




'''simple docstring'''


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase_      :  int				     =  random.Random()



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Optional[Any]=1.0 ,    UpperCamelCase__    :					Any=None ,    UpperCamelCase__    :					Any=None				):




     """simple docstring"""


     if rng is None:
          a_     :							Optional[Any]     						=  global_rng

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

     return values







class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def __init__( self   :  List[str]						,						lowercase__   :  Optional[int]						,						lowercase__   :  Optional[int]=7						,						lowercase__   :  Optional[int]=400						,						lowercase__   :  Any=2000						,						lowercase__   :  Any=1						,						lowercase__   :  Optional[Any]=0.0						,						lowercase__   :  List[str]=1_6000						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  List[Any]=True						,						):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Tuple=False						,						lowercase__   :  Optional[Any]=False       ):


           '''simple docstring'''

           def _flatten(lowercase__   :  Dict       ):
                return list(itertools.chain(*_lowercase       )       )

           if equal_length:
                a_     :							Tuple     						=  floats_list((self.batch_size, self.max_seq_length)       )
           else:
                # make sure that inputs increase in size
                a_     :							Dict     						=  [
                    _flatten(floats_list((x, self.feature_size)       )       )
                    for x in range(self.min_seq_length						,						self.max_seq_length						,						self.seq_length_diff       )
                ]

           if numpify:
                a_     :							List[str]     						=  [np.asarray(_lowercase       ) for x in speech_inputs]

           return speech_inputs







class      SCREAMING_SNAKE_CASE    (						lowercase_							,      unittest.TestCase				):
      __magic_name__  :       List[Any]   							=					WavaVecaFeatureExtractor






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  WavaVecaFeatureExtractionTester(self       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           self.assertTrue(np.all(np.mean(_lowercase						,						axis=0       ) < 1e-3       )       )
           self.assertTrue(np.all(np.abs(np.var(_lowercase						,						axis=0       ) - 1       ) < 1e-3       )       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           # create three inputs of length 800, 1000, and 1200
           a_     :							Any     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							Dict     						=  [np.asarray(_lowercase       ) for speech_input in speech_inputs]

           # Test not batched input
           a_     :							Union[str, Any]     						=  feat_extract(speech_inputs[0]						,						return_tensors="""np"""       ).input_values
           a_     :							List[Any]     						=  feat_extract(np_speech_inputs[0]						,						return_tensors="""np"""       ).input_values
           self.assertTrue(np.allclose(_lowercase						,						_lowercase						,						atol=1e-3       )       )

           # Test batched
           a_     :							List[Any]     						=  feat_extract(_lowercase						,						return_tensors="""np"""       ).input_values
           a_     :							Tuple     						=  feat_extract(_lowercase						,						return_tensors="""np"""       ).input_values
           for enc_seq_a, enc_seq_a in zip(_lowercase						,						_lowercase       ):
                self.assertTrue(np.allclose(_lowercase						,						_lowercase						,						atol=1e-3       )       )

           # Test 2-D numpy arrays are batched.
           a_     :							List[str]     						=  [floats_list((1, x)       )[0] for x in (800, 800, 800)]
           a_     :							List[str]     						=  np.asarray(_lowercase       )
           a_     :							Any     						=  feat_extract(_lowercase						,						return_tensors="""np"""       ).input_values
           a_     :							int     						=  feat_extract(_lowercase						,						return_tensors="""np"""       ).input_values
           for enc_seq_a, enc_seq_a in zip(_lowercase						,						_lowercase       ):
                self.assertTrue(np.allclose(_lowercase						,						_lowercase						,						atol=1e-3       )       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Any     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							Any     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]

           a_     :							Tuple     						=  ["""longest""", """max_length""", """do_not_pad"""]
           a_     :							Union[str, Any]     						=  [None, 1600, None]
           for max_length, padding in zip(_lowercase						,						_lowercase       ):
                a_     :							Any     						=  feat_extract(_lowercase						,						padding=_lowercase						,						max_length=_lowercase						,						return_tensors="""np"""       )
                a_     :							int     						=  processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:800]       )
                self.assertTrue(input_values[0][800:].sum() < 1e-6       )
                self._check_zero_mean_unit_variance(input_values[1][:1000]       )
                self.assertTrue(input_values[0][1000:].sum() < 1e-6       )
                self._check_zero_mean_unit_variance(input_values[2][:1200]       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							List[str]     						=  range(800						,						1400						,						200       )
           a_     :							int     						=  [floats_list((1, x)       )[0] for x in lengths]

           a_     :							str     						=  ["""longest""", """max_length""", """do_not_pad"""]
           a_     :							List[Any]     						=  [None, 1600, None]

           for max_length, padding in zip(_lowercase						,						_lowercase       ):
                a_     :							Optional[int]     						=  feat_extract(_lowercase						,						max_length=_lowercase						,						padding=_lowercase       )
                a_     :							Dict     						=  processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:800]       )
                self._check_zero_mean_unit_variance(input_values[1][:1000]       )
                self._check_zero_mean_unit_variance(input_values[2][:1200]       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							List[str]     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							int     						=  feat_extract(
               _lowercase						,						truncation=_lowercase						,						max_length=1000						,						padding="""max_length"""						,						return_tensors="""np"""       )
           a_     :							str     						=  processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]       )
           self._check_zero_mean_unit_variance(input_values[1]       )
           self._check_zero_mean_unit_variance(input_values[2]       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							Dict     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							int     						=  feat_extract(
               _lowercase						,						truncation=_lowercase						,						max_length=1000						,						padding="""longest"""						,						return_tensors="""np"""       )
           a_     :							str     						=  processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]       )
           self._check_zero_mean_unit_variance(input_values[1, :1000]       )
           self._check_zero_mean_unit_variance(input_values[2]       )

           # make sure that if max_length < longest -> then pad to max_length
           self.assertTrue(input_values.shape == (3, 1000)       )

           a_     :							List[str]     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							Tuple     						=  feat_extract(
               _lowercase						,						truncation=_lowercase						,						max_length=2000						,						padding="""longest"""						,						return_tensors="""np"""       )
           a_     :							Optional[Any]     						=  processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]       )
           self._check_zero_mean_unit_variance(input_values[1, :1000]       )
           self._check_zero_mean_unit_variance(input_values[2]       )

           # make sure that if max_length > longest -> then pad to longest
           self.assertTrue(input_values.shape == (3, 1200)       )






      @require_torch
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           import torch

           a_     :							Tuple     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							Any     						=  np.random.rand(100       ).astype(np.floataa       )
           a_     :							Optional[int]     						=  np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                a_     :							Any     						=  feature_extractor.pad([{"""input_values""": inputs}]						,						return_tensors="""np"""       )
                self.assertTrue(np_processed.input_values.dtype == np.floataa       )
                a_     :							Optional[int]     						=  feature_extractor.pad([{"""input_values""": inputs}]						,						return_tensors="""pt"""       )
                self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )






      @slow
      @require_torch
      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                a_     :							Optional[Any]     						=  WavaVecaConfig.from_pretrained(_lowercase       )
                a_     :							Tuple     						=  WavaVecaFeatureExtractor.from_pretrained(_lowercase       )

                # only "layer" feature extraction norm should make use of
                # attention_mask
                self.assertEqual(feat_extract.return_attention_mask						,						config.feat_extract_norm == """layer"""       )



'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128s'		,pretrained=lowerCAmelCase__   )
																					else:
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_192'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_256'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_384'		,pretrained=lowerCAmelCase__   )

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

														SCREAMING_SNAKE_CASE_				=				from_model.state_dict()
														SCREAMING_SNAKE_CASE_				=				list(from_model.state_dict().keys()   )
														SCREAMING_SNAKE_CASE_				=				list(our_model.state_dict().keys()   )
														print(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
														for i in range(len(lowerCAmelCase__   )   ):
																					SCREAMING_SNAKE_CASE_				=				weights[og_keys[i]]
														our_model.load_state_dict(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

							assert torch.allclose(lowerCAmelCase__		,lowerCAmelCase__   ), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name   )
														SCREAMING_SNAKE_CASE_				=				LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name   )

														print(F"""Pushed {checkpoint_name}"""   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

							SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
							SCREAMING_SNAKE_CASE_				=				num_labels
							SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
							SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				idalabel
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 6, 8]		,depths=[2, 3, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 8, 12]		,depths=[4, 4, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]		,num_attention_heads=[3, 5, 6]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]		,num_attention_heads=[4, 6, 8]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]		,num_attention_heads=[6, 9, 12]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0.1		,),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				str     	=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default=None,
							    type=str,
							    help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="levit-dump-folder/",
							    type=Path,
							    required=False,
							    help="Path to the output PyTorch model directory.",
							)
							parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
							parser.add_argument(
							    "--no-push_to_hub",
							    dest="push_to_hub",
							    action="store_false",
							    help="Do not push model and image processor to the hub",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				Path     	=			args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





