




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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       List[str]			=    KandinskyPriorPipeline
      lowerCAmelCase__	:       Union[str, Any]			=    ["prompt"]
      lowerCAmelCase__	:       Optional[Any]			=    ["prompt", "negative_prompt"]
      lowerCAmelCase__	:       Dict			=    [
          "num_images_per_prompt",
          "generator",
          "num_inference_steps",
          "latents",
          "negative_prompt",
          "guidance_scale",
          "output_type",
          "return_dict",
      ]
      lowerCAmelCase__	:       List[str]			=    False







      @property
      def 					snake_case		(   self    :       str ):
          return 32







      @property
      def 					snake_case		(   self    :       Dict ):
          return 32







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          return self.time_input_dim







      @property
      def 					snake_case		(   self    :       str ):
          return self.time_input_dim * 4







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          return 100







      @property
      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
          return tokenizer







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          torch.manual_seed(0 )
          __UpperCamelCase     						=  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(snake_case )







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          torch.manual_seed(0 )

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

          __UpperCamelCase     						=  PriorTransformer(**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
          __UpperCamelCase     						=  nn.Parameter(torch.ones(model.clip_std.shape ) )
          return model







      @property
      def 					snake_case		(   self    :       Any ):
          torch.manual_seed(0 )
          __UpperCamelCase     						=  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       ,				)

          __UpperCamelCase     						=  CLIPVisionModelWithProjection(snake_case )
          return model







      @property
      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  CLIPImageProcessor(
              crop_size=224       ,				do_center_crop=snake_case       ,				do_normalize=snake_case       ,				do_resize=snake_case       ,				image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,				image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,				resample=3       ,				size=224       ,				)

          return image_processor







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.dummy_prior
          __UpperCamelCase     						=  self.dummy_image_encoder
          __UpperCamelCase     						=  self.dummy_text_encoder
          __UpperCamelCase     						=  self.dummy_tokenizer
          __UpperCamelCase     						=  self.dummy_image_processor

          __UpperCamelCase     						=  UnCLIPScheduler(
              variance_type='''fixed_small_log'''       ,				prediction_type='''sample'''       ,				num_train_timesteps=1000       ,				clip_sample=snake_case       ,				clip_sample_range=10.0       ,				)

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

          return components







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







      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  '''cpu'''

          __UpperCamelCase     						=  self.get_dummy_components()

          __UpperCamelCase     						=  self.pipeline_class(**snake_case )
          __UpperCamelCase     						=  pipe.to(snake_case )

          pipe.set_progress_bar_config(disable=snake_case )

          __UpperCamelCase     						=  pipe(**self.get_dummy_inputs(snake_case ) )
          __UpperCamelCase     						=  output.image_embeds

          __UpperCamelCase     						=  pipe(
              **self.get_dummy_inputs(snake_case )       ,				return_dict=snake_case       ,				)[0]

          __UpperCamelCase     						=  image[0, -10:]
          __UpperCamelCase     						=  image_from_tuple[0, -10:]

          assert image.shape == (1, 32)

          __UpperCamelCase     						=  np.array(
              [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56] )

          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 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  torch_device == '''cpu'''
          __UpperCamelCase     						=  True
          __UpperCamelCase     						=  False

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







      @skip_mps
      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  torch_device == '''cpu'''
          __UpperCamelCase     						=  False

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







import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



