


'''simple docstring'''







import argparse

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


logging.set_verbosity_info()






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      List[str]       ):
       # Initialise PyTorch model
       _A					:     Optional[Any]     = TaConfig.from_json_file(lowerCamelCase       )
       print(F'Building PyTorch model from configuration: {config}'       )
       _A					:     List[Any]     = TaForConditionalGeneration(lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

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


if __name__ == "__main__":
   A				:					Any    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   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.'''
   )
   A				:					Optional[Any]    						=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   KandinskyVaaPriorPipeline
    a													=   ["prompt"]
    a													=   ["prompt", "negative_prompt"]
    a													=   [
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    a													=   False


    @property
    def        A   (  self      :							List[str]):
           return 32


    @property
    def        A   (  self      :							List[Any]):
           return 32


    @property
    def        A   (  self      :							Dict):
           return self.time_input_dim


    @property
    def        A   (  self      :							Tuple):
           return self.time_input_dim * 4


    @property
    def        A   (  self      :							Optional[int]):
           return 100


    @property
    def        A   (  self      :							Dict):
           _A					:     Tuple     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
           return tokenizer


    @property
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     Any     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE)


    @property
    def        A   (  self      :							List[Any]):
           torch.manual_seed(0)

           _A					:     Optional[Any]     = {
               'num_attention_heads': 2,
               'attention_head_dim': 12,
               'embedding_dim': self.text_embedder_hidden_size,
               'num_layers': 1,
           }

           _A					:     Any     = PriorTransformer(**SCREAMING_SNAKE_CASE)
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           _A					:     str     = nn.Parameter(torch.ones(model.clip_std.shape))
           return model


    @property
    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[Any]     = CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size				,							image_size=224				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							num_attention_heads=4				,							num_channels=3				,							num_hidden_layers=5				,							patch_size=14				,							)

           _A					:     Union[str, Any]     = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE)
           return model


    @property
    def        A   (  self      :							int):
           _A					:     Optional[Any]     = CLIPImageProcessor(
               crop_size=224				,							do_center_crop=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_resize=SCREAMING_SNAKE_CASE				,							image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,							image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,							resample=3				,							size=224				,							)

           return image_processor


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = self.dummy_prior
           _A					:     Dict     = self.dummy_image_encoder
           _A					:     Dict     = self.dummy_text_encoder
           _A					:     str     = self.dummy_tokenizer
           _A					:     Optional[Any]     = self.dummy_image_processor

           _A					:     Optional[Any]     = UnCLIPScheduler(
               variance_type='fixed_small_log'				,							prediction_type='sample'				,							num_train_timesteps=1000				,							clip_sample=SCREAMING_SNAKE_CASE				,							clip_sample_range=10.0				,							)

           _A					:     Dict     = {
               'prior': prior,
               'image_encoder': image_encoder,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'scheduler': scheduler,
               'image_processor': image_processor,
           }

           return components


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Dict     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     int     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = {
               'prompt': 'horse',
               'generator': generator,
               'guidance_scale': 4.0,
               'num_inference_steps': 2,
               'output_type': 'np',
           }
           return inputs


    def        A   (  self      :							List[Any]):
           _A					:     str     = 'cpu'

           _A					:     Tuple     = self.get_dummy_components()

           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           _A					:     Any     = pipe.to(SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Dict     = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
           _A					:     str     = output.image_embeds

           _A					:     Optional[int]     = pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

           _A					:     Optional[int]     = image[0, -10:]
           _A					:     int     = image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           _A					:     Dict     = np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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


    @skip_mps
    def        A   (  self      :							Any):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[int]     = True
           _A					:     Tuple     = False

           self._test_inference_batch_single_identical(
               test_max_difference=SCREAMING_SNAKE_CASE				,							relax_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)


    @skip_mps
    def        A   (  self      :							int):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[Any]     = False

           self._test_attention_slicing_forward_pass(
               test_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)






