
import unittest

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

from diffusers import KandinskyPriorPipeline, 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 	A__				(	lowerCAmelCase_		,  unittest.TestCase      ):
				lowercase            =    KandinskyPriorPipeline
				lowercase            =    ["prompt"]
				lowercase            =    ["prompt", "negative_prompt"]
				lowercase            =    [
				    "num_images_per_prompt",
				    "generator",
				    "num_inference_steps",
				    "latents",
				    "negative_prompt",
				    "guidance_scale",
				    "output_type",
				    "return_dict",
				]
				lowercase            =    False






				@property
				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							return 32






				@property
				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							return 32






				@property
				def 				_lowerCamelCase						(		self   : Union[str, Any]  ):






							'''simple docstring'''







							return self.time_input_dim






				@property
				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							return self.time_input_dim * 4






				@property
				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							return 100






				@property
				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
							return tokenizer






				@property
				def 				_lowerCamelCase						(		self   : Union[str, Any]  ):






							'''simple docstring'''







							torch.manual_seed(0  )
							lowerCAmelCase__ : 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=1_000       ,	)
							return CLIPTextModelWithProjection(lowerCamelCase__  )






				@property
				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							torch.manual_seed(0  )

							lowerCAmelCase__ : int   =						{
							    'num_attention_heads': 2,
							    'attention_head_dim': 12,
							    'embedding_dim': self.text_embedder_hidden_size,
							    'num_layers': 1,
							}

							lowerCAmelCase__ : List[Any]   =						PriorTransformer(**lowerCamelCase__  )
							# 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
							lowerCAmelCase__ : List[Any]   =						nn.Parameter(torch.ones(model.clip_std.shape  )  )
							return model






				@property
				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							torch.manual_seed(0  )
							lowerCAmelCase__ : 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       ,	)

							lowerCAmelCase__ : Dict   =						CLIPVisionModelWithProjection(lowerCamelCase__  )
							return model






				@property
				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						CLIPImageProcessor(
							    crop_size=224       ,	do_center_crop=lowerCamelCase__       ,	do_normalize=lowerCamelCase__       ,	do_resize=lowerCamelCase__       ,	image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,	image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,	resample=3       ,	size=224       ,	)

							return image_processor






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						self.dummy_prior
							lowerCAmelCase__ : int   =						self.dummy_image_encoder
							lowerCAmelCase__ : Optional[int]   =						self.dummy_text_encoder
							lowerCAmelCase__ : Optional[int]   =						self.dummy_tokenizer
							lowerCAmelCase__ : str   =						self.dummy_image_processor

							lowerCAmelCase__ : List[str]   =						UnCLIPScheduler(
							    variance_type='fixed_small_log'       ,	prediction_type='sample'       ,	num_train_timesteps=1_000       ,	clip_sample=lowerCamelCase__       ,	clip_sample_range=1_0.0       ,	)

							lowerCAmelCase__ : List[str]   =						{
							    'prior': prior,
							    'image_encoder': image_encoder,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'scheduler': scheduler,
							    'image_processor': image_processor,
							}

							return components






				def 				_lowerCamelCase						(		self   : Any       ,	a   : Dict       ,	a   : Optional[Any]=0  ):






							'''simple docstring'''







							if str(lowerCamelCase__  ).startswith('mps'  ):
										lowerCAmelCase__ : str   =						torch.manual_seed(lowerCamelCase__  )
							else:
										lowerCAmelCase__ : Any   =						torch.Generator(device=lowerCamelCase__  ).manual_seed(lowerCamelCase__  )
							lowerCAmelCase__ : Tuple   =						{
							    'prompt': 'horse',
							    'generator': generator,
							    'guidance_scale': 4.0,
							    'num_inference_steps': 2,
							    'output_type': 'np',
							}
							return inputs






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						'cpu'

							lowerCAmelCase__ : str   =						self.get_dummy_components()

							lowerCAmelCase__ : Optional[int]   =						self.pipeline_class(**lowerCamelCase__  )
							lowerCAmelCase__ : List[Any]   =						pipe.to(lowerCamelCase__  )

							pipe.set_progress_bar_config(disable=lowerCamelCase__  )

							lowerCAmelCase__ : Union[str, Any]   =						pipe(**self.get_dummy_inputs(lowerCamelCase__  )  )
							lowerCAmelCase__ : str   =						output.image_embeds

							lowerCAmelCase__ : Optional[Any]   =						pipe(
							    **self.get_dummy_inputs(lowerCamelCase__  )       ,	return_dict=lowerCamelCase__       ,	)[0]

							lowerCAmelCase__ : Tuple   =						image[0, -10:]
							lowerCAmelCase__ : int   =						image_from_tuple[0, -10:]

							assert image.shape == (1, 32)

							lowerCAmelCase__ : Union[str, Any]   =						np.array(
							    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]  )

							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 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						torch_device == 'cpu'
							lowerCAmelCase__ : str   =						True
							lowerCAmelCase__ : Dict   =						False

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






				@skip_mps
				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						torch_device == 'cpu'
							lowerCAmelCase__ : List[Any]   =						False

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






import argparse

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


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			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."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


