

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Optional[int]       				=  IFInpaintingPipeline
	_lowercase  :   Optional[Any]       				=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	_lowercase  :   Union[str, Any]       				=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_lowercase  :   Union[str, Any]       				=  PipelineTesterMixin.required_optional_params - {"latents"}







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return self._get_dummy_components()







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: Tuple  , __UpperCamelCase: str=0      ):

						'''simple docstring'''





						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )

						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )

						__magic_name__							=    {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs







	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available()  , reason='XFormers attention is only available with CUDA and `xformers` installed'  , )
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != 'cuda'  , reason='float16 requires CUDA'      )
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						super().test_save_load_floataa(expected_max_diff=1E-1      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						self._test_save_load_local()







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2  , )







import argparse
from pathlib import Path

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




def 					_lowercase     (				a_				:					str					,a_				:					str					,a_				:					str					,a_				:					Path					,a_				:					str = None					,a_				:					str = None					,a_				:					str = None					,)				->	Tuple:




					'''simple docstring'''







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

					if generator_tokenizer_name_or_path is None:
										__magic_name__							=    generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										__magic_name__							=    question_encoder_name_or_path

					__magic_name__							=    RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

					# Save model.
					__magic_name__							=    RagConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)

					__magic_name__							=    gen_config
					__magic_name__							=    question_encoder_config

					__magic_name__							=    model_class.from_pretrained_question_encoder_generator(
					    a_					,a_					,config=a_				)
					rag_model.save_pretrained(a_				)

					# Sanity check.
					model_class.from_pretrained(a_				)

					# Save tokenizers.
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'				)
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'				)


if __name__ == "__main__":
						A__						=							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``"
						    ),
						)

						A__						=							parser.parse_args()

						A__						=							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,
						)




