

'''simple docstring'''

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class        A_ :






     '''simple docstring'''



     def    UpperCAmelCase_       (		self :     Dict       )						->	Union[str, Any]:
      torch.manual_seed(0       )
      UpperCAmelCase       :	Tuple											= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'       )

      torch.manual_seed(0       )
      UpperCAmelCase       :	List[Any]											= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'       )

      torch.manual_seed(0       )
      UpperCAmelCase       :	Any											= UNetaDConditionModel(
          sample_size=32     ,			layers_per_block=1     ,			block_out_channels=[32, 64]     ,			down_block_types=[
              'ResnetDownsampleBlock2D',
              'SimpleCrossAttnDownBlock2D',
          ]     ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,			in_channels=3     ,			out_channels=6     ,			cross_attention_dim=32     ,			encoder_hid_dim=32     ,			attention_head_dim=8     ,			addition_embed_type='text'     ,			addition_embed_type_num_heads=2     ,			cross_attention_norm='group_norm'     ,			resnet_time_scale_shift='scale_shift'     ,			act_fn='gelu'     ,			)
      unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

      torch.manual_seed(0       )
      UpperCAmelCase       :	Dict											= DDPMScheduler(
          num_train_timesteps=1_000     ,			beta_schedule='squaredcos_cap_v2'     ,			beta_start=0.0001     ,			beta_end=0.02     ,			thresholding=__lowerCAmelCase     ,			dynamic_thresholding_ratio=0.95     ,			sample_max_value=1.0     ,			prediction_type='epsilon'     ,			variance_type='learned_range'     ,			)

      torch.manual_seed(0       )
      UpperCAmelCase       :	List[str]											= IFWatermarker()

      return {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "watermarker": watermarker,
          "safety_checker": None,
          "feature_extractor": None,
      }


     def    UpperCAmelCase_       (		self :     Dict       )						->	List[str]:
      torch.manual_seed(0       )
      UpperCAmelCase       :	Optional[int]											= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'       )

      torch.manual_seed(0       )
      UpperCAmelCase       :	List[Any]											= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'       )

      torch.manual_seed(0       )
      UpperCAmelCase       :	Any											= UNetaDConditionModel(
          sample_size=32     ,			layers_per_block=[1, 2]     ,			block_out_channels=[32, 64]     ,			down_block_types=[
              'ResnetDownsampleBlock2D',
              'SimpleCrossAttnDownBlock2D',
          ]     ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,			in_channels=6     ,			out_channels=6     ,			cross_attention_dim=32     ,			encoder_hid_dim=32     ,			attention_head_dim=8     ,			addition_embed_type='text'     ,			addition_embed_type_num_heads=2     ,			cross_attention_norm='group_norm'     ,			resnet_time_scale_shift='scale_shift'     ,			act_fn='gelu'     ,			class_embed_type='timestep'     ,			mid_block_scale_factor=1.414     ,			time_embedding_act_fn='gelu'     ,			time_embedding_dim=32     ,			)
      unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

      torch.manual_seed(0       )
      UpperCAmelCase       :	Tuple											= DDPMScheduler(
          num_train_timesteps=1_000     ,			beta_schedule='squaredcos_cap_v2'     ,			beta_start=0.0001     ,			beta_end=0.02     ,			thresholding=__lowerCAmelCase     ,			dynamic_thresholding_ratio=0.95     ,			sample_max_value=1.0     ,			prediction_type='epsilon'     ,			variance_type='learned_range'     ,			)

      torch.manual_seed(0       )
      UpperCAmelCase       :	Any											= DDPMScheduler(
          num_train_timesteps=1_000     ,			beta_schedule='squaredcos_cap_v2'     ,			beta_start=0.0001     ,			beta_end=0.02     ,			)

      torch.manual_seed(0       )
      UpperCAmelCase       :	Union[str, Any]											= IFWatermarker()

      return {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "image_noising_scheduler": image_noising_scheduler,
          "watermarker": watermarker,
          "safety_checker": None,
          "feature_extractor": None,
      }


     def    UpperCAmelCase_       (		self :     Tuple       )						->	Optional[Any]:
      UpperCAmelCase       :	int											= self.get_dummy_components()
      UpperCAmelCase       :	int											= self.pipeline_class(**__lowerCAmelCase       )
      pipe.to(__lowerCAmelCase       )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase       )

      UpperCAmelCase       :	Dict											= self.get_dummy_inputs(__lowerCAmelCase       )

      UpperCAmelCase       :	int											= inputs['prompt']
      UpperCAmelCase       :	str											= inputs['generator']
      UpperCAmelCase       :	List[str]											= inputs['num_inference_steps']
      UpperCAmelCase       :	int											= inputs['output_type']

      if "image" in inputs:
       UpperCAmelCase       :	Dict											= inputs['image']
      else:
       UpperCAmelCase       :	Union[str, Any]											= None

      if "mask_image" in inputs:
       UpperCAmelCase       :	Union[str, Any]											= inputs['mask_image']
      else:
       UpperCAmelCase       :	str											= None

      if "original_image" in inputs:
       UpperCAmelCase       :	Optional[int]											= inputs['original_image']
      else:
       UpperCAmelCase       :	Union[str, Any]											= None

      UpperCAmelCase					, UpperCAmelCase       :	Union[str, Any]											= pipe.encode_prompt(__lowerCAmelCase       )

      # inputs with prompt converted to embeddings
      UpperCAmelCase       :	List[str]											= {
          'prompt_embeds': prompt_embeds,
          'negative_prompt_embeds': negative_prompt_embeds,
          'generator': generator,
          'num_inference_steps': num_inference_steps,
          'output_type': output_type,
      }

      if image is not None:
       UpperCAmelCase       :	Tuple											= image

      if mask_image is not None:
       UpperCAmelCase       :	Optional[int]											= mask_image

      if original_image is not None:
       UpperCAmelCase       :	List[Any]											= original_image

      # set all optional components to None
      for optional_component in pipe._optional_components:
       setattr(__lowerCAmelCase     ,			__lowerCAmelCase     ,			__lowerCAmelCase       )

      UpperCAmelCase       :	Optional[Any]											= pipe(**__lowerCAmelCase       )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(__lowerCAmelCase       )
       UpperCAmelCase       :	str											= self.pipeline_class.from_pretrained(__lowerCAmelCase       )
       pipe_loaded.to(__lowerCAmelCase       )
       pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase       )

      pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

      for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(__lowerCAmelCase     ,			__lowerCAmelCase       ) is None     ,			f"""`{optional_component}` did not stay set to None after loading."""     ,			)

      UpperCAmelCase       :	Any											= self.get_dummy_inputs(__lowerCAmelCase       )

      UpperCAmelCase       :	Dict											= inputs['generator']
      UpperCAmelCase       :	Tuple											= inputs['num_inference_steps']
      UpperCAmelCase       :	Tuple											= inputs['output_type']

      # inputs with prompt converted to embeddings
      UpperCAmelCase       :	Optional[int]											= {
          'prompt_embeds': prompt_embeds,
          'negative_prompt_embeds': negative_prompt_embeds,
          'generator': generator,
          'num_inference_steps': num_inference_steps,
          'output_type': output_type,
      }

      if image is not None:
       UpperCAmelCase       :	Optional[int]											= image

      if mask_image is not None:
       UpperCAmelCase       :	str											= mask_image

      if original_image is not None:
       UpperCAmelCase       :	List[Any]											= original_image

      UpperCAmelCase       :	Any											= pipe_loaded(**__lowerCAmelCase       )[0]

      UpperCAmelCase       :	str											= np.abs(to_np(__lowerCAmelCase       ) - to_np(__lowerCAmelCase       )       ).max()
      self.assertLess(__lowerCAmelCase     ,			1E-4       )




     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	int:
      UpperCAmelCase       :	List[str]											= self.get_dummy_components()
      UpperCAmelCase       :	Optional[int]											= self.pipeline_class(**__lowerCAmelCase       )
      pipe.to(__lowerCAmelCase       )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase       )

      UpperCAmelCase       :	List[Any]											= self.get_dummy_inputs(__lowerCAmelCase       )
      UpperCAmelCase       :	List[Any]											= pipe(**__lowerCAmelCase       )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(__lowerCAmelCase       )
       UpperCAmelCase       :	Union[str, Any]											= self.pipeline_class.from_pretrained(__lowerCAmelCase       )
       pipe_loaded.to(__lowerCAmelCase       )
       pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase       )

      pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

      UpperCAmelCase       :	Dict											= self.get_dummy_inputs(__lowerCAmelCase       )
      UpperCAmelCase       :	Union[str, Any]											= pipe_loaded(**__lowerCAmelCase       )[0]

      UpperCAmelCase       :	Any											= np.abs(to_np(__lowerCAmelCase       ) - to_np(__lowerCAmelCase       )       ).max()
      self.assertLess(__lowerCAmelCase     ,			1E-4       )





"""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,
       )



