





'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			str						   =						StableDiffusionInstructPixaPixPipeline
   __UpperCamelCase :			Optional[int]						   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
   __UpperCamelCase :			Any						   =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCamelCase :			List[str]						   =						IMAGE_TO_IMAGE_IMAGE_PARAMS
   __UpperCamelCase :			Dict						   =						IMAGE_TO_IMAGE_IMAGE_PARAMS





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							UNetaDConditionModel(
          block_out_channels=(32, 64)					,layers_per_block=2					,sample_size=32					,in_channels=8					,out_channels=4					,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,cross_attention_dim=32					,)
      __SCREAMING_SNAKE_CASE   				=							PNDMScheduler(skip_prk_steps=lowerCamelCase     )
      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							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     )
      __SCREAMING_SNAKE_CASE   				=							CLIPTextConfig(
          bos_token_id=0					,eos_token_id=2					,hidden_size=32					,intermediate_size=37					,layer_norm_eps=1E-0_5					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1000					,)
      __SCREAMING_SNAKE_CASE   				=							CLIPTextModel(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

      __SCREAMING_SNAKE_CASE   				=							{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Optional[Any]=0     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase     )     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image.cpu().permute(0					,2					,3					,1     )[0]
      __SCREAMING_SNAKE_CASE   				=							Image.fromarray(np.uinta(lowerCamelCase     )     ).convert("""RGB"""     )
      if str(lowerCamelCase     ).startswith("""mps"""     ):
         __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							torch.Generator(device=lowerCamelCase     ).manual_seed(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """image_guidance_scale""": 1,
          """output_type""": """numpy""",
      }
      return inputs





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]     )

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





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							"""french fries"""
      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase					,negative_prompt=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							output.images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]     )

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





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							[inputs["""prompt"""]] * 2

      __SCREAMING_SNAKE_CASE   				=							np.array(inputs["""image"""]     ).astype(np.floataa     ) / 255.0
      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     ).unsqueeze(0     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image / 2 + 0.5
      __SCREAMING_SNAKE_CASE   				=							image.permute(0					,3					,1					,2     )
      __SCREAMING_SNAKE_CASE   				=							image.repeat(2					,1					,1					,1     )

      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]     )

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





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							EulerAncestralDiscreteScheduler(
          beta_start=0.00_085					,beta_end=0.012					,beta_schedule="""scaled_linear"""     )
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

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

      __SCREAMING_SNAKE_CASE   				=							[round(lowerCamelCase					,4     ) for x in image_slice.flatten().tolist()]
      print(""",""".join([str(lowerCamelCase     ) for x in slice]     )     )

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]     )

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							VaeImageProcessor(do_resize=lowerCamelCase					,do_normalize=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							pipe(**self.get_dummy_inputs_by_type(lowerCamelCase					,input_image_type="""pt"""     )     )[0]

      __SCREAMING_SNAKE_CASE   				=							components["""vae"""]
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs_by_type(lowerCamelCase					,input_image_type="""pt"""     )

      for image_param in self.image_latents_params:
         if image_param in inputs.keys():
            __SCREAMING_SNAKE_CASE   				=							vae.encode(inputs[image_param]     ).latent_dist.mode()

      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     )[0]

      __SCREAMING_SNAKE_CASE   				=							np.abs(out - out_latents_inputs     ).max()
      self.assertLess(lowerCamelCase					,1E-4					,"""passing latents as image input generate different result from passing image"""     )







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





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      int=0     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							load_image(
          """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""     )
      __SCREAMING_SNAKE_CASE   				=							{
          """prompt""": """turn him into a cyborg""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 3,
          """guidance_scale""": 7.5,
          """image_guidance_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]     )

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





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]     )

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





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							DDIMScheduler.from_config(pipe.scheduler.config     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]     )

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							0

      def callback_fn(lowerCamelCase					:      int					,lowerCamelCase					:      int					,lowerCamelCase					:      torch.FloatTensor     ) -> None:
         __SCREAMING_SNAKE_CASE   				=							True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
            __SCREAMING_SNAKE_CASE   				=							latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 64)
            __SCREAMING_SNAKE_CASE   				=							latents[0, -3:, -3:, -1]
            __SCREAMING_SNAKE_CASE   				=							np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]     )

            assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
         elif step == 2:
            __SCREAMING_SNAKE_CASE   				=							latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 64)
            __SCREAMING_SNAKE_CASE   				=							latents[0, -3:, -3:, -1]
            __SCREAMING_SNAKE_CASE   				=							np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]     )

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

      __SCREAMING_SNAKE_CASE   				=							False

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase					,torch_dtype=torch.floataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

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





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase					,torch_dtype=torch.floataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing(1     )
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.cuda.max_memory_allocated()
      # make sure that less than 2.2 GB is allocated
      assert mem_bytes < 2.2 * 10**9





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      # resize to resolution that is divisible by 8 but not 16 or 32
      __SCREAMING_SNAKE_CASE   				=							inputs["""image"""].resize((504, 504)     )

      __SCREAMING_SNAKE_CASE   				=							"""timbrooks/instruct-pix2pix"""
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          lowerCamelCase					,safety_checker=lowerCamelCase					,)
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							output.images[0]

      __SCREAMING_SNAKE_CASE   				=							image[255:258, 383:386, -1]

      assert image.shape == (504, 504, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-3








import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Optional[int]      )     ->	Dict:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[int]						,__a :     int						,__a :     List[str]="attention"      )     ->	List[str]:

					"""simple docstring"""

					_a      :       str   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]      )
					_a      :       Tuple   =						k_tmp.reshape(k_tmp.shape[0]						,k_tmp.shape[1] * k_tmp.shape[2]      )
					_a      :       Any   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]      )
					_a      :       Dict   =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,o_tmp.shape[2]      )
					_a      :       Union[str, Any]   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]      )
					_a      :       Any   =						q_tmp.reshape(q_tmp.shape[0]						,q_tmp.shape[1] * q_tmp.shape[2]      )
					_a      :       Tuple   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]      )
					_a      :       int   =						v_tmp.reshape(v_tmp.shape[0]						,v_tmp.shape[1] * v_tmp.shape[2]      )
					return k, o, q, v



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Union[str, Any]						,__a :     List[Any]						,__a :     Any=False      )     ->	Any:

					"""simple docstring"""

					if split_mlp_wi:
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										_a      :       List[str]   =						(wi_a, wi_a)
					else:
										_a      :       List[str]   =						params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

					_a      :       Optional[int]   =						params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Union[str, Any]						,__a :     str      )     ->	List[str]:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def   __UpperCAmelCase					(      __a :     dict						,*, __a :     int						,__a :     bool						,__a :     bool = False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						traverse_util.flatten_dict(variables['''target''']      )
					_a      :       Any   =						{'''/'''.join(__a      ): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_a      :       Optional[int]   =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''						,__a      )

					_a      :       Tuple   =						collections.OrderedDict()

					# Shared embeddings.
					_a      :       Any   =						old['''token_embedder/embedding''']

					# Encoder.
					for i in range(__a      ):
										# Block i, layer 0 (Self Attention).
										_a      :       Optional[Any]   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_attention_layer_norm'''      )
										_a ,   _a ,   _a ,   _a      :       List[str]   =						tax_attention_lookup(__a						,__a						,'''encoder'''						,'''attention'''      )
										_a      :       List[str]   =						layer_norm
										_a      :       Optional[Any]   =						k.T
										_a      :       str   =						o.T
										_a      :       List[Any]   =						q.T
										_a      :       Tuple   =						v.T

										# Block i, layer 1 (MLP).
										_a      :       str   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_mlp_layer_norm'''      )
										_a ,   _a      :       Any   =						tax_mlp_lookup(__a						,__a						,'''encoder'''						,__a      )
										_a      :       str   =						layer_norm
										if split_mlp_wi:
															_a      :       List[Any]   =						wi[0].T
															_a      :       Any   =						wi[1].T
										else:
															_a      :       Any   =						wi.T
										_a      :       Optional[Any]   =						wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															_a      :       Dict   =						tax_relpos_bias_lookup(
															    __a						,__a						,'''encoder'''      ).T

					_a      :       List[str]   =						old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										_a      :       List[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''encoder'''      ).T
										_a      :       Optional[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''decoder'''      ).T

					if not is_encoder_only:
										# Decoder.
										for i in range(__a      ):
															# Block i, layer 0 (Self Attention).
															_a      :       Union[str, Any]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_self_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       Optional[Any]   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''self_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Dict   =						k.T
															_a      :       str   =						o.T
															_a      :       str   =						q.T
															_a      :       List[str]   =						v.T

															# Block i, layer 1 (Cross Attention).
															_a      :       Any   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_cross_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       str   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''encoder_decoder_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Optional[int]   =						k.T
															_a      :       Dict   =						o.T
															_a      :       str   =						q.T
															_a      :       int   =						v.T

															# Block i, layer 2 (MLP).
															_a      :       Optional[int]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_mlp_layer_norm'''      )
															_a ,   _a      :       Tuple   =						tax_mlp_lookup(__a						,__a						,'''decoder'''						,__a      )
															_a      :       Optional[Any]   =						layer_norm
															if split_mlp_wi:
																				_a      :       List[str]   =						wi[0].T
																				_a      :       List[Any]   =						wi[1].T
															else:
																				_a      :       Dict   =						wi.T
															_a      :       str   =						wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_a      :       Tuple   =						tax_relpos_bias_lookup(__a						,__a						,'''decoder'''      ).T

										_a      :       Tuple   =						old['''decoder/decoder_norm/scale''']

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_a      :       Any   =						old['''decoder/logits_dense/kernel'''].T

					return new



def   __UpperCAmelCase					(      __a :     Dict						,__a :     bool      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Tuple   =						collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_a      :       Any   =						state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_a      :       Optional[int]   =						state_dict['''shared.weight''']

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print('''Using shared word embeddings as lm_head.'''      )
															_a      :       str   =						state_dict['''shared.weight''']

					return state_dict



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Union[str, Any]						,__a :     Dict						,__a :     Union[str, Any]						,__a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       List[str]   =						checkpoints.load_tax_checkpoint(__a      )
					_a      :       str   =						convert_tax_to_pytorch(
					    __a						,num_layers=config.num_layers						,is_encoder_only=__a						,scalable_attention=__a      )
					_a      :       str   =						make_state_dict(__a						,__a      )
					model.load_state_dict(__a						,strict=__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Any						,__a :     Union[str, Any]						,__a :     bool = False						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						MTaConfig.from_json_file(__a      )
					print(F"""Building PyTorch model from configuration: {config}"""      )
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_a      :       Any   =						UMTaEncoderModel(__a      )
					else:
										_a      :       Tuple   =						UMTaForConditionalGeneration(__a      )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(__a						,__a						,__a						,__a						,__a      )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
					model.save_pretrained(__a      )

					# Verify that we can load the checkpoint.
					model.from_pretrained(__a      )
					print('''Done'''      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
			# Required parameters
			parser.add_argument(
			    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
			)
			parser.add_argument(
			    '''--scalable_attention''',
			    action='''store_true''',
			    help='''Whether the model uses scaled attention (umt5 model)''',
			    default=False,
			)
			a__												=						parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)



