



import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case						:Dict						=					logging.get_logger(__name__)


__snake_case						:Dict						=					'''https://openaipublic.azureedge.net/jukebox/models/'''
__snake_case						:Union[str, Any]						=					{
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}





def 					__snake_case     (      _UpperCAmelCase    ):
    if key.endswith('''.model.1.bias'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.1.bias'''       ,			'''.conv1d_1.bias'''    )
    elif key.endswith('''.model.1.weight'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.1.weight'''       ,			'''.conv1d_1.weight'''    )
    elif key.endswith('''.model.3.bias'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.3.bias'''       ,			'''.conv1d_2.bias'''    )
    elif key.endswith('''.model.3.weight'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.3.weight'''       ,			'''.conv1d_2.weight'''    )

    if "conditioner_blocks.0." in key:
        __a							=    key.replace('''conditioner_blocks.0'''       ,			'''conditioner_blocks'''    )

    if "prime_prior" in key:
        __a							=    key.replace('''prime_prior'''       ,			'''encoder'''    )

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        __a							=    key.replace('''.emb.'''       ,			'''.'''    )

    if key.endswith('''k'''    ):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace('''.k'''       ,			'''.codebook'''    )
    if "y_emb." in key:
        return key.replace('''y_emb.'''       ,			'''metadata_embedding.'''    )

    if "x_emb.emb." in key:
        __a							=    key.replace('''0.x_emb.emb'''       ,			'''embed_tokens'''    )

    if "prime_state_ln" in key:
        return key.replace('''prime_state_ln'''       ,			'''encoder.final_layer_norm'''    )
    if ".ln" in key:
        return key.replace('''.ln'''       ,			'''.layer_norm'''    )
    if "_ln" in key:
        return key.replace('''_ln'''       ,			'''_layer_norm'''    )

    if "prime_state_proj" in key:
        return key.replace('''prime_state_proj'''       ,			'''encoder.proj_in'''    )
    if "prime_x_out" in key:
        return key.replace('''prime_x_out'''       ,			'''encoder.lm_head'''    )
    if "prior.x_out" in key:
        return key.replace('''x_out'''       ,			'''fc_proj_out'''    )
    if "x_emb" in key:
        return key.replace('''x_emb'''       ,			'''embed_tokens'''    )

    return key





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    {}
    import re

    __a							=    re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''    )
    __a							=    re.compile(
        R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
    __a							=    re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''    )

    __a							=    re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''    )
    __a							=    re.compile(
        R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
    __a							=    re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''    )

    __a							=    re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''    )
    __a							=    re.compile(
        R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
    __a							=    re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''    )

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(_UpperCAmelCase    ):
            __a							=    re_encoder_block_conv_in.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    )
            __a							=    f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
            __a							=    re_encoder_block_conv_in.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_encoder_block_resnet.fullmatch(_UpperCAmelCase    ):
            __a							=    re_encoder_block_resnet.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    )
            __a							=    {'''1''': 1, '''3''': 2}[groups[-2]]
            __a							=    f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
            __a							=    f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            __a							=    prefix + resnet_block
            __a							=    re_encoder_block_resnet.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_encoder_block_proj_out.fullmatch(_UpperCAmelCase    ):
            __a							=    re_encoder_block_proj_out.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
            __a							=    re_encoder_block_proj_out.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_UpperCAmelCase    ):
            __a							=    re_decoder_block_conv_out.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    ) - 2
            __a							=    f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
            __a							=    re_decoder_block_conv_out.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_decoder_block_resnet.fullmatch(_UpperCAmelCase    ):
            __a							=    re_decoder_block_resnet.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    ) - 2
            __a							=    {'''1''': 1, '''3''': 2}[groups[-2]]
            __a							=    f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
            __a							=    f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            __a							=    prefix + resnet_block
            __a							=    re_decoder_block_resnet.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_decoder_block_proj_in.fullmatch(_UpperCAmelCase    ):
            __a							=    re_decoder_block_proj_in.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
            __a							=    re_decoder_block_proj_in.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_UpperCAmelCase    ):
            __a							=    re_prior_cond_conv_out.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[1]    ) * 2 + int(groups[2]    ) - 2
            __a							=    f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
            __a							=    re_prior_cond_conv_out.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_prior_cond_resnet.fullmatch(_UpperCAmelCase    ):
            __a							=    re_prior_cond_resnet.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[1]    ) * 2 + int(groups[2]    ) - 2
            __a							=    {'''1''': 1, '''3''': 2}[groups[-2]]
            __a							=    f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
            __a							=    f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            __a							=    prefix + resnet_block
            __a							=    re_prior_cond_resnet.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_prior_cond_proj_in.fullmatch(_UpperCAmelCase    ):
            __a							=    re_prior_cond_proj_in.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
            __a							=    re_prior_cond_proj_in.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        # keep original key
        else:
            __a							=    original_key

        __a							=    replace_key(_UpperCAmelCase    )

        if f'{key_prefix}.{key}' not in model_state_dict or key is None:
            print(f'failed converting {original_key} to {key}, does not match'    )

        # handle missmatched shape
        elif value.shape != model_state_dict[f'{key_prefix}.{key}'].shape:
            __a							=    model_state_dict[f'{key_prefix}.{key}']
            print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'    )
            __a							=    original_key

        __a							=    original_key
        __a							=    value

    return new_dict





@torch.no_grad()
def 					__snake_case     (      _UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"    )[-1]}'    ):
            __a							=    requests.get(f'{PREFIX}{file}'       ,			allow_redirects=_UpperCAmelCase    )
            os.makedirs(f'{pytorch_dump_folder_path}/'       ,			exist_ok=_UpperCAmelCase    )
            open(f'{pytorch_dump_folder_path}/{file.split("/"    )[-1]}'       ,			'''wb'''    ).write(r.content    )

    __a							=    MODEL_MAPPING[model_name.split('''/'''    )[-1]]

    __a							=    JukeboxConfig.from_pretrained(_UpperCAmelCase    )
    __a							=    JukeboxModel(_UpperCAmelCase    )

    __a							=    []
    __a							=    {}
    for i, dict_name in enumerate(_UpperCAmelCase    ):
        __a							=    torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"    )[-1]}'    )['''model''']

        __a							=    {}
        for k in old_dic.keys():
            if k.endswith('''.b'''    ):
                __a							=    old_dic[k]
            elif k.endswith('''.w'''    ):
                __a							=    old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                __a							=    old_dic[k]
            else:
                __a							=    old_dic[k]

        __a							=    '''vqvae''' if i == 0 else f'priors.{3 - i}'
        __a							=    fix_jukebox_keys(_UpperCAmelCase       ,			model.state_dict()       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
        weight_dict.append(_UpperCAmelCase    )

    __a							=    weight_dict.pop(0    )
    model.vqvae.load_state_dict(_UpperCAmelCase    )
    for i in range(len(_UpperCAmelCase    )    ):
        model.priors[i].load_state_dict(weight_dict[2 - i]    )

    Path(_UpperCAmelCase    ).mkdir(exist_ok=_UpperCAmelCase    )
    with open(f'{pytorch_dump_folder_path}/mapping.json'       ,			'''w'''    ) as txtfile:
        json.dump(_UpperCAmelCase       ,			_UpperCAmelCase    )

    print(f'Saving model {model_name} to {pytorch_dump_folder_path}'    )
    model.save_pretrained(_UpperCAmelCase    )

    return weight_dict


if __name__ == "__main__":
 __snake_case						:Dict						=					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''jukebox-5b-lyrics''',
     type=str,
     help='''Name of the model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''jukebox-5b-lyrics-converted''',
     type=str,
     help='''Path to the output PyTorch model directory.''',
 )
 __snake_case						:Optional[Any]						=					parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__snake_case						:Dict						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					int					,		*__SCREAMING_SNAKE_CASE							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           requires_backends(self					,		'''vision''')
           self.check_model_type(
               TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           __a							=    {}
           __a							=    {}

           if prompt is not None:
               __a							=    prompt

           if generate_kwargs is not None:
               __a							=    generate_kwargs
           if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                   __a							=    {}
               if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                   raise ValueError(
                       '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                       ''' please use only one''')
               __a							=    max_new_tokens
           return preprocess_params, forward_kwargs, {}



       def __call__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str], "Image.Image", List["Image.Image"]]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None):


           '''simple docstring'''

           __a							=    load_image(__SCREAMING_SNAKE_CASE)

           if prompt is not None:
               if not isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                   raise ValueError(
                       F'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - but expected a single string. '
                       '''Note also that one single text can be provided for conditional image to text generation.''')

               __a							=    self.model.config.model_type

               if model_type == "git":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(text=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
                   __a							=    [self.tokenizer.cls_token_id] + input_ids
                   __a							=    torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
                   model_inputs.update({'''input_ids''': input_ids})

               elif model_type == "pix2struct":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		header_text=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

               elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   model_inputs.update(__SCREAMING_SNAKE_CASE)

               else:
                   raise ValueError(F'Model type {model_type} does not support conditional text generation')

           else:
               __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

           if self.model.config.model_type == "git" and prompt is None:
               __a							=    None

           return model_inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']					,		__SCREAMING_SNAKE_CASE)
               and all(x is None for x in model_inputs['''input_ids'''])
           ):
               __a							=    None

           if generate_kwargs is None:
               __a							=    {}
           # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
           #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
           #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
           #  in the `_prepare_model_inputs` method.
           __a							=    model_inputs.pop(self.model.main_input_name)
           __a							=    self.model.generate(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return model_outputs





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    []
           for output_ids in model_outputs:
               __a							=    {
                   '''generated_text''': self.tokenizer.decode(
                       __SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE					,		)
               }
               records.append(__SCREAMING_SNAKE_CASE)
           return records





