




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					LEDTokenizer
      lowerCAmelCase__			: Dict    =					LEDTokenizerFast
      lowerCAmelCase__			: List[Any]    =					True







      def       snake_case	(     self					: List[str]				):
          super().setUp()
          __UpperCamelCase  		=						[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          __UpperCamelCase  		=						dict(zip(snake_case ,       range(len(snake_case				)				)				)				)
          __UpperCamelCase  		=						['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          __UpperCamelCase  		=						{'''unk_token''': '''<unk>'''}

          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''vocab_file''']				)
          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''merges_file''']				)
          with open(self.vocab_file ,       '''w''' ,       encoding='''utf-8'''				) as fp:
              fp.write(json.dumps(snake_case				) + '''\n'''				)
          with open(self.merges_file ,       '''w''' ,       encoding='''utf-8'''				) as fp:
              fp.write('''\n'''.join(snake_case				)				)







      def       snake_case	(     self					: Tuple ,       **snake_case					: Optional[int]				):
          kwargs.update(self.special_tokens_map				)
          return self.tokenizer_class.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: Optional[Any] ,       **snake_case					: Tuple				):
          kwargs.update(self.special_tokens_map				)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Optional[int]				):
          return "lower newer", "lower newer"







      @cached_property
      def       snake_case	(     self					: Tuple				):
          return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''				)







      @cached_property
      def       snake_case	(     self					: Optional[int]				):
          return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''				)







      @require_torch
      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          __UpperCamelCase  		=						[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase  		=						tokenizer(snake_case ,       max_length=len(snake_case				) ,       padding=snake_case ,       return_tensors='''pt'''				)
              self.assertIsInstance(snake_case ,       snake_case				)

              self.assertEqual((2, 9) ,       batch.input_ids.shape				)
              self.assertEqual((2, 9) ,       batch.attention_mask.shape				)
              __UpperCamelCase  		=						batch.input_ids.tolist()[0]
              self.assertListEqual(snake_case ,       snake_case				)







      @require_torch
      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase  		=						tokenizer(snake_case ,       padding=snake_case ,       return_tensors='''pt'''				)
              self.assertIn('''input_ids''' ,       snake_case				)
              self.assertIn('''attention_mask''' ,       snake_case				)
              self.assertNotIn('''labels''' ,       snake_case				)
              self.assertNotIn('''decoder_attention_mask''' ,       snake_case				)







      @require_torch
      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase  		=						tokenizer(text_target=snake_case ,       max_length=32 ,       padding='''max_length''' ,       return_tensors='''pt'''				)
              self.assertEqual(32 ,       targets['''input_ids'''].shape[1]				)







      @require_torch
      def       snake_case	(     self					: Optional[Any]				):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase  		=						tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog'''] ,       padding=snake_case ,       truncation=snake_case ,       return_tensors='''pt'''				)
              self.assertIsInstance(snake_case ,       snake_case				)
              self.assertEqual(batch.input_ids.shape ,       (2, 5122)				)







      @require_torch
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						['''A long paragraph for summarization.''']
          __UpperCamelCase  		=						[
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase  		=						tokenizer(snake_case ,       return_tensors='''pt'''				)
              __UpperCamelCase  		=						tokenizer(text_target=snake_case ,       return_tensors='''pt'''				)
              __UpperCamelCase  		=						inputs['''input_ids''']
              __UpperCamelCase  		=						targets['''input_ids''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)







      @require_torch
      def       snake_case	(     self					: List[Any]				):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase  		=						['''Summary of the text.''', '''Another summary.''']
              __UpperCamelCase  		=						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

              __UpperCamelCase  		=						tokenizer(snake_case ,       padding=snake_case				)
              __UpperCamelCase  		=						[[0] * len(snake_case				) for x in encoded_output['''input_ids''']]
              __UpperCamelCase  		=						tokenizer.pad(snake_case				)
              self.assertSequenceEqual(outputs['''global_attention_mask'''] ,       snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          pass







      def       snake_case	(     self					: List[Any]				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(snake_case ,       **snake_case				)
                  __UpperCamelCase  		=						self.tokenizer_class.from_pretrained(snake_case ,       **snake_case				)
                  __UpperCamelCase  		=						'''A, <mask> AllenNLP sentence.'''
                  __UpperCamelCase  		=						tokenizer_r.encode_plus(snake_case ,       add_special_tokens=snake_case ,       return_token_type_ids=snake_case				)
                  __UpperCamelCase  		=						tokenizer_p.encode_plus(snake_case ,       add_special_tokens=snake_case ,       return_token_type_ids=snake_case				)
                  self.assertEqual(sum(tokens_r['''token_type_ids''']				) ,       sum(tokens_p['''token_type_ids''']				)				)
                  self.assertEqual(
                      sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				) ,       sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				) ,       )

                  __UpperCamelCase  		=						tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                  __UpperCamelCase  		=						tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)
                  self.assertSequenceEqual(tokens_p['''input_ids'''] ,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)
                  self.assertSequenceEqual(tokens_r['''input_ids'''] ,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)

                  self.assertSequenceEqual(
                      snake_case ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                  self.assertSequenceEqual(
                      snake_case ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)












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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    if "emb" in name:
        __UpperCamelCase  		=						name.replace('''emb'''					,     '''model.decoder.embed_tokens'''      )
    if "transformer" in name:
        __UpperCamelCase  		=						name.replace('''transformer'''					,     '''model.decoder'''      )
    if "cross_attention" in name:
        __UpperCamelCase  		=						name.replace('''cross_attention'''					,     '''encoder_attn'''      )
    if "linear1" in name:
        __UpperCamelCase  		=						name.replace('''linear1'''					,     '''fc1'''      )
    if "linear2" in name:
        __UpperCamelCase  		=						name.replace('''linear2'''					,     '''fc2'''      )
    if "norm1" in name:
        __UpperCamelCase  		=						name.replace('''norm1'''					,     '''self_attn_layer_norm'''      )
    if "norm_cross" in name:
        __UpperCamelCase  		=						name.replace('''norm_cross'''					,     '''encoder_attn_layer_norm'''      )
    if "norm2" in name:
        __UpperCamelCase  		=						name.replace('''norm2'''					,     '''final_layer_norm'''      )
    if "out_norm" in name:
        __UpperCamelCase  		=						name.replace('''out_norm'''					,     '''model.decoder.layer_norm'''      )
    if "linears" in name:
        __UpperCamelCase  		=						name.replace('''linears'''					,     '''lm_heads'''      )
    if "condition_provider.conditioners.description.output_proj" in name:
        __UpperCamelCase  		=						name.replace('''condition_provider.conditioners.description.output_proj'''					,     '''enc_to_dec_proj'''      )
    return name




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple[Dict, Dict]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	MusicgenDecoderConfig:

    """simple docstring"""


    if checkpoint == "small":
        # default config values
        __UpperCamelCase  		=						10_24
        __UpperCamelCase  		=						24
        __UpperCamelCase  		=						16
    elif checkpoint == "medium":
        __UpperCamelCase  		=						15_36
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						24
    elif checkpoint == "large":
        __UpperCamelCase  		=						20_48
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						32
    else:
        raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."      )
    __UpperCamelCase  		=						MusicgenDecoderConfig(
        hidden_size=lowercase_					,     ffn_dim=hidden_size * 4					,     num_hidden_layers=lowercase_					,     num_attention_heads=lowercase_					,     )
    return config




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_="cpu"      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						MusicGen.get_pretrained(lowercase_					,     device=lowercase_      )
    __UpperCamelCase  		=						decoder_config_from_checkpoint(lowercase_      )

    __UpperCamelCase  		=						fairseq_model.lm.state_dict()
    __UpperCamelCase		,      __UpperCamelCase  		=						rename_state_dict(
        lowercase_					,     hidden_size=decoder_config.hidden_size      )

    __UpperCamelCase  		=						TaEncoderModel.from_pretrained('''t5-base'''      )
    __UpperCamelCase  		=						EncodecModel.from_pretrained('''facebook/encodec_32khz'''      )
    __UpperCamelCase  		=						MusicgenForCausalLM(lowercase_      ).eval()

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

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

    if len(lowercase_      ) > 0:
        raise ValueError(F"Missing key(s) in state_dict: {missing_keys}"      )

    if len(lowercase_      ) > 0:
        raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}"      )

    # init the composite model
    __UpperCamelCase  		=						MusicgenForConditionalGeneration(text_encoder=lowercase_					,     audio_encoder=lowercase_					,     decoder=lowercase_      )

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

    # check we can do a forward pass
    __UpperCamelCase  		=						torch.arange(0					,     8					,     dtype=torch.long      ).reshape(2					,     -1      )
    __UpperCamelCase  		=						input_ids.reshape(2 * 4					,     -1      )

    with torch.no_grad():
        __UpperCamelCase  		=						model(input_ids=lowercase_					,     decoder_input_ids=lowercase_      ).logits

    if logits.shape != (8, 1, 20_48):
        raise ValueError('''Incorrect shape for logits'''      )

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

    __UpperCamelCase  		=						MusicgenProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )

    # set the appropriate bos/pad token ids
    __UpperCamelCase  		=						20_48
    __UpperCamelCase  		=						20_48

    # set other default generation config params
    __UpperCamelCase  		=						int(30 * audio_encoder.config.frame_rate      )
    __UpperCamelCase  		=						True
    __UpperCamelCase  		=						3.0

    if pytorch_dump_folder is not None:
        Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
        logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"      )
        model.save_pretrained(lowercase_      )
        processor.save_pretrained(lowercase_      )

    if repo_id:
        logger.info(F"Pushing model {checkpoint} to {repo_id}"      )
        model.push_to_hub(lowercase_      )
        processor.push_to_hub(lowercase_      )


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)






