

'''simple docstring'''






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowerCAmelCase__      		=			[
    # (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'''),
]

lowerCAmelCase__      		=			[
    # (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'''),
]

lowerCAmelCase__      		=			[]
# 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
        lowerCAmelCase__      		=			f'down_blocks.{i}.resnets.{j}.'
        lowerCAmelCase__      		=			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
            lowerCAmelCase__      		=			f'down_blocks.{i}.attentions.{j}.'
            lowerCAmelCase__      		=			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
        lowerCAmelCase__      		=			f'up_blocks.{i}.resnets.{j}.'
        lowerCAmelCase__      		=			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
            lowerCAmelCase__      		=			f'up_blocks.{i}.attentions.{j}.'
            lowerCAmelCase__      		=			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
        lowerCAmelCase__      		=			f'down_blocks.{i}.downsamplers.0.conv.'
        lowerCAmelCase__      		=			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
        lowerCAmelCase__      		=			f'up_blocks.{i}.upsamplers.0.'
        lowerCAmelCase__      		=			f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase__      		=			'''mid_block.attentions.0.'''
lowerCAmelCase__      		=			'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    lowerCAmelCase__      		=			f'mid_block.resnets.{j}.'
    lowerCAmelCase__      		=			f'middle_block.{2*j}.'
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def 	_A  (					A__   ):






 """simple docstring"""



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


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

lowerCAmelCase__      		=			[
    # (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):
        lowerCAmelCase__      		=			f'encoder.down_blocks.{i}.resnets.{j}.'
        lowerCAmelCase__      		=			f'encoder.down.{i}.block.{j}.'
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        lowerCAmelCase__      		=			f'down_blocks.{i}.downsamplers.0.'
        lowerCAmelCase__      		=			f'down.{i}.downsample.'
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

        lowerCAmelCase__      		=			f'up_blocks.{i}.upsamplers.0.'
        lowerCAmelCase__      		=			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):
        lowerCAmelCase__      		=			f'decoder.up_blocks.{i}.resnets.{j}.'
        lowerCAmelCase__      		=			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):
    lowerCAmelCase__      		=			f'mid_block.resnets.{i}.'
    lowerCAmelCase__      		=			f'mid.block_{i+1}.'
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


def 	_A  (					A__   ):






 """simple docstring"""



 return w.reshape(*w.shape  ,       1  ,       1   )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				{k: k for k in vae_state_dict.keys()}
 for k, v in mapping.items():
  for sd_part, hf_part in vae_conversion_map:
   __lowercase													=				v.replace(A__  ,       A__   )
  __lowercase													=				v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    __lowercase													=				v.replace(A__  ,       A__   )
   __lowercase													=				v
 __lowercase													=				{v: vae_state_dict[k] for k, v in mapping.items()}
 __lowercase													=				['''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"   )
    __lowercase													=				reshape_weight_for_sd(A__   )
 return new_state_dict


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


lowerCAmelCase__      		=			[
    # (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'''),
]
lowerCAmelCase__      		=			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase__      		=			re.compile('''|'''.join(protected.keys()))

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


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				{}
 __lowercase													=				{}
 __lowercase													=				{}
 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'''   )
  ):
   __lowercase													=				k[: -len('''.q_proj.weight'''   )]
   __lowercase													=				k[-len('''q_proj.weight'''   )]
   if k_pre not in capture_qkv_weight:
    __lowercase													=				[None, None, None]
   __lowercase													=				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'''   )
  ):
   __lowercase													=				k[: -len('''.q_proj.bias'''   )]
   __lowercase													=				k[-len('''q_proj.bias'''   )]
   if k_pre not in capture_qkv_bias:
    __lowercase													=				[None, None, None]
   __lowercase													=				v
   continue

  __lowercase													=				textenc_pattern.sub(lambda A__   : protected[re.escape(m.group(0   )   )]  ,       A__   )
  __lowercase													=				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'''   )
  __lowercase													=				textenc_pattern.sub(lambda A__   : protected[re.escape(m.group(0   )   )]  ,       A__   )
  __lowercase													=				torch.cat(A__   )

 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'''   )
  __lowercase													=				textenc_pattern.sub(lambda A__   : protected[re.escape(m.group(0   )   )]  ,       A__   )
  __lowercase													=				torch.cat(A__   )

 return new_state_dict


def 	_A  (					A__   ):






 """simple docstring"""



 return text_enc_dict


if __name__ == "__main__":
    lowerCAmelCase__      		=			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.'''
    )

    lowerCAmelCase__      		=			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
    lowerCAmelCase__      		=			osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
    lowerCAmelCase__      		=			osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
    lowerCAmelCase__      		=			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):
        lowerCAmelCase__      		=			load_file(unet_path, device='''cpu''')
    else:
        lowerCAmelCase__      		=			osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
        lowerCAmelCase__      		=			torch.load(unet_path, map_location='''cpu''')

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

    if osp.exists(text_enc_path):
        lowerCAmelCase__      		=			load_file(text_enc_path, device='''cpu''')
    else:
        lowerCAmelCase__      		=			osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
        lowerCAmelCase__      		=			torch.load(text_enc_path, map_location='''cpu''')

    # Convert the UNet model
    lowerCAmelCase__      		=			convert_unet_state_dict(unet_state_dict)
    lowerCAmelCase__      		=			{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

    # Convert the VAE model
    lowerCAmelCase__      		=			convert_vae_state_dict(vae_state_dict)
    lowerCAmelCase__      		=			{'''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
    lowerCAmelCase__      		=			'''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
        lowerCAmelCase__      		=			{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
        lowerCAmelCase__      		=			convert_text_enc_state_dict_vaa(text_enc_dict)
        lowerCAmelCase__      		=			{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
    else:
        lowerCAmelCase__      		=			convert_text_enc_state_dict(text_enc_dict)
        lowerCAmelCase__      		=			{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

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

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




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

