


"""simple docstring"""







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _A						(						lowercase	,   lowercase=1		):







    """simple docstring"""
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('''.'''		)[n_shave_prefix_segments:]		)
    else:
        return ".".join(path.split('''.'''		)[:n_shave_prefix_segments]		)
def _A						(						lowercase	,   lowercase=0		):







    """simple docstring"""
    a					=[]
    for old_item in old_list:
        a					=old_item.replace('''in_layers.0'''	,   '''norm1'''		)
        a					=new_item.replace('''in_layers.2'''	,   '''conv1'''		)

        a					=new_item.replace('''out_layers.0'''	,   '''norm2'''		)
        a					=new_item.replace('''out_layers.3'''	,   '''conv2'''		)

        a					=new_item.replace('''emb_layers.1'''	,   '''time_emb_proj'''		)
        a					=new_item.replace('''skip_connection'''	,   '''conv_shortcut'''		)

        a					=shave_segments(lowercase	,   n_shave_prefix_segments=lowercase		)

        mapping.append({'''old''': old_item, '''new''': new_item}		)

    return mapping
def _A						(						lowercase	,   lowercase=0		):







    """simple docstring"""
    a					=[]
    for old_item in old_list:
        a					=old_item

        a					=new_item.replace('''norm.weight'''	,   '''group_norm.weight'''		)
        a					=new_item.replace('''norm.bias'''	,   '''group_norm.bias'''		)

        a					=new_item.replace('''proj_out.weight'''	,   '''proj_attn.weight'''		)
        a					=new_item.replace('''proj_out.bias'''	,   '''proj_attn.bias'''		)

        a					=shave_segments(lowercase	,   n_shave_prefix_segments=lowercase		)

        mapping.append({'''old''': old_item, '''new''': new_item}		)

    return mapping
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase=None	,   lowercase=None	,   lowercase=None		):







    """simple docstring"""
    assert isinstance(lowercase	,   lowercase		), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            a					=old_checkpoint[path]
            a					=old_tensor.shape[0] // 3

            a					=(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

            a					=old_tensor.shape[0] // config['''num_head_channels'''] // 3

            a					=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
            a		,				a		,				a					=old_tensor.split(channels // num_heads	,   dim=1		)

            a					=query.reshape(lowercase		)
            a					=key.reshape(lowercase		)
            a					=value.reshape(lowercase		)

    for path in paths:
        a					=path['''new''']

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        a					=new_path.replace('''middle_block.0'''	,   '''mid_block.resnets.0'''		)
        a					=new_path.replace('''middle_block.1'''	,   '''mid_block.attentions.0'''		)
        a					=new_path.replace('''middle_block.2'''	,   '''mid_block.resnets.1'''		)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                a					=new_path.replace(replacement['''old''']	,   replacement['''new''']		)

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            a					=old_checkpoint[path['''old''']][:, :, 0]
        else:
            a					=old_checkpoint[path['''old''']]
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					={}

    a					=checkpoint['''time_embed.0.weight''']
    a					=checkpoint['''time_embed.0.bias''']
    a					=checkpoint['''time_embed.2.weight''']
    a					=checkpoint['''time_embed.2.bias''']

    a					=checkpoint['''input_blocks.0.0.weight''']
    a					=checkpoint['''input_blocks.0.0.bias''']

    a					=checkpoint['''out.0.weight''']
    a					=checkpoint['''out.0.bias''']
    a					=checkpoint['''out.2.weight''']
    a					=checkpoint['''out.2.bias''']

    # Retrieves the keys for the input blocks only
    a					=len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''input_blocks''' in layer}		)
    a					={
        layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
        for layer_id in range(lowercase		)
    }

    # Retrieves the keys for the middle blocks only
    a					=len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''middle_block''' in layer}		)
    a					={
        layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
        for layer_id in range(lowercase		)
    }

    # Retrieves the keys for the output blocks only
    a					=len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''output_blocks''' in layer}		)
    a					={
        layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
        for layer_id in range(lowercase		)
    }

    for i in range(1	,   lowercase		):
        a					=(i - 1) // (config['''num_res_blocks'''] + 1)
        a					=(i - 1) % (config['''num_res_blocks'''] + 1)

        a					=[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
        a					=[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

        if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
            a					=checkpoint[
                f'''input_blocks.{i}.0.op.weight'''
            ]
            a					=checkpoint[
                f'''input_blocks.{i}.0.op.bias'''
            ]
            continue

        a					=renew_resnet_paths(lowercase		)
        a					={'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
        a					={'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
        assign_to_checkpoint(
            lowercase	,   lowercase	,   lowercase	,   additional_replacements=[meta_path, resnet_op]	,   config=lowercase		)

        if len(lowercase		):
            a					=renew_attention_paths(lowercase		)
            a					={
                '''old''': f'''input_blocks.{i}.1''',
                '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
            }
            a					={
                f'''input_blocks.{i}.1.qkv.bias''': {
                    '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                    '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                    '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                },
                f'''input_blocks.{i}.1.qkv.weight''': {
                    '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                    '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                    '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                },
            }
            assign_to_checkpoint(
                lowercase	,   lowercase	,   lowercase	,   additional_replacements=[meta_path]	,   attention_paths_to_split=lowercase	,   config=lowercase	,   )

    a					=middle_blocks[0]
    a					=middle_blocks[1]
    a					=middle_blocks[2]

    a					=renew_resnet_paths(lowercase		)
    assign_to_checkpoint(lowercase	,   lowercase	,   lowercase	,   config=lowercase		)

    a					=renew_resnet_paths(lowercase		)
    assign_to_checkpoint(lowercase	,   lowercase	,   lowercase	,   config=lowercase		)

    a					=renew_attention_paths(lowercase		)
    a					={
        '''middle_block.1.qkv.bias''': {
            '''key''': '''mid_block.attentions.0.key.bias''',
            '''query''': '''mid_block.attentions.0.query.bias''',
            '''value''': '''mid_block.attentions.0.value.bias''',
        },
        '''middle_block.1.qkv.weight''': {
            '''key''': '''mid_block.attentions.0.key.weight''',
            '''query''': '''mid_block.attentions.0.query.weight''',
            '''value''': '''mid_block.attentions.0.value.weight''',
        },
    }
    assign_to_checkpoint(
        lowercase	,   lowercase	,   lowercase	,   attention_paths_to_split=lowercase	,   config=lowercase		)

    for i in range(lowercase		):
        a					=i // (config['''num_res_blocks'''] + 1)
        a					=i % (config['''num_res_blocks'''] + 1)
        a					=[shave_segments(lowercase	,   2		) for name in output_blocks[i]]
        a					={}

        for layer in output_block_layers:
            a		,				a					=layer.split('''.'''		)[0], shave_segments(lowercase	,   1		)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(lowercase		)
            else:
                a					=[layer_name]

        if len(lowercase		) > 1:
            a					=[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
            a					=[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

            a					=renew_resnet_paths(lowercase		)
            a					=renew_resnet_paths(lowercase		)

            a					={'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            assign_to_checkpoint(lowercase	,   lowercase	,   lowercase	,   additional_replacements=[meta_path]	,   config=lowercase		)

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                a					=list(output_block_list.values()		).index(['''conv.weight''', '''conv.bias''']		)
                a					=checkpoint[
                    f'''output_blocks.{i}.{index}.conv.weight'''
                ]
                a					=checkpoint[
                    f'''output_blocks.{i}.{index}.conv.bias'''
                ]

                # Clear attentions as they have been attributed above.
                if len(lowercase		) == 2:
                    a					=[]

            if len(lowercase		):
                a					=renew_attention_paths(lowercase		)
                a					={
                    '''old''': f'''output_blocks.{i}.1''',
                    '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                }
                a					={
                    f'''output_blocks.{i}.1.qkv.bias''': {
                        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                    },
                    f'''output_blocks.{i}.1.qkv.weight''': {
                        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                    },
                }
                assign_to_checkpoint(
                    lowercase	,   lowercase	,   lowercase	,   additional_replacements=[meta_path]	,   attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions		) else None	,   config=lowercase	,   )
        else:
            a					=renew_resnet_paths(lowercase	,   n_shave_prefix_segments=1		)
            for path in resnet_0_paths:
                a					='''.'''.join(['''output_blocks''', str(lowercase		), path['''old''']]		)
                a					='''.'''.join(['''up_blocks''', str(lowercase		), '''resnets''', str(lowercase		), path['''new''']]		)

                a					=checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
       lowerCamelCase_       :       int      						=  argparse.ArgumentParser()

       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
       )

       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the architecture.""",
       )

       parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

       lowerCamelCase_       :       Optional[int]      						=  parser.parse_args()

       lowerCamelCase_       :       int      						=  torch.load(args.checkpoint_path)

       with open(args.config_file) as f:
              lowerCamelCase_       :       str      						=  json.loads(f.read())

       lowerCamelCase_       :       Any      						=  convert_ldm_checkpoint(checkpoint, config)

       if "ldm" in config:
              del config["ldm"]

       lowerCamelCase_       :       Any      						=  UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              lowerCamelCase_       :       int      						=  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
              lowerCamelCase_       :       Dict      						=  VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

              lowerCamelCase_       :       Any      						=  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
              pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
              model.save_pretrained(args.dump_path)



"""simple docstring"""







import os
import sys
import unittest


lowerCamelCase_       :       Tuple      						=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")






class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=get_test_to_tester_mapping(__A						)
     a					=get_test_to_tester_mapping(__A						)

     a					={'''BertModelTest''': '''BertModelTester'''}

     a					={
         '''BlipModelTest''': '''BlipModelTester''',
         '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
         '''BlipTextModelTest''': '''BlipTextModelTester''',
         '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
         '''BlipVQAModelTest''': '''BlipVQAModelTester''',
         '''BlipVisionModelTest''': '''BlipVisionModelTester''',
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=get_model_to_test_mapping(__A						)
     a					=get_model_to_test_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTest'''],
         '''BertForMultipleChoice''': ['''BertModelTest'''],
         '''BertForNextSentencePrediction''': ['''BertModelTest'''],
         '''BertForPreTraining''': ['''BertModelTest'''],
         '''BertForQuestionAnswering''': ['''BertModelTest'''],
         '''BertForSequenceClassification''': ['''BertModelTest'''],
         '''BertForTokenClassification''': ['''BertModelTest'''],
         '''BertLMHeadModel''': ['''BertModelTest'''],
         '''BertModel''': ['''BertModelTest'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
         '''BlipModel''': ['''BlipModelTest'''],
         '''BlipTextModel''': ['''BlipTextModelTest'''],
         '''BlipVisionModel''': ['''BlipVisionModelTest'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=get_model_to_tester_mapping(__A						)
     a					=get_model_to_tester_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTester'''],
         '''BertForMultipleChoice''': ['''BertModelTester'''],
         '''BertForNextSentencePrediction''': ['''BertModelTester'''],
         '''BertForPreTraining''': ['''BertModelTester'''],
         '''BertForQuestionAnswering''': ['''BertModelTester'''],
         '''BertForSequenceClassification''': ['''BertModelTester'''],
         '''BertForTokenClassification''': ['''BertModelTester'''],
         '''BertLMHeadModel''': ['''BertModelTester'''],
         '''BertModel''': ['''BertModelTester'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
         '''BlipModel''': ['''BlipModelTester'''],
         '''BlipTextModel''': ['''BlipTextModelTester'''],
         '''BlipVisionModel''': ['''BlipVisionModelTester'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)