler.from_pretrained(UpperCAmelCase__ ,			subfolder='''scheduler''')
           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__ ,			scheduler=UpperCAmelCase__ ,			safety_checker=UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__							=   self.get_inputs()
           A__							=   pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 2_048, 3)

           A__							=   np.array(
               [
                   0.36968392,
                   0.27025372,
                   0.32446766,
                   0.28379387,
                   0.36363274,
                   0.30733347,
                   0.27100027,
                   0.27054125,
                   0.25536096,
               ])

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Tuple:





           '''simple docstring'''

           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2-base''' ,			safety_checker=UpperCAmelCase__)
           A__							=   LMSDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__							=   self.get_inputs()
           A__							=   pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 2_048, 3)

           A__							=   np.array(
               [
                   [
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                   ]
               ])

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


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





           '''simple docstring'''

           A__							=   0

           def callback_fn(UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     torch.FloatTensor) -> None:
                  A__							=   True
                  nonlocal number_of_steps
                  number_of_steps += 1
                  if step == 1:
                         A__							=   latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 64, 256)
                         A__							=   latents[0, -3:, -3:, -1]

                         A__							=   np.array(
                             [
                                 0.18681869,
                                 0.33907816,
                                 0.5361276,
                                 0.14432865,
                                 -0.02856611,
                                 -0.73941123,
                                 0.23397987,
                                 0.47322682,
                                 -0.37823164,
                             ])
                         assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
                  elif step == 2:
                         A__							=   latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 64, 256)
                         A__							=   latents[0, -3:, -3:, -1]

                         A__							=   np.array(
                             [
                                 0.18539645,
                                 0.33987248,
                                 0.5378559,
                                 0.14437142,
                                 -0.02455261,
                                 -0.7338317,
                                 0.23990755,
                                 0.47356272,
                                 -0.3786505,
                             ])

                         assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

           A__							=   False

           A__							=   '''stabilityai/stable-diffusion-2-base'''
           A__							=   DDIMScheduler.from_pretrained(UpperCAmelCase__ ,			subfolder='''scheduler''')
           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__ ,			scheduler=UpperCAmelCase__ ,			safety_checker=UpperCAmelCase__)
           A__							=   pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__							=   self.get_inputs()
           pipe(**UpperCAmelCase__ ,			callback=UpperCAmelCase__ ,			callback_steps=1)
           assert callback_fn.has_been_called
           assert number_of_steps == 3





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





           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           A__							=   '''stabilityai/stable-diffusion-2-base'''
           A__							=   DDIMScheduler.from_pretrained(UpperCAmelCase__ ,			subfolder='''scheduler''')
           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__ ,			scheduler=UpperCAmelCase__ ,			safety_checker=UpperCAmelCase__)
           A__							=   pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           A__							=   self.get_inputs()
           A__							=   pipe(**UpperCAmelCase__)

           A__							=   torch.cuda.max_memory_allocated()
           # make sure that less than 5.2 GB is allocated
           assert mem_bytes < 5.5 * 10**9








import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   AutoConfig.from_pretrained(lowercase_						)
       A__							=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_						)
       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)

       A__							=   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       if config.model_type == "t5":
              A__							=   '''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              A__							=   '''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   '''TransientGlobalSelfAttention'''
       else:
              raise ValueError(
                  '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                  ''' attribute with a value from [\'local\', \'transient-global].'''						)

       # Encoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''encoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_attention_layer_norm

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_global_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo
              A__							=   tax_mlp_layer_norm

              A__							=   flax_model_encoder_layer_block

       # Only for layer 0:
       A__							=   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_encoder_rel_embedding

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
              A__							=   tax_encoder_global_rel_embedding

       # Assigning
       A__							=   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       A__							=   tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
              A__							=   tax_enc_dec_attention_module['''key''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''out''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''query''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''decoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_pre_attention_layer_norm

              A__							=   tax_enc_dec_attention_key
              A__							=   tax_enc_dec_attention_out
              A__							=   tax_enc_dec_attention_query
              A__							=   tax_enc_dec_attention_value

              A__							=   tax_cross_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo

              A__							=   txa_mlp_layer_norm

              A__							=   flax_model_decoder_layer_block

       # Decoder Normalization
       A__							=   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       A__							=   txa_decoder_norm

       # Only for layer 0:
       A__							=   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_decoder_rel_embedding

       # Token Embeddings
       A__							=   tax_model['''target''']['''token_embedder''']['''embedding''']
       A__							=   txa_token_embeddings

       # LM Head (only in v1.1 and LongT5 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              A__							=   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

       flax_model.save_pretrained(lowercase_						)
       print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  _lowerCamelCase     :		Tuple								=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


