import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


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






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Dict			  =		StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
        __UpperCamelCase     :List[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        sd_pipe.set_scheduler('''sample_euler''')

        __UpperCamelCase     :Tuple			  =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase     :Union[str, Any]			  =		torch.manual_seed(0)
        __UpperCamelCase     :List[str]			  =		sd_pipe([prompt] , generator=__lowercase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''')

        __UpperCamelCase     :str			  =		output.images

        __UpperCamelCase     :List[Any]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCamelCase     :Optional[int]			  =		np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39])

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







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Union[str, Any]			  =		StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
        __UpperCamelCase     :Optional[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        sd_pipe.set_scheduler('''sample_euler''')

        __UpperCamelCase     :Tuple			  =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase     :List[str]			  =		torch.manual_seed(0)
        __UpperCamelCase     :List[Any]			  =		sd_pipe([prompt] , generator=__lowercase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''')

        __UpperCamelCase     :int			  =		output.images

        __UpperCamelCase     :Dict			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCamelCase     :str			  =		np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-1







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Union[str, Any]			  =		StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
        __UpperCamelCase     :str			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        sd_pipe.set_scheduler('''sample_dpmpp_2m''')

        __UpperCamelCase     :Optional[int]			  =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase     :str			  =		torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		sd_pipe(
            [prompt] , generator=__lowercase , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=__lowercase , )

        __UpperCamelCase     :List[str]			  =		output.images

        __UpperCamelCase     :Union[str, Any]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCamelCase     :Tuple			  =		np.array(
            [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48])

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






"""simple docstring"""






import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		) ->    Optional[int]:
      '''simple docstring'''


      if config_name_or_path is None:
            lowercase														=	"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

      if generator_tokenizer_name_or_path is None:
            lowercase														=	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowercase														=	question_encoder_name_or_path

      lowercase														=	RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

      # Save model.
      lowercase														=	RagConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)

      lowercase														=	gen_config
      lowercase														=	question_encoder_config

      lowercase														=	model_class.from_pretrained_question_encoder_generator(
          lowerCAmelCase__					,		lowerCAmelCase__					,		config=lowerCAmelCase__		)
      rag_model.save_pretrained(lowerCAmelCase__		)

      # Sanity check.
      model_class.from_pretrained(lowerCAmelCase__		)

      # Save tokenizers.
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""		)
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""		)


if __name__ == "__main__":
       __lowerCAmelCase    :       int					     =argparse.ArgumentParser()
       parser.add_argument(
           """--model_type""",
           choices=["""rag_sequence""", """rag_token"""],
           required=True,
           type=str,
           help="""RAG model type: rag_sequence, rag_token""",
       )
       parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
       parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
       parser.add_argument(
           """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
       )

       parser.add_argument(
           """--generator_tokenizer_name_or_path""",
           type=str,
           help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
       )
       parser.add_argument(
           """--question_encoder_tokenizer_name_or_path""",
           type=str,
           help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
       )
       parser.add_argument(
           """--config_name_or_path""",
           type=str,
           help=(
               """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
               """ ``model_type``"""
           ),
       )

       __lowerCAmelCase    :       List[str]					     =parser.parse_args()

       __lowerCAmelCase    :       Dict					     =Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )



