import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
      from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class      A							:


   def __init__(self     :						str       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Dict=3       ,  __UpperCAmelCase     :						int=3_2       ,  __UpperCAmelCase     :						Union[str, Any]=3       ,  __UpperCAmelCase     :						str=1_0       ,  __UpperCAmelCase     :						Dict=[1_0, 2_0, 3_0, 4_0]       ,  __UpperCAmelCase     :						List[Any]=[1, 1, 2, 1]       ,  __UpperCAmelCase     :						int=True       ,  __UpperCAmelCase     :						List[str]=True       ,  __UpperCAmelCase     :						Optional[int]="relu"       ,  __UpperCAmelCase     :						Tuple=3       ,  __UpperCAmelCase     :						Any=None       ,  )						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  embeddings_size
         UpperCAmelCase__								=  hidden_sizes
         UpperCAmelCase__								=  depths
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  num_labels
         UpperCAmelCase__								=  scope
         UpperCAmelCase__								=  len(__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[str]		)						->				str:
         """simple docstring"""



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

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

         UpperCAmelCase__								=  self.get_config()

         return config, pixel_values, labels


   def 			lowercase_      (self     :						List[str]		)						->				Optional[Any]:
         """simple docstring"""



         return RegNetConfig(
             num_channels=self.num_channels       ,  embeddings_size=self.embeddings_size       ,  hidden_sizes=self.hidden_sizes       ,  depths=self.depths       ,  hidden_act=self.hidden_act       ,  num_labels=self.num_labels       ,  )


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						int		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  RegNetModel(config=__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,  )


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  RegNetForImageClassification(__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()
         UpperCAmelCase__								=  model(__UpperCAmelCase       ,  labels=__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)		)





   def 			lowercase_      (self     :						List[Any]		)						->				List[str]:
         """simple docstring"""



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





@require_torch
class      A							(				UpperCAmelCase_		,				UpperCAmelCase_		,				unittest.TestCase     ):
   __UpperCAmelCase : str      =			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   __UpperCAmelCase : Dict      =			(
       {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase : Union[str, Any]      =			False
   __UpperCAmelCase : Dict      =			False
   __UpperCAmelCase : str      =			False
   __UpperCAmelCase : Any      =			False


   def 			lowercase_      (self     :						Union[str, Any]		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  RegNetModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self       ,  config_class=__UpperCAmelCase       ,  has_text_modality=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Optional[int]		)						->				Dict:
         """simple docstring"""



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


   def 			lowercase_      (self     :						List[Any]		)						->				Dict:
         """simple docstring"""



         return


   @unittest.skip(reason="RegNet does not use inputs_embeds"		)
   def 			lowercase_      (self     :						Tuple		)						->				Any:
         """simple docstring"""



         pass


   @unittest.skip(reason="RegNet does not support input and output embeddings"		)
   def 			lowercase_      (self     :						List[Any]		)						->				int:
         """simple docstring"""



         pass


   def 			lowercase_      (self     :						Optional[int]		)						->				List[str]:
         """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 			lowercase_      (self     :						Tuple		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase		)


   def 			lowercase_      (self     :						Dict		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(config=__UpperCAmelCase		)
               for name, module in model.named_modules():
                     if isinstance(__UpperCAmelCase       ,  (nn.BatchNormad, nn.GroupNorm)		):
                           self.assertTrue(
                               torch.all(module.weight == 1		)       ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,  )
                           self.assertTrue(
                               torch.all(module.bias == 0		)       ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,  )


   def 			lowercase_      (self     :						Dict		)						->				Optional[Any]:
         """simple docstring"""



         def check_hidden_states_output(__UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[int]		):
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               model.to(__UpperCAmelCase		)
               model.eval()

               with torch.no_grad():
                     UpperCAmelCase__								=  model(**self._prepare_for_class(__UpperCAmelCase       ,  __UpperCAmelCase		)		)

               UpperCAmelCase__								=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               UpperCAmelCase__								=  self.model_tester.num_stages
               self.assertEqual(len(__UpperCAmelCase		)       ,  expected_num_stages + 1		)

               # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]		)       ,  [self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,  )

         UpperCAmelCase__		,      UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__								=  ["basic", "bottleneck"]
         for model_class in self.all_model_classes:
               for layer_type in layers_type:
                     UpperCAmelCase__								=  layer_type
                     UpperCAmelCase__								=  True
                     check_hidden_states_output(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)

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

                     check_hidden_states_output(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Dict		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase		)





   @slow
   def 			lowercase_      (self     :						int		)						->				Dict:
         """simple docstring"""



         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__								=  RegNetModel.from_pretrained(__UpperCAmelCase		)
               self.assertIsNotNone(__UpperCAmelCase		)




def    lowerCAmelCase_ (     )     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
      return image







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


   @cached_property
   def 			lowercase_      (self     :						Any		)						->				Union[str, Any]:
         """simple docstring"""



         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
             if is_vision_available()
             else None
         )





   @slow
   def 			lowercase_      (self     :						Dict		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).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		)

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

         UpperCAmelCase__								=  torch.tensor([-0.4180, -1.5051, -3.4836]		).to(__UpperCAmelCase		)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  __UpperCAmelCase       ,  atol=1E-4		)		)




import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


UpperCamelCase__											=					'base_with_context'




def    lowerCAmelCase_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]							)							)
      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)
      for lyr_num, lyr in enumerate(model.encoders							):
            UpperCAmelCase__								=  weights[f"""layers_{lyr_num}"""]
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  ly_weight["attention"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]							)							)
      return model




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)

      for lyr_num, lyr in enumerate(model.encoders							):
            UpperCAmelCase__								=  weights[f"""layers_{lyr_num}"""]
            UpperCAmelCase__								=  ly_weight["attention"]

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]							)							)

      return model




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T							)							)
      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T							)							)

      for lyr_num, lyr in enumerate(model.decoders							):
            UpperCAmelCase__								=  weights[f"""layers_{lyr_num}"""]
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T							)							)

            UpperCAmelCase__								=  ly_weight["self_attention"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)

            UpperCAmelCase__								=  ly_weight["MultiHeadDotProductAttention_0"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T							)							)

      return model




def    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoints.load_tax_checkpoint(args.checkpoint_path							)
      UpperCAmelCase__								=  jnp.tree_util.tree_map(onp.array,  __A							)

      UpperCAmelCase__								=  [
          "from __gin__ import dynamic_registration",
          "from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
          "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
          "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
      ]

      UpperCAmelCase__								=  os.path.join(args.checkpoint_path,  "..",  "config.gin"							)
      UpperCAmelCase__								=  inference.parse_training_gin_file(__A,  __A							)
      UpperCAmelCase__								=  inference.InferenceModel(args.checkpoint_path,  __A							)

      UpperCAmelCase__								=  DDPMScheduler(beta_schedule="squaredcos_cap_v2",  variance_type="fixed_large"							)

      UpperCAmelCase__								=  SpectrogramNotesEncoder(
          max_length=synth_model.sequence_length["inputs"],  vocab_size=synth_model.model.module.config.vocab_size,  d_model=synth_model.model.module.config.emb_dim,  dropout_rate=synth_model.model.module.config.dropout_rate,  num_layers=synth_model.model.module.config.num_encoder_layers,  num_heads=synth_model.model.module.config.num_heads,  d_kv=synth_model.model.module.config.head_dim,  d_ff=synth_model.model.module.config.mlp_dim,  feed_forward_proj="gated-gelu",  )

      UpperCAmelCase__								=  SpectrogramContEncoder(
          input_dims=synth_model.audio_codec.n_dims,  targets_context_length=synth_model.sequence_length["targets_context"],  d_model=synth_model.model.module.config.emb_dim,  dropout_rate=synth_model.model.module.config.dropout_rate,  num_layers=synth_model.model.module.config.num_encoder_layers,  num_heads=synth_model.model.module.config.num_heads,  d_kv=synth_model.model.module.config.head_dim,  d_ff=synth_model.model.module.config.mlp_dim,  feed_forward_proj="gated-gelu",  )

      UpperCAmelCase__								=  TaFilmDecoder(
          input_dims=synth_model.audio_codec.n_dims,  targets_length=synth_model.sequence_length["targets_context"],  max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time,  d_model=synth_model.model.module.config.emb_dim,  num_layers=synth_model.model.module.config.num_decoder_layers,  num_heads=synth_model.model.module.config.num_heads,  d_kv=synth_model.model.module.config.head_dim,  d_ff=synth_model.model.module.config.mlp_dim,  dropout_rate=synth_model.model.module.config.dropout_rate,  )

      UpperCAmelCase__								=  load_notes_encoder(ta_checkpoint["target"]["token_encoder"],  __A							)
      UpperCAmelCase__								=  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"],  __A							)
      UpperCAmelCase__								=  load_decoder(ta_checkpoint["target"]["decoder"],  __A							)

      UpperCAmelCase__								=  OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"							)

      UpperCAmelCase__								=  SpectrogramDiffusionPipeline(
          notes_encoder=__A,  continuous_encoder=__A,  decoder=__A,  scheduler=__A,  melgan=__A,  )
      if args.save:
            pipe.save_pretrained(args.output_path							)


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()

      parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
      parser.add_argument(
          '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
      )
      parser.add_argument(
          '--checkpoint_path',
          default=f'''{MODEL}/checkpoint_500000''',
          type=str,
          required=False,
          help='Path to the original jax model checkpoint.',
      )
      UpperCamelCase__											=					parser.parse_args()

      main(args)



