


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int="attention"		):




      """simple docstring"""





      a		:int									=			params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a		:Union[str, Any]									=			params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a		:str									=			params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a		:Union[str, Any]									=			params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Any=False		):




      """simple docstring"""





      if split_mlp_wi:
            a		:List[str]									=			params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            a		:Tuple									=			params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            a		:int									=			(wi_a, wi_a)
      else:
            a		:Tuple									=			params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      a		:Any									=			params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict					,    *, UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						bool		):




      """simple docstring"""





      a		:Any									=			traverse_util.flatten_dict(variables['''target''']		)
      a		:List[str]									=			{'''/'''.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		:List[Any]									=			'''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''					,    _A		)

      a		:Dict									=			collections.OrderedDict()

      # Shared embeddings.
      a		:Dict									=			old['''token_embedder/embedding''']

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

            # Block i, layer 1 (MLP).
            a		:Optional[int]									=			tax_layer_norm_lookup(_A					,    _A					,    '''encoder'''					,    '''pre_mlp_layer_norm'''		)
            a		:str									=			tax_mlp_lookup(_A					,    _A					,    '''encoder'''					,    _A		)
            a		:Any									=			layer_norm
            if split_mlp_wi:
                  a		:Optional[int]									=			wi[0].T
                  a		:int									=			wi[1].T
            else:
                  a		:Any									=			wi.T
            a		:List[str]									=			wo.T

      a		:int									=			old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a		:Union[str, Any]									=			old['''encoder/encoder_norm/scale''']

      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		:List[Any]									=			tax_attention_lookup(_A					,    _A					,    '''decoder'''					,    '''self_attention'''		)
                  a		:Tuple									=			layer_norm
                  a		:List[Any]									=			k.T
                  a		:Optional[int]									=			o.T
                  a		:Union[str, Any]									=			q.T
                  a		:Dict									=			v.T

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

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

            a		:int									=			old['''decoder/decoder_norm/scale''']
            a		:Any									=			old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

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

      return new
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						bool		):




      """simple docstring"""





      a		:List[Any]									=			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		:Tuple									=			state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a		:Optional[Any]									=			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		:Dict									=			state_dict['''shared.weight''']

      return state_dict
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      a		:List[str]									=			checkpoints.load_tax_checkpoint(_A		)
      a		:Dict									=			convert_tax_to_pytorch(_A					,    num_layers=config.num_layers					,    is_encoder_only=_A		)
      a		:List[str]									=			make_state_dict(_A					,    _A		)
      model.load_state_dict(_A					,    strict=_A		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						bool = False		):




      """simple docstring"""





      a		:str									=			TaConfig.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		:int									=			TaEncoderModel(_A		)
      else:
            a		:Union[str, Any]									=			TaForConditionalGeneration(_A		)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(_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__":
     snake_case							:       Tuple										=	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
     )
     snake_case							:       Any										=	parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= KandinskyInpaintPipeline
      UpperCAmelCase_   :   str   				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_   :   Optional[int]   				= False




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return self.time_input_dim




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
         return tokenizer




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Any 					=	MCLIPConfig(
             numDims=self.cross_attention_dim       ,transformerDimensions=self.text_embedder_hidden_size       ,hidden_size=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_hidden_layers=5       ,vocab_size=1005       ,)

         UpperCAmelCase__				:					Any 					=	MultilingualCLIP(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	text_encoder.eval()

         return text_encoder




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         torch.manual_seed(0				)

         UpperCAmelCase__				:					Tuple 					=	{
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_image''',
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''encoder_hid_dim''': self.text_embedder_hidden_size,
             '''encoder_hid_dim_type''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         UpperCAmelCase__				:					List[str] 					=	UNetaDConditionModel(**lowerCamelCase_				)
         return model




      @property
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	VQModel(**self.dummy_movq_kwargs				)
         return model




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.dummy_text_encoder
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_tokenizer
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_unet
         UpperCAmelCase__				:					Any 					=	self.dummy_movq

         UpperCAmelCase__				:					Tuple 					=	DDIMScheduler(
             num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0085       ,beta_end=0.012       ,clip_sample=lowerCamelCase_       ,set_alpha_to_one=lowerCamelCase_       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=lowerCamelCase_       ,)

         UpperCAmelCase__				:					Dict 					=	{
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1				)				).to(lowerCamelCase_				)
         # create init_image
         UpperCAmelCase__				:					str 					=	floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	image.cpu().permute(0       ,2       ,3       ,1				)[0]
         UpperCAmelCase__				:					Union[str, Any] 					=	Image.fromarray(np.uinta(lowerCamelCase_				)				).convert('''RGB'''				).resize((256, 256)				)
         # create mask
         UpperCAmelCase__				:					List[str] 					=	np.ones((64, 64)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	0

         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					Any 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Dict 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	{
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	'''cpu'''

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()

         UpperCAmelCase__				:					Tuple 					=	self.pipeline_class(**lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	pipe.to(lowerCamelCase_				)

         pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	pipe(**self.get_dummy_inputs(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[Any] 					=	output.images

         UpperCAmelCase__				:					Union[str, Any] 					=	pipe(
             **self.get_dummy_inputs(lowerCamelCase_				)       ,return_dict=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1]
         UpperCAmelCase__				:					int 					=	image_from_tuple[0, -3:, -3:, -1]

         print(f'''image.shape {image.shape}'''				)

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

         UpperCAmelCase__				:					List[Any] 					=	np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

         assert (
             np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
         ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
         ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



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




@slow
@require_torch_gpu
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

         UpperCAmelCase__				:					str 					=	load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	np.ones((768, 768)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					int 					=	0

         UpperCAmelCase__				:					List[Any] 					=	'''a hat'''

         UpperCAmelCase__				:					str 					=	KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa				)
         pipe_prior.to(lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Tuple 					=	pipeline.to(lowerCamelCase_				)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	torch.Generator(device='''cpu'''				).manual_seed(0				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					int 					=	pipe_prior(
             lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

         UpperCAmelCase__				:					Optional[Any] 					=	pipeline(
             lowerCamelCase_       ,image=lowerCamelCase_       ,mask_image=lowerCamelCase_       ,image_embeds=lowerCamelCase_       ,negative_image_embeds=lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

         UpperCAmelCase__				:					str 					=	output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(lowerCamelCase_       ,lowerCamelCase_				)




