

import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

__UpperCAmelCase								=			random.Random()





def      snake_case_				(__A					:						Tuple ,       __A					:						str=1.0 ,       __A					:						Dict=None ,       __A					:						Any=None					)      ->     List[str]:
   if rng is None:
      __lowerCAmelCase  :      List[str]    =						global_rng

   __lowerCAmelCase  :      Union[str, Any]    =						[]
   for batch_idx in range(shape[0]					):
      values.append([]					)
      for _ in range(shape[1]					):
         values[-1].append(rng.random() * scale					)

   return values



@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   str	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[Any]=4_00	,							lowerCAmelCase			:   List[str]=20_00	,							lowerCAmelCase			:   List[str]=10	,							lowerCAmelCase			:   List[str]=1_60	,							lowerCAmelCase			:   Any=8	,							lowerCAmelCase			:   List[Any]=0.0	,							lowerCAmelCase			:   Optional[Any]=40_00	,							lowerCAmelCase			:   Optional[int]=False	,							lowerCAmelCase			:   List[str]=True	,							)							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      Any    =						min_seq_length
     __lowerCAmelCase  :      Optional[int]    =						max_seq_length
     __lowerCAmelCase  :      Union[str, Any]    =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __lowerCAmelCase  :      Dict    =						padding_value
     __lowerCAmelCase  :      List[str]    =						sampling_rate
     __lowerCAmelCase  :      str    =						return_attention_mask
     __lowerCAmelCase  :      Optional[int]    =						do_normalize
     __lowerCAmelCase  :      Optional[Any]    =						feature_size
     __lowerCAmelCase  :      Tuple    =						chunk_length
     __lowerCAmelCase  :      Dict    =						hop_length







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str=False	,							lowerCAmelCase			:   List[str]=False   )							->     Dict:






     """simple docstring"""







     def _flatten(lowerCAmelCase			:   Tuple   ):
        return list(itertools.chain(*lowerCAmelCase   )   )

     if equal_length:
        __lowerCAmelCase  :      str    =						[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
     else:
        # make sure that inputs increase in size
        __lowerCAmelCase  :      Union[str, Any]    =						[
            floats_list((x, self.feature_size)   )
            for x in range(self.min_seq_length	,							self.max_seq_length	,							self.seq_length_diff   )
        ]
     if numpify:
        __lowerCAmelCase  :      Dict    =						[np.asarray(lowerCAmelCase   ) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=WhisperFeatureExtractor if is_speech_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						WhisperFeatureExtractionTester(self   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase  :      List[Any]    =						feat_extract_first.save_pretrained(lowerCAmelCase   )[0]
        check_json_file_has_correct_format(lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						self.feature_extraction_class.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Tuple    =						feat_extract_first.to_dict()
     __lowerCAmelCase  :      Union[str, Any]    =						feat_extract_second.to_dict()
     __lowerCAmelCase  :      Union[str, Any]    =						feat_extract_first.mel_filters
     __lowerCAmelCase  :      List[Any]    =						feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase  :      Any    =						os.path.join(lowerCAmelCase	,							"""feat_extract.json"""   )
        feat_extract_first.to_json_file(lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						self.feature_extraction_class.from_json_file(lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						feat_extract_first.to_dict()
     __lowerCAmelCase  :      Any    =						feat_extract_second.to_dict()
     __lowerCAmelCase  :      List[Any]    =						feat_extract_first.mel_filters
     __lowerCAmelCase  :      Union[str, Any]    =						feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     __lowerCAmelCase  :      Dict    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]
     __lowerCAmelCase  :      Dict    =						[np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     # Test feature size
     __lowerCAmelCase  :      Dict    =						feature_extractor(lowerCAmelCase	,							padding="""max_length"""	,							return_tensors="""np"""   ).input_features
     self.assertTrue(input_features.ndim == 3   )
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
     self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

     # Test not batched input
     __lowerCAmelCase  :      Tuple    =						feature_extractor(speech_inputs[0]	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      Union[str, Any]    =						feature_extractor(np_speech_inputs[0]	,							return_tensors="""np"""   ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # Test batched
     __lowerCAmelCase  :      List[str]    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      str    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase	,							lowerCAmelCase   ):
        self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # Test 2-D numpy arrays are batched.
     __lowerCAmelCase  :      List[Any]    =						[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
     __lowerCAmelCase  :      List[Any]    =						np.asarray(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      Tuple    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase	,							lowerCAmelCase   ):
        self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # Test truncation required
     __lowerCAmelCase  :      Tuple    =						[floats_list((1, x)   )[0] for x in range(2_00	,							(feature_extractor.n_samples + 5_00)	,							2_00   )]
     __lowerCAmelCase  :      Tuple    =						[np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     __lowerCAmelCase  :      Dict    =						[x[: feature_extractor.n_samples] for x in speech_inputs]
     __lowerCAmelCase  :      List[str]    =						[np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs_truncated]

     __lowerCAmelCase  :      Dict    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      Optional[int]    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase	,							lowerCAmelCase   ):
        self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     import torch

     __lowerCAmelCase  :      str    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      int    =						np.random.rand(1_00	,							32   ).astype(np.floataa   )
     __lowerCAmelCase  :      Any    =						np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
        __lowerCAmelCase  :      List[str]    =						feature_extractor.pad([{"""input_features""": inputs}]	,							return_tensors="""np"""   )
        self.assertTrue(np_processed.input_features.dtype == np.floataa   )
        __lowerCAmelCase  :      List[Any]    =						feature_extractor.pad([{"""input_features""": inputs}]	,							return_tensors="""pt"""   )
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Optional[Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,							"""clean"""	,							split="""validation"""   )
     # automatic decoding with librispeech
     __lowerCAmelCase  :      int    =						ds.sort("""id"""   ).select(range(lowerCAmelCase   )   )[:num_samples]["""audio"""]

     return [x["array"] for x in speech_samples]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						torch.tensor(
         [
             0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
             0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
             0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
             -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
         ]   )
     # fmt: on

     __lowerCAmelCase  :      Dict    =						self._load_datasamples(1   )
     __lowerCAmelCase  :      Dict    =						WhisperFeatureExtractor()
     __lowerCAmelCase  :      List[str]    =						feature_extractor(lowerCAmelCase	,							return_tensors="""pt"""   ).input_features
     self.assertEqual(input_features.shape	,							(1, 80, 30_00)   )
     self.assertTrue(torch.allclose(input_features[0, 0, :30]	,							lowerCAmelCase	,							atol=1e-4   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      List[str]    =						self._load_datasamples(1   )[0]
     __lowerCAmelCase  :      Any    =						((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
     __lowerCAmelCase  :      str    =						feat_extract.zero_mean_unit_var_norm([audio]	,							attention_mask=lowerCAmelCase   )[0]

     self.assertTrue(np.all(np.mean(lowerCAmelCase   ) < 1e-3   )   )
     self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase   ) - 1   ) < 1e-3   )   )





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def      snake_case_				(__A					:						Optional[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						SwinConfig()
   __lowerCAmelCase  :      List[Any]    =						swin_name.split("""_"""					)

   __lowerCAmelCase  :      Dict    =						name_split[1]
   __lowerCAmelCase  :      Optional[Any]    =						int(name_split[4]					)
   __lowerCAmelCase  :      List[Any]    =						int(name_split[3][-1]					)

   if model_size == "tiny":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      List[Any]    =						(2, 2, 6, 2)
      __lowerCAmelCase  :      Optional[Any]    =						(3, 6, 1_2, 2_4)
   elif model_size == "small":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      Optional[int]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      Optional[int]    =						(3, 6, 1_2, 2_4)
   elif model_size == "base":
      __lowerCAmelCase  :      List[Any]    =						1_2_8
      __lowerCAmelCase  :      Tuple    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(4, 8, 1_6, 3_2)
   else:
      __lowerCAmelCase  :      List[Any]    =						1_9_2
      __lowerCAmelCase  :      List[str]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      __lowerCAmelCase  :      Dict    =						2_1_8_4_1
   else:
      __lowerCAmelCase  :      Optional[Any]    =						1_0_0_0
      __lowerCAmelCase  :      Union[str, Any]    =						"""huggingface/label-files"""
      __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
      __lowerCAmelCase  :      Any    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
      __lowerCAmelCase  :      int    =						{int(__A					): v for k, v in idalabel.items()}
      __lowerCAmelCase  :      str    =						idalabel
      __lowerCAmelCase  :      int    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :      Optional[Any]    =						img_size
   __lowerCAmelCase  :      Optional[Any]    =						num_classes
   __lowerCAmelCase  :      Tuple    =						embed_dim
   __lowerCAmelCase  :      Union[str, Any]    =						depths
   __lowerCAmelCase  :      Optional[Any]    =						num_heads
   __lowerCAmelCase  :      Tuple    =						window_size

   return config





def      snake_case_				(__A					:						int					)      ->     Optional[Any]:
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if "layers" in name:
      __lowerCAmelCase  :      int    =						"""encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      str    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

   if name == "norm.weight":
      __lowerCAmelCase  :      Dict    =						"""layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase  :      Optional[int]    =						"""layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase  :      int    =						name.replace("""head""" ,       """classifier"""					)
   else:
      __lowerCAmelCase  :      List[str]    =						"""swin.""" + name

   return name





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     int:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Tuple    =						orig_state_dict.pop(__A					)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase  :      Any    =						key.split("""."""					)
         __lowerCAmelCase  :      Union[str, Any]    =						int(key_split[1]					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Optional[int]    =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCAmelCase  :      List[str]    =						val[:dim, :]
            __lowerCAmelCase  :      List[Any]    =						val[
                dim : dim * 2, :
            ]
            __lowerCAmelCase  :      str    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      str    =						val[
                :dim
            ]
            __lowerCAmelCase  :      int    =						val[
                dim : dim * 2
            ]
            __lowerCAmelCase  :      int    =						val[
                -dim:
            ]
      else:
         __lowerCAmelCase  :      Tuple    =						val

   return orig_state_dict





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						timm.create_model(__A ,       pretrained=__A					)
   timm_model.eval()

   __lowerCAmelCase  :      str    =						get_swin_config(__A					)
   __lowerCAmelCase  :      Any    =						SwinForImageClassification(__A					)
   model.eval()

   __lowerCAmelCase  :      str    =						convert_state_dict(timm_model.state_dict() ,       __A					)
   model.load_state_dict(__A					)

   __lowerCAmelCase  :      str    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __lowerCAmelCase  :      Any    =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,       """-"""					)					)					)
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=__A ,       return_tensors="""pt"""					)

   __lowerCAmelCase  :      Tuple    =						timm_model(inputs["""pixel_values"""]					)
   __lowerCAmelCase  :      Dict    =						model(**__A					).logits

   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(__A					)

   print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
   image_processor.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin timm model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   __UpperCAmelCase								=			parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


