

'''simple docstring'''






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class      lowercase_      :




  """simple docstring"""




  def __init__( self  :	Dict   ,lowercase__  :	Dict   ,lowercase__  :	Tuple=1_3   ,lowercase__  :	int=3   ,lowercase__  :	Union[str, Any]=True   ,lowercase__  :	Dict=True   ,lowercase__  :	Any=0.1   ,lowercase__  :	int=0.1   ,lowercase__  :	List[Any]=2_2_4   ,lowercase__  :	Union[str, Any]=1_0_0_0   ,lowercase__  :	Optional[Any]=[3, 3, 6, 4]   ,lowercase__  :	int=[4_8, 5_6, 1_1_2, 2_2_0]   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					num_channels
   __lowercase				    =					is_training
   __lowercase				    =					use_labels
   __lowercase				    =					hidden_dropout_prob
   __lowercase				    =					attention_probs_dropout_prob
   __lowercase				    =					num_labels
   __lowercase				    =					image_size
   __lowercase				    =					layer_depths
   __lowercase				    =					embed_dims






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   __lowercase				    =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

   __lowercase				    =					None
   if self.use_labels:
    __lowercase				    =					ids_tensor([self.batch_size]   ,self.num_labels     )

   __lowercase				    =					self.get_config()

   return config, pixel_values, labels






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return SwiftFormerConfig(
       depths=self.layer_depths   ,embed_dims=self.embed_dims   ,mlp_ratio=4   ,downsamples=[True, True, True, True]   ,hidden_act='''gelu'''   ,num_labels=self.num_labels   ,down_patch_size=3   ,down_stride=2   ,down_pad=1   ,drop_rate=0.0   ,drop_path_rate=0.0   ,use_layer_scale=lowercase__   ,layer_scale_init_value=1e-5   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Any   ,lowercase__  :	Dict   ,lowercase__  :	int     ):
   __lowercase				    =					SwiftFormerModel(config=lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(lowercase__     )
   self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.embed_dims[-1], 7, 7)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	Tuple   ,lowercase__  :	str   ,lowercase__  :	Any     ):
   __lowercase				    =					self.num_labels
   __lowercase				    =					SwiftFormerForImageClassification(lowercase__     )
   model.to(lowercase__     )
   model.eval()
   __lowercase				    =					model(lowercase__   ,labels=lowercase__     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)     )

   __lowercase				    =					SwiftFormerForImageClassification(lowercase__     )
   model.to(lowercase__     )
   model.eval()

   __lowercase				    =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
   __lowercase				    =					model(lowercase__     )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))				    =					self.prepare_config_and_inputs()
   __lowercase				    =					{'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class      lowercase_      (lowerCamelCase__							,			lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    str							     =     (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     (
      {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     False
  SCREAMING_SNAKE_CASE       :    Dict							     =     False
  SCREAMING_SNAKE_CASE       :    Any							     =     False
  SCREAMING_SNAKE_CASE       :    List[str]							     =     False
  SCREAMING_SNAKE_CASE       :    int							     =     False






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					SwiftFormerModelTester(self     )
   __lowercase				    =					ConfigTester(
       self   ,config_class=lowercase__   ,has_text_modality=lowercase__   ,hidden_size=3_7   ,num_attention_heads=1_2   ,num_hidden_layers=1_2   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   self.config_tester.run_common_tests()






  @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''     )
  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   pass






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase				    =					model_class(lowercase__     )
    __lowercase				    =					model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase__   ,nn.Linear     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase				    =					model_class(lowercase__     )
    __lowercase				    =					inspect.signature(model.forward     )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase				    =					[*signature.parameters.keys()]

    __lowercase				    =					['''pixel_values''']
    self.assertListEqual(arg_names[:1]   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__     )






  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase				    =					SwiftFormerModel.from_pretrained(lowercase__     )
    self.assertIsNotNone(lowercase__     )






  @unittest.skip(reason='''SwiftFormer does not output attentions'''     )
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   pass






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   def check_hidden_states_output(lowercase__  :	Any   ,lowercase__  :	Dict   ,lowercase__  :	Tuple     ):
    __lowercase				    =					model_class(lowercase__     )
    model.to(lowercase__     )
    model.eval()

    with torch.no_grad():
     __lowercase				    =					model(**self._prepare_for_class(lowercase__   ,lowercase__     )     )

    __lowercase				    =					outputs.hidden_states

    __lowercase				    =					8
    self.assertEqual(len(lowercase__     )   ,lowercase__     )  # TODO

    # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
    # with the width and height being successively divided by 2, after every 2 blocks
    for i in range(len(lowercase__     )     ):
     self.assertEqual(
         hidden_states[i].shape   ,torch.Size(
             [
                 self.model_tester.batch_size,
                 self.model_tester.embed_dims[i // 2],
                 (self.model_tester.image_size // 4) // 2 ** (i // 2),
                 (self.model_tester.image_size // 4) // 2 ** (i // 2),
             ]     )   ,)

   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase				    =					True
    check_hidden_states_output(lowercase__   ,lowercase__   ,lowercase__     )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __lowercase				    =					True

    check_hidden_states_output(lowercase__   ,lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   def _config_zero_init(lowercase__  :	Any     ):
    __lowercase				    =					copy.deepcopy(lowercase__     )
    for key in configs_no_init.__dict__.keys():
     if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
      setattr(lowercase__   ,lowercase__   ,1e-1_0     )
     if isinstance(getattr(lowercase__   ,lowercase__   ,lowercase__     )   ,lowercase__     ):
      __lowercase				    =					_config_zero_init(getattr(lowercase__   ,lowercase__     )     )
      setattr(lowercase__   ,lowercase__   ,lowercase__     )
    return configs_no_init

   __lowercase    ,      __lowercase				    =					self.model_tester.prepare_config_and_inputs_for_common()

   __lowercase				    =					_config_zero_init(lowercase__     )
   for model_class in self.all_model_classes:
    __lowercase				    =					model_class(config=lowercase__     )
    for name, param in model.named_parameters():
     if param.requires_grad:
      self.assertIn(
          ((param.data.mean() * 1e9) / 1e9).round().item()   ,[0.0, 1.0]   ,msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,)






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   pass


def 						_A	(       ):






 """simple docstring"""



 __lowercase				    =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
 return image






@require_torch
@require_vision
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  @cached_property
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''     ) if is_vision_available() else None






  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''     ).to(lowercase__     )

   __lowercase				    =					self.default_image_processor
   __lowercase				    =					prepare_img()
   __lowercase				    =					image_processor(images=lowercase__   ,return_tensors='''pt'''     ).to(lowercase__     )

   # forward pass
   with torch.no_grad():
    __lowercase				    =					model(**lowercase__     )

   # verify the logits
   __lowercase				    =					torch.Size((1, 1_0_0_0)     )
   self.assertEqual(outputs.logits.shape   ,lowercase__     )

   __lowercase				    =					torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]     ).to(lowercase__     )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,lowercase__   ,atol=1e-4     )     )









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()
UpperCamelCase    							=      logging.get_logger(__name__)


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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					OrderedDict					,							__lowerCAmelCase							:					int							)     ->  Tuple[Dict, Dict]:
   __UpperCamelCase	:			Tuple					=					list(state_dict.keys()							)
   __UpperCamelCase	:			List[Any]					=					{}
   for key in keys:
      __UpperCamelCase	:			Optional[Any]					=					state_dict.pop(__lowerCAmelCase							)
      __UpperCamelCase	:			Dict					=					rename_keys(__lowerCAmelCase							)
      if "in_proj_weight" in key:
         # split fused qkv proj
         __UpperCamelCase	:			Optional[Any]					=					val[:hidden_size, :]
         __UpperCamelCase	:			Optional[Any]					=					val[hidden_size : 2 * hidden_size, :]
         __UpperCamelCase	:			List[str]					=					val[-hidden_size:, :]
      elif "enc_to_dec_proj" in key:
         __UpperCamelCase	:			Dict					=					val
      else:
         __UpperCamelCase	:			Any					=					val
   return state_dict, enc_dec_proj_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      __UpperCamelCase	:			int					=					1024
      __UpperCamelCase	:			Union[str, Any]					=					24
      __UpperCamelCase	:			int					=					16
   elif checkpoint == "medium":
      __UpperCamelCase	:			List[Any]					=					1536
      __UpperCamelCase	:			Dict					=					48
      __UpperCamelCase	:			Dict					=					24
   elif checkpoint == "large":
      __UpperCamelCase	:			List[Any]					=					2048
      __UpperCamelCase	:			str					=					48
      __UpperCamelCase	:			Optional[int]					=					32
   else:
      raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
   __UpperCamelCase	:			Any					=					MusicgenDecoderConfig(
       hidden_size=__lowerCAmelCase					,							ffn_dim=hidden_size * 4					,							num_hidden_layers=__lowerCAmelCase					,							num_attention_heads=__lowerCAmelCase					,							)
   return config


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=None					,							__lowerCAmelCase							:					str="cpu"							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					MusicGen.get_pretrained(__lowerCAmelCase					,							device=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					decoder_config_from_checkpoint(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					fairseq_model.lm.state_dict()
   __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					rename_state_dict(
       __lowerCAmelCase					,							hidden_size=decoder_config.hidden_size							)

   __UpperCamelCase	:			List[Any]					=					TaEncoderModel.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			Dict					=					EncodecModel.from_pretrained("""facebook/encodec_32khz"""							)
   __UpperCamelCase	:			List[str]					=					MusicgenForCausalLM(__lowerCAmelCase							).eval()

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

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

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

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

   # init the composite model
   __UpperCamelCase	:			Tuple					=					MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase					,							audio_encoder=__lowerCAmelCase					,							decoder=__lowerCAmelCase							)

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

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

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

   if logits.shape != (8, 1, 2048):
      raise ValueError("""Incorrect shape for logits"""							)

   # now construct the processor
   __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			List[Any]					=					AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""					,							padding_side="""left"""							)

   __UpperCamelCase	:			Any					=					MusicgenProcessor(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase							)

   # set the appropriate bos/pad token ids
   __UpperCamelCase	:			Tuple					=					2048
   __UpperCamelCase	:			int					=					2048

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

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

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


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

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



