






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class 			__a  (    a_       ):
 # to overwrite at feature extractactor specific tests
 _lowerCAmelCase       :       Optional[int]        = None
 _lowerCAmelCase       :       List[Any]        = None







 @property
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      return self.feat_extract_tester.prepare_feat_extract_dict()







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      self.assertTrue(hasattr(_lowerCamelCase							,   "feature_size"						)						)
      self.assertTrue(hasattr(_lowerCamelCase							,   "sampling_rate"						)						)
      self.assertTrue(hasattr(_lowerCamelCase							,   "padding_value"						)						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.feat_extract_tester.prepare_inputs_for_common()
      UpperCamelCase__			:   Dict       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   List[str]       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   Dict       				=  BatchFeature({input_name: speech_inputs}						)

      self.assertTrue(all(len(_lowerCamelCase						) == len(_lowerCamelCase						) for x, y in zip(_lowerCamelCase							,   processed_features[input_name]						)						)						)

      UpperCamelCase__			:   List[str]       				=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase						)
      UpperCamelCase__			:   int       				=  BatchFeature({input_name: speech_inputs}							,   tensor_type="np"						)

      UpperCamelCase__			:   List[Any]       				=  processed_features[input_name]

      if len(batch_features_input.shape						) < 3:
           UpperCamelCase__			:   Any       				=  batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.feature_size)						)







 @require_torch
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase						)
      UpperCamelCase__			:   Tuple       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   Any       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   Optional[Any]       				=  BatchFeature({input_name: speech_inputs}							,   tensor_type="pt"						)

      UpperCamelCase__			:   Tuple       				=  processed_features[input_name]

      if len(batch_features_input.shape						) < 3:
           UpperCamelCase__			:   Optional[Any]       				=  batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.feature_size)						)







 @require_tf
 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   List[Any]       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   Optional[int]       				=  BatchFeature({input_name: speech_inputs}							,   tensor_type="tf"						)

      UpperCamelCase__			:   Optional[Any]       				=  processed_features[input_name]

      if len(batch_features_input.shape						) < 3:
           UpperCamelCase__			:   Optional[int]       				=  batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.feature_size)						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=False						):


      '''simple docstring'''






      def _inputs_have_equal_length(SCREAMING_SNAKE_CASE					:   Optional[Any]						):
           UpperCamelCase__			:   Union[str, Any]       				=  len(input[0]						)
           for input_slice in input[1:]:
                if len(_lowerCamelCase						) != length:
                     return False
           return True

      def _inputs_are_equal(SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[str]						):
           if len(_lowerCamelCase						) != len(_lowerCamelCase						):
                return False

           for input_slice_a, input_slice_a in zip(_lowerCamelCase							,   _lowerCamelCase						):
                if not np.allclose(np.asarray(_lowerCamelCase						)							,   np.asarray(_lowerCamelCase						)							,   atol=1e-3						):
                     return False
           return True

      UpperCamelCase__			:   Dict       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase						)
      UpperCamelCase__			:   Tuple       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   int       				=  BatchFeature({input_name: speech_inputs}						)

      UpperCamelCase__			:   Optional[Any]       				=  self.feat_extract_tester.seq_length_diff
      UpperCamelCase__			:   Union[str, Any]       				=  self.feat_extract_tester.max_seq_length + pad_diff
      UpperCamelCase__			:   Any       				=  self.feat_extract_tester.min_seq_length
      UpperCamelCase__			:   Optional[int]       				=  self.feat_extract_tester.batch_size
      UpperCamelCase__			:   Tuple       				=  self.feat_extract_tester.feature_size

      # test padding for List[int] + numpy
      UpperCamelCase__			:   List[Any]       				=  feat_extract.pad(_lowerCamelCase							,   padding=_lowerCamelCase						)
      UpperCamelCase__			:   List[Any]       				=  input_a[input_name]

      UpperCamelCase__			:   Optional[Any]       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"						)
      UpperCamelCase__			:   Optional[int]       				=  input_a[input_name]

      UpperCamelCase__			:   List[str]       				=  feat_extract.pad(_lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[-1]						)						)
      UpperCamelCase__			:   Optional[int]       				=  input_a[input_name]

      UpperCamelCase__			:   Dict       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   return_tensors="np"						)
      UpperCamelCase__			:   Optional[int]       				=  input_a[input_name]

      # max_length parameter has to be provided when setting `padding="max_length"`
      with self.assertRaises(_lowerCamelCase						):
           feat_extract.pad(_lowerCamelCase							,   padding="max_length"						)[input_name]

      UpperCamelCase__			:   List[Any]       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=_lowerCamelCase							,   return_tensors="np"						)
      UpperCamelCase__			:   Optional[Any]       				=  input_a[input_name]

      self.assertFalse(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(_inputs_are_equal(_lowerCamelCase							,   _lowerCamelCase						)						)
      self.assertTrue(len(input_a[0]						) == pad_min_length						)
      self.assertTrue(len(input_a[1]						) == pad_min_length + pad_diff						)
      self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]						))						)
      self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)						)

      if feature_size > 1:
           self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size						)

      # test padding for `pad_to_multiple_of` for List[int] + numpy
      UpperCamelCase__			:   int       				=  feat_extract.pad(_lowerCamelCase							,   pad_to_multiple_of=10						)
      UpperCamelCase__			:   Union[str, Any]       				=  input_a[input_name]

      UpperCamelCase__			:   List[Any]       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   pad_to_multiple_of=10						)
      UpperCamelCase__			:   Optional[int]       				=  input_a[input_name]

      UpperCamelCase__			:   int       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   pad_to_multiple_of=10							,   max_length=_lowerCamelCase						)
      UpperCamelCase__			:   str       				=  input_a[input_name]

      UpperCamelCase__			:   Union[str, Any]       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   pad_to_multiple_of=10							,   max_length=_lowerCamelCase							,   return_tensors="np"							,   )
      UpperCamelCase__			:   Optional[Any]       				=  input_a[input_name]

      self.assertTrue(all(len(_lowerCamelCase						) % 10 == 0 for x in input_a						)						)
      self.assertTrue(_inputs_are_equal(_lowerCamelCase							,   _lowerCamelCase						)						)

      UpperCamelCase__			:   Tuple       				=  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
      self.assertTrue(all(len(_lowerCamelCase						) == expected_mult_pad_length for x in input_a						)						)
      self.assertEqual(input_a.shape[:2]							,   (batch_size, expected_mult_pad_length)						)

      if feature_size > 1:
           self.assertTrue(input_a.shape[2] == feature_size						)

      # Check padding value is correct
      UpperCamelCase__			:   Optional[Any]       				=  (np.ones(self.feat_extract_tester.feature_size						) * feat_extract.padding_value).sum()
      self.assertTrue(
          abs(np.asarray(input_a[0]						)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)						)
          < 1e-3						)
      self.assertTrue(
          abs(
              np.asarray(input_a[1]						)[pad_min_length + pad_diff :].sum()
              - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)						)
          < 1e-3						)
      self.assertTrue(
          abs(
              np.asarray(input_a[2]						)[pad_min_length + 2 * pad_diff :].sum()
              - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)						)
          < 1e-3						)
      self.assertTrue(
          abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)						) < 1e-3						)
      self.assertTrue(
          abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)						)
          < 1e-3						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[Any]=False						):


      '''simple docstring'''






      def _inputs_have_equal_length(SCREAMING_SNAKE_CASE					:   Union[str, Any]						):
           UpperCamelCase__			:   Any       				=  len(input[0]						)
           for input_slice in input[1:]:
                if len(_lowerCamelCase						) != length:
                     return False
           return True

      def _inputs_are_equal(SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):
           if len(_lowerCamelCase						) != len(_lowerCamelCase						):
                return False

           for input_slice_a, input_slice_a in zip(_lowerCamelCase							,   _lowerCamelCase						):
                if not np.allclose(np.asarray(_lowerCamelCase						)							,   np.asarray(_lowerCamelCase						)							,   atol=1e-3						):
                     return False
           return True

      UpperCamelCase__			:   int       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   Dict       				=  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase						)
      UpperCamelCase__			:   Optional[int]       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   List[str]       				=  BatchFeature({input_name: speech_inputs}						)

      # truncate to smallest
      UpperCamelCase__			:   str       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[0]						)							,   truncation=_lowerCamelCase						)
      UpperCamelCase__			:   Optional[Any]       				=  input_a[input_name]

      UpperCamelCase__			:   Union[str, Any]       				=  feat_extract.pad(_lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[0]						)						)
      UpperCamelCase__			:   Dict       				=  input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertFalse(_inputs_have_equal_length(_lowerCamelCase						)						)

      # truncate to smallest with np
      UpperCamelCase__			:   int       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[0]						)							,   return_tensors="np"							,   truncation=_lowerCamelCase							,   )
      UpperCamelCase__			:   Optional[Any]       				=  input_a[input_name]

      UpperCamelCase__			:   Tuple       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[0]						)							,   return_tensors="np"						)
      UpperCamelCase__			:   Optional[int]       				=  input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(input_a.shape[1] == len(speech_inputs[0]						)						)

      # since truncation forces padding to be smaller than longest input
      # function can't return `np.ndarray`, but has to return list
      self.assertFalse(_inputs_have_equal_length(_lowerCamelCase						)						)

      # truncate to middle
      UpperCamelCase__			:   List[Any]       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[1]						)							,   truncation=_lowerCamelCase							,   return_tensors="np"							,   )
      UpperCamelCase__			:   Any       				=  input_a[input_name]

      UpperCamelCase__			:   Tuple       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[1]						)							,   truncation=_lowerCamelCase						)
      UpperCamelCase__			:   List[Any]       				=  input_a[input_name]

      UpperCamelCase__			:   int       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[1]						)							,   return_tensors="np"						)
      UpperCamelCase__			:   int       				=  input_a[input_name]

      self.assertTrue(input_a.shape[1] == len(speech_inputs[1]						)						)
      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(_inputs_are_equal(_lowerCamelCase							,   _lowerCamelCase						)						)

      # since truncation forces padding to be smaller than longest input
      # function can't return `np.ndarray`, but has to return list
      self.assertFalse(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertTrue(len(input_a[-1]						) == len(speech_inputs[-1]						)						)

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(_lowerCamelCase						):
           feat_extract.pad(_lowerCamelCase							,   truncation=_lowerCamelCase						)[input_name]

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(_lowerCamelCase						):
           feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   truncation=_lowerCamelCase						)[input_name]

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(_lowerCamelCase						):
           feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   truncation=_lowerCamelCase						)[input_name]

      # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
      with self.assertRaises(_lowerCamelCase						):
           feat_extract.pad(_lowerCamelCase							,   padding="max_length"							,   truncation=_lowerCamelCase						)[input_name]

      # test truncation for `pad_to_multiple_of` for List[int] + numpy
      UpperCamelCase__			:   Union[str, Any]       				=  12
      UpperCamelCase__			:   int       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[0]						)							,   pad_to_multiple_of=_lowerCamelCase							,   truncation=_lowerCamelCase							,   )
      UpperCamelCase__			:   List[Any]       				=  input_a[input_name]

      UpperCamelCase__			:   Optional[Any]       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=len(speech_inputs[0]						)							,   pad_to_multiple_of=_lowerCamelCase							,   )
      UpperCamelCase__			:   Any       				=  input_a[input_name]

      # retrieve expected_length as multiple of pad_to_multiple_of
      UpperCamelCase__			:   int       				=  len(speech_inputs[0]						)
      if expected_length % pad_to_multiple_of != 0:
           UpperCamelCase__			:   Optional[int]       				=  ((len(speech_inputs[0]						) // pad_to_multiple_of) + 1) * pad_to_multiple_of

      self.assertTrue(len(input_a[0]						) == expected_length						)
      self.assertTrue(_inputs_have_equal_length(_lowerCamelCase						)						)
      self.assertFalse(_inputs_have_equal_length(_lowerCamelCase						)						)







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      self._check_padding(numpify=_lowerCamelCase						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      self._check_padding(numpify=_lowerCamelCase						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      self._check_truncation(numpify=_lowerCamelCase						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      self._check_truncation(numpify=_lowerCamelCase						)







 @require_torch
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   List[Any]       				=  self.feat_extract_tester.prepare_inputs_for_common()
      UpperCamelCase__			:   Tuple       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   Optional[Any]       				=  BatchFeature({input_name: speech_inputs}						)

      UpperCamelCase__			:   Union[str, Any]       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   return_tensors="np"						)[input_name]
      UpperCamelCase__			:   Any       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   return_tensors="pt"						)[input_name]

      self.assertTrue(abs(input_np.astype(np.floataa						).sum() - input_pt.numpy().astype(np.floataa						).sum()						) < 1e-2						)







 @require_tf
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.feature_extraction_class(**self.feat_extract_dict						)
      UpperCamelCase__			:   int       				=  self.feat_extract_tester.prepare_inputs_for_common()
      UpperCamelCase__			:   Dict       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   str       				=  BatchFeature({input_name: speech_inputs}						)

      UpperCamelCase__			:   List[Any]       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   return_tensors="np"						)[input_name]
      UpperCamelCase__			:   Optional[int]       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   return_tensors="tf"						)[input_name]

      self.assertTrue(abs(input_np.astype(np.floataa						).sum() - input_tf.numpy().astype(np.floataa						).sum()						) < 1e-2						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.feat_extract_dict
      UpperCamelCase__			:   List[str]       				=  True
      UpperCamelCase__			:   Union[str, Any]       				=  self.feature_extraction_class(**_lowerCamelCase						)
      UpperCamelCase__			:   Tuple       				=  self.feat_extract_tester.prepare_inputs_for_common()
      UpperCamelCase__			:   List[str]       				=  [len(_lowerCamelCase						) for x in speech_inputs]
      UpperCamelCase__			:   Any       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   Tuple       				=  BatchFeature({input_name: speech_inputs}						)

      UpperCamelCase__			:   str       				=  feat_extract.pad(_lowerCamelCase							,   padding="longest"							,   return_tensors="np"						)
      self.assertIn("attention_mask"							,   _lowerCamelCase						)
      self.assertListEqual(list(processed.attention_mask.shape						)							,   list(processed[input_name].shape[:2]						)						)
      self.assertListEqual(processed.attention_mask.sum(-1						).tolist()							,   _lowerCamelCase						)







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.feat_extract_dict
      UpperCamelCase__			:   List[str]       				=  True
      UpperCamelCase__			:   List[str]       				=  self.feature_extraction_class(**_lowerCamelCase						)
      UpperCamelCase__			:   int       				=  self.feat_extract_tester.prepare_inputs_for_common()
      UpperCamelCase__			:   Any       				=  [len(_lowerCamelCase						) for x in speech_inputs]
      UpperCamelCase__			:   List[Any]       				=  feat_extract.model_input_names[0]

      UpperCamelCase__			:   Dict       				=  BatchFeature({input_name: speech_inputs}						)
      UpperCamelCase__			:   List[Any]       				=  min(_lowerCamelCase						)

      UpperCamelCase__			:   Union[str, Any]       				=  feat_extract.pad(
          _lowerCamelCase							,   padding="max_length"							,   max_length=_lowerCamelCase							,   truncation=_lowerCamelCase							,   return_tensors="np"						)
      self.assertIn("attention_mask"							,   _lowerCamelCase						)
      self.assertListEqual(
          list(processed_pad.attention_mask.shape						)							,   [processed_pad[input_name].shape[0], max_length]						)
      self.assertListEqual(
          processed_pad.attention_mask[:, :max_length].sum(-1						).tolist()							,   [max_length for x in speech_inputs]						)







"""simple docstring"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
          f'stage{idx}.patch_embed.proj.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
          f'stage{idx}.patch_embed.proj.bias',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
          f'stage{idx}.patch_embed.norm.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
          f'stage{idx}.patch_embed.norm.bias',
      )    )
  return embed
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj.bias',
      )    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')    )
  return attention_weights
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')    )
  return token
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       []
  head.append(('layernorm.weight', 'norm.weight')    )
  head.append(('layernorm.bias', 'norm.bias')    )
  head.append(('classifier.weight', 'head.weight')    )
  head.append(('classifier.bias', 'head.bias')    )
  return head
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

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

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

  lowercase							    =       lowercase							    =       CvtConfig(num_labels=__snake_case		,					idalabel=__snake_case		,					labelaid=__snake_case    )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'		,					1    )[-1][4:6] == "13":
    lowercase							    =       [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'		,					1    )[-1][4:6] == "21":
    lowercase							    =       [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowercase							    =       [2, 2, 20]
    lowercase							    =       [3, 12, 16]
    lowercase							    =       [1_92, 7_68, 10_24]

  lowercase							    =       CvtForImageClassification(__snake_case    )
  lowercase							    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
  lowercase							    =       image_size
  lowercase							    =       torch.load(__snake_case		,					map_location=torch.device('cpu'    )    )

  lowercase							    =       OrderedDict()
  lowercase							    =       []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      lowercase							    =       list_of_state_dict + cls_token(__snake_case    )
    lowercase							    =       list_of_state_dict + embeddings(__snake_case    )
    for cnt in range(config.depth[idx]    ):
      lowercase							    =       list_of_state_dict + attention(__snake_case		,					__snake_case    )

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__snake_case    )
  model.save_pretrained(__snake_case    )
  image_processor.save_pretrained(__snake_case    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCamelCase		:  Optional[Any]						=		argparse.ArgumentParser()
    parser.add_argument(
        '--cvt_model',
        default='cvt-w24',
        type=str,
        help='Name of the cvt model you\'d like to convert.',
    )
    parser.add_argument(
        '--image_size',
        default=3_8_4,
        type=int,
        help='Input Image Size',
    )
    parser.add_argument(
        '--cvt_file_name',
        default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
        type=str,
        help='Input Image Size',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


