'''simple docstring'''




import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
_A								=   logging.get_logger(__name__)


_A								=   ['model.decoder.embed_positions.weights']

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if "emb" in name:
          lowercase_					:       Tuple     				=					name.replace('emb'  ,       'model.decoder.embed_tokens'							)
     if "transformer" in name:
          lowercase_					:       Any     				=					name.replace('transformer'  ,       'model.decoder'							)
     if "cross_attention" in name:
          lowercase_					:       int     				=					name.replace('cross_attention'  ,       'encoder_attn'							)
     if "linear1" in name:
          lowercase_					:       Dict     				=					name.replace('linear1'  ,       'fc1'							)
     if "linear2" in name:
          lowercase_					:       Tuple     				=					name.replace('linear2'  ,       'fc2'							)
     if "norm1" in name:
          lowercase_					:       Optional[Any]     				=					name.replace('norm1'  ,       'self_attn_layer_norm'							)
     if "norm_cross" in name:
          lowercase_					:       int     				=					name.replace('norm_cross'  ,       'encoder_attn_layer_norm'							)
     if "norm2" in name:
          lowercase_					:       Union[str, Any]     				=					name.replace('norm2'  ,       'final_layer_norm'							)
     if "out_norm" in name:
          lowercase_					:       Dict     				=					name.replace('out_norm'  ,       'model.decoder.layer_norm'							)
     if "linears" in name:
          lowercase_					:       Dict     				=					name.replace('linears'  ,       'lm_heads'							)
     if "condition_provider.conditioners.description.output_proj" in name:
          lowercase_					:       Optional[int]     				=					name.replace('condition_provider.conditioners.description.output_proj'  ,       'enc_to_dec_proj'							)
     return name

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       str     				=					list(state_dict.keys()							)
     lowercase_					:       Tuple     				=					{}
     for key in keys:
          lowercase_					:       str     				=					state_dict.pop(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Tuple     				=					rename_keys(SCREAMING_SNAKE_CASE_							)
          if "in_proj_weight" in key:
               # split fused qkv proj
               lowercase_					:       Any     				=					val[:hidden_size, :]
               lowercase_					:       Tuple     				=					val[hidden_size : 2 * hidden_size, :]
               lowercase_					:       List[Any]     				=					val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               lowercase_					:       Any     				=					val
          else:
               lowercase_					:       List[str]     				=					val
     return state_dict, enc_dec_proj_state_dict

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if checkpoint == "small":
          # default config values
          lowercase_					:       Tuple     				=					1_024
          lowercase_					:       List[Any]     				=					24
          lowercase_					:       int     				=					16
     elif checkpoint == "medium":
          lowercase_					:       List[Any]     				=					1_536
          lowercase_					:       Optional[int]     				=					48
          lowercase_					:       str     				=					24
     elif checkpoint == "large":
          lowercase_					:       List[Any]     				=					2_048
          lowercase_					:       Dict     				=					48
          lowercase_					:       Optional[int]     				=					32
     else:
          raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''							)
     lowercase_					:       Tuple     				=					MusicgenDecoderConfig(
         hidden_size=SCREAMING_SNAKE_CASE_  ,       ffn_dim=hidden_size * 4  ,       num_hidden_layers=SCREAMING_SNAKE_CASE_  ,       num_attention_heads=SCREAMING_SNAKE_CASE_  ,       )
     return config

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=None  ,       SCREAMING_SNAKE_CASE_=None  ,       SCREAMING_SNAKE_CASE_="cpu"							):
     lowercase_					:       Union[str, Any]     				=					MusicGen.get_pretrained(SCREAMING_SNAKE_CASE_  ,       device=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[Any]     				=					decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       str     				=					fairseq_model.lm.state_dict()
     lowercase_				,lowercase_					:       int     				=					rename_state_dict(
         SCREAMING_SNAKE_CASE_  ,       hidden_size=decoder_config.hidden_size							)

     lowercase_					:       Tuple     				=					TaEncoderModel.from_pretrained('t5-base'							)
     lowercase_					:       Tuple     				=					EncodecModel.from_pretrained('facebook/encodec_32khz'							)
     lowercase_					:       int     				=					MusicgenForCausalLM(SCREAMING_SNAKE_CASE_							).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     lowercase_				,lowercase_					:       Dict     				=					decoder.load_state_dict(SCREAMING_SNAKE_CASE_  ,       strict=SCREAMING_SNAKE_CASE_							)

     for key in missing_keys.copy():
          if key.startswith(('text_encoder', 'audio_encoder')							) or key in EXPECTED_MISSING_KEYS:
               missing_keys.remove(SCREAMING_SNAKE_CASE_							)

     if len(SCREAMING_SNAKE_CASE_							) > 0:
          raise ValueError(f'''Missing key(s) in state_dict: {missing_keys}'''							)

     if len(SCREAMING_SNAKE_CASE_							) > 0:
          raise ValueError(f'''Unexpected key(s) in state_dict: {unexpected_keys}'''							)

     # init the composite model
     lowercase_					:       List[Any]     				=					MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE_  ,       audio_encoder=SCREAMING_SNAKE_CASE_  ,       decoder=SCREAMING_SNAKE_CASE_							)

     # load the pre-trained enc-dec projection (from the decoder state dict)
     model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE_							)

     # check we can do a forward pass
     lowercase_					:       str     				=					torch.arange(0  ,       8  ,       dtype=torch.long							).reshape(2  ,       -1							)
     lowercase_					:       Union[str, Any]     				=					input_ids.reshape(2 * 4  ,       -1							)

     with torch.no_grad():
          lowercase_					:       List[Any]     				=					model(input_ids=SCREAMING_SNAKE_CASE_  ,       decoder_input_ids=SCREAMING_SNAKE_CASE_							).logits

     if logits.shape != (8, 1, 2_048):
          raise ValueError('Incorrect shape for logits'							)

     # now construct the processor
     lowercase_					:       int     				=					AutoTokenizer.from_pretrained('t5-base'							)
     lowercase_					:       Tuple     				=					AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'  ,       padding_side='left'							)

     lowercase_					:       List[str]     				=					MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE_  ,       tokenizer=SCREAMING_SNAKE_CASE_							)

     # set the appropriate bos/pad token ids
     lowercase_					:       int     				=					2_048
     lowercase_					:       Optional[int]     				=					2_048

     # set other default generation config params
     lowercase_					:       List[str]     				=					int(30 * audio_encoder.config.frame_rate							)
     lowercase_					:       Any     				=					True
     lowercase_					:       Optional[Any]     				=					3.0

     if pytorch_dump_folder is not None:
          Path(SCREAMING_SNAKE_CASE_							).mkdir(exist_ok=SCREAMING_SNAKE_CASE_							)
          logger.info(f'''Saving model {checkpoint} to {pytorch_dump_folder}'''							)
          model.save_pretrained(SCREAMING_SNAKE_CASE_							)
          processor.save_pretrained(SCREAMING_SNAKE_CASE_							)

     if repo_id:
          logger.info(f'''Pushing model {checkpoint} to {repo_id}'''							)
          model.push_to_hub(SCREAMING_SNAKE_CASE_							)
          processor.push_to_hub(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 _A								=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint',
     default='small',
     type=str,
     help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
 )
 parser.add_argument(
     '--pytorch_dump_folder',
     required=True,
     default=None,
     type=str,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
 )
 parser.add_argument(
     '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
 )

 _A								=   parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=12       ,				_a=7       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=99       ,				_a=32       ,				_a=32       ,				_a=2       ,				_a=4       ,				_a=37       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=0.02       ,				_a=0       ,				_a=None       ,				)    ->     Optional[Any]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       List[Any]     				=					batch_size
        lowercase_					:       Optional[Any]     				=					seq_length
        lowercase_					:       Optional[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_input_mask
        lowercase_					:       Any     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					projection_dim
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					dropout
        lowercase_					:       Tuple     				=					attention_dropout
        lowercase_					:       Optional[Any]     				=					max_position_embeddings
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					scope
        lowercase_					:       Optional[Any]     				=					bos_token_id
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_input_mask:
             lowercase_					:       Dict     				=					random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
             lowercase_					:       Dict     				=					input_mask.numpy()
             lowercase_				,lowercase_					:       int     				=					input_mask.shape
             lowercase_					:       int     				=					np.random.randint(1       ,				seq_length - 1       ,				size=(batch_size,)				)
             for batch_idx, start_index in enumerate(_a				):
                  lowercase_					:       Tuple     				=					1
                  lowercase_					:       List[Any]     				=					0

        lowercase_					:       List[Any]     				=					self.get_config()

        return config, input_ids, tf.convert_to_tensor(_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        return BlipTextConfig(
            vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				projection_dim=self.projection_dim       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				dropout=self.dropout       ,				attention_dropout=self.attention_dropout       ,				max_position_embeddings=self.max_position_embeddings       ,				initializer_range=self.initializer_range       ,				bos_token_id=self.bos_token_id       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFBlipTextModel(config=_a				)
        lowercase_					:       List[str]     				=					model(_a       ,				attention_mask=_a       ,				training=_a				)
        lowercase_					:       Union[str, Any]     				=					model(_a       ,				training=_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
        self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)				)





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Any     =    (TFBlipTextModel,) if is_tf_available() else ()
   A							:       Union[str, Any]     =    False
   A							:       int     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					BlipTextModelTester(self				)
        lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        pass
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='Blip does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[str]     				=					TFBlipTextModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)





   def 		_lowerCamelCase (self       ,				_a=True				)    ->     Optional[int]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=_a				)




