

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

SCREAMING_SNAKE_CASE_					:	Tuple							  =						[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

SCREAMING_SNAKE_CASE_					:	Tuple							  =						[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

SCREAMING_SNAKE_CASE_					:	List[str]							  =						[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  SCREAMING_SNAKE_CASE_					:	Tuple							  =						f"""down_blocks.{i}.resnets.{j}."""
  SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						f"""input_blocks.{3*i + j + 1}.0."""
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   SCREAMING_SNAKE_CASE_					:	Dict							  =						f"""down_blocks.{i}.attentions.{j}."""
   SCREAMING_SNAKE_CASE_					:	int							  =						f"""input_blocks.{3*i + j + 1}.1."""
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  SCREAMING_SNAKE_CASE_					:	Dict							  =						f"""up_blocks.{i}.resnets.{j}."""
  SCREAMING_SNAKE_CASE_					:	int							  =						f"""output_blocks.{3*i + j}.0."""
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   SCREAMING_SNAKE_CASE_					:	Any							  =						f"""up_blocks.{i}.attentions.{j}."""
   SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						f"""output_blocks.{3*i + j}.1."""
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  SCREAMING_SNAKE_CASE_					:	Dict							  =						f"""down_blocks.{i}.downsamplers.0.conv."""
  SCREAMING_SNAKE_CASE_					:	int							  =						f"""input_blocks.{3*(i+1)}.0.op."""
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  SCREAMING_SNAKE_CASE_					:	Tuple							  =						f"""up_blocks.{i}.upsamplers.0."""
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						'''mid_block.attentions.0.'''
SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 SCREAMING_SNAKE_CASE_					:	List[str]							  =						f"""mid_block.resnets.{j}."""
 SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						f"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def  _snake_case     (    UpperCAmelCase_  :					List[Any]			):
     A__										=  {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          A__										=  sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    A__										=  v.replace(_lowercase					,   _lowercase			)
               A__										=  v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               A__										=  v.replace(_lowercase					,   _lowercase			)
          A__										=  v
     A__										=  {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


# ================#
# VAE Conversion #
# ================#

SCREAMING_SNAKE_CASE_					:	Any							  =						[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  SCREAMING_SNAKE_CASE_					:	List[str]							  =						f"""encoder.down_blocks.{i}.resnets.{j}."""
  SCREAMING_SNAKE_CASE_					:	Dict							  =						f"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  SCREAMING_SNAKE_CASE_					:	int							  =						f"""down_blocks.{i}.downsamplers.0."""
  SCREAMING_SNAKE_CASE_					:	List[Any]							  =						f"""down.{i}.downsample."""
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  SCREAMING_SNAKE_CASE_					:	Tuple							  =						f"""up_blocks.{i}.upsamplers.0."""
  SCREAMING_SNAKE_CASE_					:	List[str]							  =						f"""up.{3-i}.upsample."""
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						f"""decoder.up_blocks.{i}.resnets.{j}."""
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						f"""decoder.up.{3-i}.block.{j}."""
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 SCREAMING_SNAKE_CASE_					:	Dict							  =						f"""mid_block.resnets.{i}."""
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						f"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]





def  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     return w.reshape(*w.shape					,   1					,   1			)





def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]			):
     A__										=  {k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               A__										=  v.replace(_lowercase					,   _lowercase			)
          A__										=  v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    A__										=  v.replace(_lowercase					,   _lowercase			)
               A__										=  v
     A__										=  {v: vae_state_dict[k] for k, v in mapping.items()}
     A__										=  ["q", "k", "v", "proj_out"]
     for k, v in new_state_dict.items():
          for weight_name in weights_to_convert:
               if F"""mid.attn_1.{weight_name}.weight""" in k:
                    print(F"""Reshaping {k} for SD format"""			)
                    A__										=  reshape_weight_for_sd(_lowercase			)
     return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
SCREAMING_SNAKE_CASE_					:	Dict							  =						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
SCREAMING_SNAKE_CASE_					:	str							  =						re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						{'''q''': 0, '''k''': 1, '''v''': 2}





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  {}
     A__										=  {}
     A__										=  {}
     for k, v in text_enc_dict.items():
          if (
              k.endswith(""".self_attn.q_proj.weight"""			)
              or k.endswith(""".self_attn.k_proj.weight"""			)
              or k.endswith(""".self_attn.v_proj.weight"""			)
          ):
               A__										=  k[: -len(""".q_proj.weight"""			)]
               A__										=  k[-len("""q_proj.weight"""			)]
               if k_pre not in capture_qkv_weight:
                    A__										=  [None, None, None]
               A__										=  v
               continue

          if (
              k.endswith(""".self_attn.q_proj.bias"""			)
              or k.endswith(""".self_attn.k_proj.bias"""			)
              or k.endswith(""".self_attn.v_proj.bias"""			)
          ):
               A__										=  k[: -len(""".q_proj.bias"""			)]
               A__										=  k[-len("""q_proj.bias"""			)]
               if k_pre not in capture_qkv_bias:
                    A__										=  [None, None, None]
               A__										=  v
               continue

          A__										=  textenc_pattern.sub(lambda UpperCAmelCase_			: protected[re.escape(m.group(0			)			)]					,   _lowercase			)
          A__										=  v

     for k_pre, tensors in capture_qkv_weight.items():
          if None in tensors:
               raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
          A__										=  textenc_pattern.sub(lambda UpperCAmelCase_			: protected[re.escape(m.group(0			)			)]					,   _lowercase			)
          A__										=  torch.cat(_lowercase			)

     for k_pre, tensors in capture_qkv_bias.items():
          if None in tensors:
               raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
          A__										=  textenc_pattern.sub(lambda UpperCAmelCase_			: protected[re.escape(m.group(0			)			)]					,   _lowercase			)
          A__										=  torch.cat(_lowercase			)

     return new_state_dict





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]			):
     return text_enc_dict


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	List[str]							  =						argparse.ArgumentParser()

 parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
 parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
 parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
 parser.add_argument(
     '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
 )

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

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 SCREAMING_SNAKE_CASE_					:	Any							  =						osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
 SCREAMING_SNAKE_CASE_					:	Dict							  =						osp.join(args.model_path, 'text_encoder', 'model.safetensors')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  SCREAMING_SNAKE_CASE_					:	Dict							  =						load_file(unet_path, device='cpu')
 else:
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
  SCREAMING_SNAKE_CASE_					:	int							  =						torch.load(unet_path, map_location='cpu')

 if osp.exists(vae_path):
  SCREAMING_SNAKE_CASE_					:	Dict							  =						load_file(vae_path, device='cpu')
 else:
  SCREAMING_SNAKE_CASE_					:	Any							  =						osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
  SCREAMING_SNAKE_CASE_					:	str							  =						torch.load(vae_path, map_location='cpu')

 if osp.exists(text_enc_path):
  SCREAMING_SNAKE_CASE_					:	str							  =						load_file(text_enc_path, device='cpu')
 else:
  SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
  SCREAMING_SNAKE_CASE_					:	Dict							  =						torch.load(text_enc_path, map_location='cpu')

 # Convert the UNet model
 SCREAMING_SNAKE_CASE_					:	Any							  =						convert_unet_state_dict(unet_state_dict)
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						convert_vae_state_dict(vae_state_dict)
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  SCREAMING_SNAKE_CASE_					:	int							  =						{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  SCREAMING_SNAKE_CASE_					:	List[Any]							  =						convert_text_enc_state_dict_vaa(text_enc_dict)
  SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  SCREAMING_SNAKE_CASE_					:	str							  =						convert_text_enc_state_dict(text_enc_dict)
  SCREAMING_SNAKE_CASE_					:	List[Any]							  =						{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 SCREAMING_SNAKE_CASE_					:	Any							  =						{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  SCREAMING_SNAKE_CASE_					:	str							  =						{'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)









"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






