



import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_MAPPING,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
  )
  from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  import PIL
  from PIL import Image

  from transformers import BeitImageProcessor





class  __magic_name__   :






  def __init__(						self      :     Any				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str=1_00				,		UpperCamelCase__      :     List[str]=13				,		UpperCamelCase__      :     Optional[int]=30				,		UpperCamelCase__      :     Union[str, Any]=2				,		UpperCamelCase__      :     List[str]=3				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Tuple=True				,		UpperCamelCase__      :     List[Any]=32				,		UpperCamelCase__      :     Any=4				,		UpperCamelCase__      :     Optional[Any]=4				,		UpperCamelCase__      :     Tuple=37				,		UpperCamelCase__      :     Any="gelu"				,		UpperCamelCase__      :     Any=0.1				,		UpperCamelCase__      :     Optional[Any]=0.1				,		UpperCamelCase__      :     int=10				,		UpperCamelCase__      :     Tuple=0.02				,		UpperCamelCase__      :     str=3				,		UpperCamelCase__      :     Optional[int]=None				,		UpperCamelCase__      :     Dict=[0, 1, 2, 3]				,		)				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     parent
        UpperCAmelCase         =     1_00
        UpperCAmelCase         =     batch_size
        UpperCAmelCase         =     image_size
        UpperCAmelCase         =     patch_size
        UpperCAmelCase         =     num_channels
        UpperCAmelCase         =     is_training
        UpperCAmelCase         =     use_labels
        UpperCAmelCase         =     hidden_size
        UpperCAmelCase         =     num_hidden_layers
        UpperCAmelCase         =     num_attention_heads
        UpperCAmelCase         =     intermediate_size
        UpperCAmelCase         =     hidden_act
        UpperCAmelCase         =     hidden_dropout_prob
        UpperCAmelCase         =     attention_probs_dropout_prob
        UpperCAmelCase         =     type_sequence_label_size
        UpperCAmelCase         =     initializer_range
        UpperCAmelCase         =     scope
        UpperCAmelCase         =     out_indices
        UpperCAmelCase         =     num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        UpperCAmelCase         =     (image_size // patch_size) ** 2
        UpperCAmelCase         =     num_patches + 1






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Union[str, Any]:
        '''simple docstring'''







        UpperCAmelCase         =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        UpperCAmelCase         =     None
        UpperCAmelCase         =     None
        if self.use_labels:
              UpperCAmelCase         =     ids_tensor([self.batch_size]				,		self.type_sequence_label_size      )
              UpperCAmelCase         =     ids_tensor([self.batch_size, self.image_size, self.image_size]				,		self.num_labels      )

        UpperCAmelCase         =     self.get_config()

        return config, pixel_values, labels, pixel_labels






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
        '''simple docstring'''







        return BeitConfig(
            vocab_size=self.vocab_size				,		image_size=self.image_size				,		patch_size=self.patch_size				,		num_channels=self.num_channels				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		is_decoder=UpperCamelCase__				,		initializer_range=self.initializer_range				,		out_indices=self.out_indices				,		)






  def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     List[str]      )				->					Optional[Any]:
        '''simple docstring'''







        UpperCAmelCase         =     BeitModel(config=UpperCamelCase__      )
        model.to(UpperCamelCase__      )
        model.eval()
        UpperCAmelCase         =     model(UpperCamelCase__      )
        self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str      )				->					Union[str, Any]:
        '''simple docstring'''







        UpperCAmelCase         =     BeitForMaskedImageModeling(config=UpperCamelCase__      )
        model.to(UpperCamelCase__      )
        model.eval()
        UpperCAmelCase         =     model(UpperCamelCase__      )
        self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length - 1, self.vocab_size)      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     int      )				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     self.type_sequence_label_size
        UpperCAmelCase         =     BeitForImageClassification(UpperCamelCase__      )
        model.to(UpperCamelCase__      )
        model.eval()
        UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
        self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)      )

        # test greyscale images
        UpperCAmelCase         =     1
        UpperCAmelCase         =     BeitForImageClassification(UpperCamelCase__      )
        model.to(UpperCamelCase__      )
        model.eval()

        UpperCAmelCase         =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
        UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
        self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]      )				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     self.num_labels
        UpperCAmelCase         =     BeitForSemanticSegmentation(UpperCamelCase__      )
        model.to(UpperCamelCase__      )
        model.eval()
        UpperCAmelCase         =     model(UpperCamelCase__      )
        self.parent.assertEqual(
            result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
        UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
        self.parent.assertEqual(
            result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )







  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
        '''simple docstring'''







        UpperCAmelCase         =     self.prepare_config_and_inputs()
        UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
        UpperCAmelCase         =     {"pixel_values": pixel_values}
        return config, inputs_dict





@require_torch
class  __magic_name__   (							A__,  A__,  unittest.TestCase						):
  lowercase      :       Optional[Any]          =(
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  lowercase      :       Optional[Any]          =(
      {
          '''feature-extraction''': BeitModel,
          '''image-classification''': BeitForImageClassification,
          '''image-segmentation''': BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase      :       Dict          =False
  lowercase      :       int          =False
  lowercase      :       Union[str, Any]          =False






  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     BeitModelTester(self      )
        UpperCAmelCase         =     ConfigTester(self				,		config_class=UpperCamelCase__				,		has_text_modality=UpperCamelCase__				,		hidden_size=37      )






  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
        '''simple docstring'''







        self.config_tester.run_common_tests()






  @unittest.skip(reason="BEiT does not use inputs_embeds"      )
  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[Any]:
        '''simple docstring'''







        pass






  @require_torch_multi_gpu
  @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
        '''simple docstring'''







        pass






  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
        '''simple docstring'''







        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCAmelCase         =     model_class(UpperCamelCase__      )
              self.assertIsInstance(model.get_input_embeddings()				,		(nn.Module)      )
              UpperCAmelCase         =     model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCamelCase__				,		nn.Linear      )      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
        '''simple docstring'''







        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase         =     ["pixel_values"]
              self.assertListEqual(arg_names[:1]				,		UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Union[str, Any]:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					int:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__      )






  def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					int:
        '''simple docstring'''







        if not self.model_tester.is_training:
              return

        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase         =     True

        for model_class in self.all_model_classes:
              # we don't test BeitForMaskedImageModeling
              if model_class in [*get_values(UpperCamelCase__      ), BeitForMaskedImageModeling]:
                    continue

              UpperCAmelCase         =     model_class(UpperCamelCase__      )
              model.to(UpperCamelCase__      )
              model.train()
              UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )
              UpperCAmelCase         =     model(**UpperCamelCase__      ).loss
              loss.backward()






  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					List[Any]:
        '''simple docstring'''







        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
              return

        UpperCAmelCase         =     False
        UpperCAmelCase         =     True

        for model_class in self.all_model_classes:
              # we don't test BeitForMaskedImageModeling
              if (
                  model_class in [*get_values(UpperCamelCase__      ), BeitForMaskedImageModeling]
                  or not model_class.supports_gradient_checkpointing
              ):
                    continue

              UpperCAmelCase         =     model_class(UpperCamelCase__      )
              model.gradient_checkpointing_enable()
              model.to(UpperCamelCase__      )
              model.train()
              UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )
              UpperCAmelCase         =     model(**UpperCamelCase__      ).loss
              loss.backward()






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[int]:
        '''simple docstring'''







        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase         =     _config_zero_init(UpperCamelCase__      )
        for model_class in self.all_model_classes:
              UpperCAmelCase         =     model_class(config=UpperCamelCase__      )
              for name, param in model.named_parameters():
                    # we skip lambda parameters as these require special initial values
                    # determined by config.layer_scale_init_value
                    if "lambda" in name:
                          continue
                    if param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()				,		[0.0, 1.0]				,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,		)







  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
        '''simple docstring'''







        for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase         =     BeitModel.from_pretrained(UpperCamelCase__      )
              self.assertIsNotNone(UpperCamelCase__      )

def    lowerCamelCase_() ->       str:
      UpperCAmelCase         =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
      return image





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






  @cached_property
  def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
        '''simple docstring'''







        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None






  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      ).to(UpperCamelCase__      )

        UpperCAmelCase         =     self.default_image_processor
        UpperCAmelCase         =     prepare_img()
        UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).pixel_values.to(UpperCamelCase__      )

        # prepare bool_masked_pos
        UpperCAmelCase         =     torch.ones((1, 1_96)				,		dtype=torch.bool      ).to(UpperCamelCase__      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(pixel_values=UpperCamelCase__				,		bool_masked_pos=UpperCamelCase__      )
        UpperCAmelCase         =     outputs.logits

        # verify the logits
        UpperCAmelCase         =     torch.Size((1, 1_96, 81_92)      )
        self.assertEqual(logits.shape				,		UpperCamelCase__      )

        UpperCAmelCase         =     torch.tensor(
            [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      ).to(UpperCamelCase__      )

        self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]				,		UpperCamelCase__				,		atol=1e-2      )      )






  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[int]:
        '''simple docstring'''







        UpperCAmelCase         =     BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(UpperCamelCase__      )

        UpperCAmelCase         =     self.default_image_processor
        UpperCAmelCase         =     prepare_img()
        UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(**UpperCamelCase__      )
        UpperCAmelCase         =     outputs.logits

        # verify the logits
        UpperCAmelCase         =     torch.Size((1, 10_00)      )
        self.assertEqual(logits.shape				,		UpperCamelCase__      )

        UpperCAmelCase         =     torch.tensor([-1.23_85, -1.09_87, -1.01_08]      ).to(UpperCamelCase__      )

        self.assertTrue(torch.allclose(logits[0, :3]				,		UpperCamelCase__				,		atol=1e-4      )      )

        UpperCAmelCase         =     2_81
        self.assertEqual(logits.argmax(-1      ).item()				,		UpperCamelCase__      )






  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
        '''simple docstring'''







        UpperCAmelCase         =     BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
            UpperCamelCase__      )

        UpperCAmelCase         =     self.default_image_processor
        UpperCAmelCase         =     prepare_img()
        UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(**UpperCamelCase__      )
        UpperCAmelCase         =     outputs.logits

        # verify the logits
        UpperCAmelCase         =     torch.Size((1, 2_18_41)      )
        self.assertEqual(logits.shape				,		UpperCamelCase__      )

        UpperCAmelCase         =     torch.tensor([1.68_81, -0.27_87, 0.59_01]      ).to(UpperCamelCase__      )

        self.assertTrue(torch.allclose(logits[0, :3]				,		UpperCamelCase__				,		atol=1e-4      )      )

        UpperCAmelCase         =     23_96
        self.assertEqual(logits.argmax(-1      ).item()				,		UpperCamelCase__      )






  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[int]:
        '''simple docstring'''







        UpperCAmelCase         =     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
        UpperCAmelCase         =     model.to(UpperCamelCase__      )

        UpperCAmelCase         =     BeitImageProcessor(do_resize=UpperCamelCase__				,		size=6_40				,		do_center_crop=UpperCamelCase__      )

        UpperCAmelCase         =     load_dataset("hf-internal-testing/fixtures_ade20k"				,		split="test"      )
        UpperCAmelCase         =     Image.open(ds[0]["file"]      )
        UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(**UpperCamelCase__      )
        UpperCAmelCase         =     outputs.logits

        # verify the logits
        UpperCAmelCase         =     torch.Size((1, 1_50, 1_60, 1_60)      )
        self.assertEqual(logits.shape				,		UpperCamelCase__      )

        UpperCAmelCase         =     version.parse(PIL.__version__      ) < version.parse("9.0.0"      )

        if is_pillow_less_than_a:
              UpperCAmelCase         =     torch.tensor(
                  [
                      [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                      [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                      [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                  ]				,		device=UpperCamelCase__				,		)
        else:
              UpperCAmelCase         =     torch.tensor(
                  [
                      [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                      [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                      [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                  ]				,		device=UpperCamelCase__				,		)

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]				,		UpperCamelCase__				,		atol=1e-4      )      )







  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
        '''simple docstring'''







        UpperCAmelCase         =     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
        UpperCAmelCase         =     model.to(UpperCamelCase__      )

        UpperCAmelCase         =     BeitImageProcessor(do_resize=UpperCamelCase__				,		size=6_40				,		do_center_crop=UpperCamelCase__      )

        UpperCAmelCase         =     load_dataset("hf-internal-testing/fixtures_ade20k"				,		split="test"      )
        UpperCAmelCase         =     Image.open(ds[0]["file"]      )
        UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(**UpperCamelCase__      )

        UpperCAmelCase         =     outputs.logits.detach().cpu()

        UpperCAmelCase         =     image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__				,		target_sizes=[(5_00, 3_00)]      )
        UpperCAmelCase         =     torch.Size((5_00, 3_00)      )
        self.assertEqual(segmentation[0].shape				,		UpperCamelCase__      )

        UpperCAmelCase         =     image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__      )
        UpperCAmelCase         =     torch.Size((1_60, 1_60)      )
        self.assertEqual(segmentation[0].shape				,		UpperCamelCase__      )








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()
__lowerCamelCase     :							Dict  							=				logging.get_logger(__name__)


__lowerCamelCase     :							Tuple  							=				["model.decoder.embed_positions.weights"]

def    lowerCamelCase_(lowerCamelCase_    ) ->       Any:
						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    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple[Dict, Dict]:
						UpperCAmelCase         =     list(state_dict.keys()    )
						UpperCAmelCase         =     {}
						for key in keys:
												UpperCAmelCase         =     state_dict.pop(lowerCamelCase_    )
												UpperCAmelCase         =     rename_keys(lowerCamelCase_    )
												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    lowerCamelCase_(lowerCamelCase_    ) ->       MusicgenDecoderConfig:
						if checkpoint == "small":
												# default config values
												UpperCAmelCase         =     1_024
												UpperCAmelCase         =     24
												UpperCAmelCase         =     16
						elif checkpoint == "medium":
												UpperCAmelCase         =     1_536
												UpperCAmelCase         =     48
												UpperCAmelCase         =     24
						elif checkpoint == "large":
												UpperCAmelCase         =     2_048
												UpperCAmelCase         =     48
												UpperCAmelCase         =     32
						else:
												raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'    )
						UpperCAmelCase         =     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_    ,   lowerCamelCase_=None    ,   lowerCamelCase_=None    ,   lowerCamelCase_="cpu"    ) ->       List[str]:
						UpperCAmelCase         =     MusicGen.get_pretrained(lowerCamelCase_    ,   device=lowerCamelCase_    )
						UpperCAmelCase         =     decoder_config_from_checkpoint(lowerCamelCase_    )

						UpperCAmelCase         =     fairseq_model.lm.state_dict()
						UpperCAmelCase       ,		UpperCAmelCase         =     rename_state_dict(
						    lowerCamelCase_    ,   hidden_size=decoder_config.hidden_size    )

						UpperCAmelCase         =     TaEncoderModel.from_pretrained("t5-base"    )
						UpperCAmelCase         =     EncodecModel.from_pretrained("facebook/encodec_32khz"    )
						UpperCAmelCase         =     MusicgenForCausalLM(lowerCamelCase_    ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCAmelCase       ,		UpperCAmelCase         =     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         =     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         =     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=lowerCamelCase_    ,   decoder_input_ids=lowerCamelCase_    ).logits

						if logits.shape != (8, 1, 2_048):
												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=lowerCamelCase_    ,   tokenizer=lowerCamelCase_    )

						# set the appropriate bos/pad token ids
						UpperCAmelCase         =     2_048
						UpperCAmelCase         =     2_048

						# 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(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__":
		__lowerCamelCase     :							List[Any]  							=				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."
		)

		__lowerCamelCase     :							Optional[int]  							=				parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



