

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A      = logging.get_logger(__name__)

A      = {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE		):
  A__=  'swin'

  A__=  {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }






  def __init__(					self      :					Optional[Any]		,						_lowercase      :					str=2_24		,						_lowercase      :					Optional[Any]=4		,						_lowercase      :					Optional[int]=3		,						_lowercase      :					str=96		,						_lowercase      :					Dict=[2, 2, 6, 2]		,						_lowercase      :					str=[3, 6, 12, 24]		,						_lowercase      :					Union[str, Any]=7		,						_lowercase      :					Tuple=4.0		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					Dict=0.0		,						_lowercase      :					List[Any]=0.0		,						_lowercase      :					Union[str, Any]=0.1		,						_lowercase      :					Union[str, Any]="gelu"		,						_lowercase      :					Union[str, Any]=False		,						_lowercase      :					List[str]=0.0_2		,						_lowercase      :					List[str]=1E-5		,						_lowercase      :					str=32		,						_lowercase      :					Optional[int]=None		,						_lowercase      :					Union[str, Any]=None		,						**_lowercase      :					Dict		,						):




         """simple docstring"""







         super().__init__(**_lowercase )

         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     patch_size
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     embed_dim
         UpperCAmelCase__										=     depths
         UpperCAmelCase__										=     len(_lowercase )
         UpperCAmelCase__										=     num_heads
         UpperCAmelCase__										=     window_size
         UpperCAmelCase__										=     mlp_ratio
         UpperCAmelCase__										=     qkv_bias
         UpperCAmelCase__										=     hidden_dropout_prob
         UpperCAmelCase__										=     attention_probs_dropout_prob
         UpperCAmelCase__										=     drop_path_rate
         UpperCAmelCase__										=     hidden_act
         UpperCAmelCase__										=     use_absolute_embeddings
         UpperCAmelCase__										=     layer_norm_eps
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     encoder_stride
         # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         UpperCAmelCase__										=     int(embed_dim * 2 ** (len(_lowercase ) - 1) )
         UpperCAmelCase__										=     ["stem"] + [F"""stage{idx}""" for idx in range(1		,						len(_lowercase ) + 1 )]
         UpperCAmelCase__						,			UpperCAmelCase__										=     get_aligned_output_features_output_indices(
             out_features=_lowercase		,						out_indices=_lowercase		,						stage_names=self.stage_names )




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  version.parse('1.11'		)






  @property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ] )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         return 1E-4









import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     "ylacombe/bark-small"
         UpperCAmelCase__										=     tempfile.mkdtemp()
         UpperCAmelCase__										=     "en_speaker_1"
         UpperCAmelCase__										=     "This is a test string"
         UpperCAmelCase__										=     "speaker_embeddings_path.json"
         UpperCAmelCase__										=     "speaker_embeddings"






  def 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







         return AutoTokenizer.from_pretrained(self.checkpoint		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase__										=     BarkProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer.get_vocab() )






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)
         processor.save_pretrained(
             self.tmpdirname		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						speaker_embeddings_directory=self.speaker_embeddings_directory		,						)

         UpperCAmelCase__										=     self.get_tokenizer(bos_token="(BOS)"		,						eos_token="(EOS)" )

         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             self.tmpdirname		,						self.speaker_embeddings_dict_path		,						bos_token="(BOS)"		,						eos_token="(EOS)"		,						)

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer_add_kwargs.get_vocab() )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)

         UpperCAmelCase__										=     35
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     8

         UpperCAmelCase__										=     {
             "semantic_prompt": np.ones(_lowercase ),
             "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
             "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
         }

         # test providing already loaded voice_preset
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )

         UpperCAmelCase__										=     inputs["history_prompt"]
         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from npz file
         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						"file.npz" )
         np.savez(_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )
         UpperCAmelCase__										=     inputs["history_prompt"]

         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from the hub
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=self.voice_preset )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

         UpperCAmelCase__										=     tokenizer(
             self.input_string		,						padding="max_length"		,						max_length=2_56		,						add_special_tokens=_lowercase		,						return_attention_mask=_lowercase		,						return_token_type_ids=_lowercase		,						)

         for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]		,						encoded_processor[key].squeeze().tolist() )





