
from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerModel,
  )
  from transformers.models.roformer.modeling_tf_roformer import (
      TFRoFormerSelfAttention,
      TFRoFormerSinusoidalPositionalEmbedding,
  )

class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=13		,snake_case=7		,snake_case=True		,snake_case=True		,snake_case=True		,snake_case=True		,snake_case=99		,snake_case=32		,snake_case=2		,snake_case=4		,snake_case=37		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=16		,snake_case=2		,snake_case=0.02		,snake_case=3		,snake_case=4		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      parent
           lowercase							:     List[Any]       	=      13
           lowercase							:     Any       	=      7
           lowercase							:     Union[str, Any]       	=      True
           lowercase							:     Dict       	=      True
           lowercase							:     List[Any]       	=      True
           lowercase							:     List[str]       	=      True
           lowercase							:     Any       	=      99
           lowercase							:     Tuple       	=      32
           lowercase							:     Optional[Any]       	=      2
           lowercase							:     Tuple       	=      4
           lowercase							:     Tuple       	=      37
           lowercase							:     Optional[int]       	=      """gelu"""
           lowercase							:     List[str]       	=      0.1
           lowercase							:     Tuple       	=      0.1
           lowercase							:     Union[str, Any]       	=      512
           lowercase							:     List[str]       	=      16
           lowercase							:     Optional[int]       	=      2
           lowercase							:     str       	=      0.02
           lowercase							:     int       	=      3
           lowercase							:     Optional[Any]       	=      4
           lowercase							:     str       	=      None
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size      )

           lowercase							:     Tuple       	=      None
           if self.use_input_mask:
                  lowercase							:     Dict       	=      random_attention_mask([self.batch_size, self.seq_length]      )

           lowercase							:     Any       	=      None
           if self.use_token_type_ids:
                  lowercase							:     Union[str, Any]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.type_vocab_size      )

           lowercase							:     List[str]       	=      None
           lowercase							:     Optional[int]       	=      None
           lowercase							:     Any       	=      None
           if self.use_labels:
                  lowercase							:     Union[str, Any]       	=      ids_tensor([self.batch_size]		,self.type_sequence_label_size      )
                  lowercase							:     Any       	=      ids_tensor([self.batch_size, self.seq_length]		,self.num_labels      )
                  lowercase							:     str       	=      ids_tensor([self.batch_size]		,self.num_choices      )

           lowercase							:     Any       	=      RoFormerConfig(
               vocab_size=self.vocab_size		,hidden_size=self.hidden_size		,num_hidden_layers=self.num_hidden_layers		,num_attention_heads=self.num_attention_heads		,intermediate_size=self.intermediate_size		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,attention_probs_dropout_prob=self.attention_probs_dropout_prob		,max_position_embeddings=self.max_position_embeddings		,type_vocab_size=self.type_vocab_size		,initializer_range=self.initializer_range		,return_dict=snake_case		,)

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      TFRoFormerModel(config=snake_case      )
           lowercase							:     List[str]       	=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

           lowercase							:     Tuple       	=      [input_ids, input_mask]
           lowercase							:     Any       	=      model(snake_case      )

           lowercase							:     Any       	=      model(snake_case      )

           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      True
           lowercase							:     Dict       	=      TFRoFormerForCausalLM(config=snake_case      )
           lowercase							:     Dict       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           lowercase							:     Optional[int]       	=      model(snake_case      )["""logits"""]
           self.parent.assertListEqual(
               list(prediction_scores.numpy().shape      )		,[self.batch_size, self.seq_length, self.vocab_size]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      TFRoFormerForMaskedLM(config=snake_case      )
           lowercase							:     List[Any]       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           lowercase							:     Dict       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.num_labels
           lowercase							:     Any       	=      TFRoFormerForSequenceClassification(config=snake_case      )
           lowercase							:     int       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           lowercase							:     Tuple       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.num_choices
           lowercase							:     Tuple       	=      TFRoFormerForMultipleChoice(config=snake_case      )
           lowercase							:     Any       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Union[str, Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      {
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           lowercase							:     Dict       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_choices)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.num_labels
           lowercase							:     List[str]       	=      TFRoFormerForTokenClassification(config=snake_case      )
           lowercase							:     int       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           lowercase							:     List[str]       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFRoFormerForQuestionAnswering(config=snake_case      )
           lowercase							:     Optional[Any]       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           lowercase							:     Dict       	=      model(snake_case      )
           self.parent.assertEqual(result.start_logits.shape		,(self.batch_size, self.seq_length)      )
           self.parent.assertEqual(result.end_logits.shape		,(self.batch_size, self.seq_length)      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.prepare_config_and_inputs()
           (
               (
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			
           )							:     List[str]       	=      config_and_inputs
           lowercase							:     List[str]       	=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict






@require_tf
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Tuple=				(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _a		:						Dict=				(
        {
            "feature-extraction": TFRoFormerModel,
            "fill-mask": TFRoFormerForMaskedLM,
            "question-answering": TFRoFormerForQuestionAnswering,
            "text-classification": TFRoFormerForSequenceClassification,
            "text-generation": TFRoFormerForCausalLM,
            "token-classification": TFRoFormerForTokenClassification,
            "zero-shot": TFRoFormerForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    _a		:						List[Any]=				False
    _a		:						Optional[Any]=				False
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                  return True

           return False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      TFRoFormerModelTester(self      )
           lowercase							:     Optional[Any]       	=      ConfigTester(self		,config_class=snake_case		,hidden_size=37      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""      )
           self.assertIsNotNone(snake_case      )






@require_tf
class __snake_case		(      unittest.TestCase     ):
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""      )
           lowercase							:     Optional[int]       	=      tf.constant([[0, 1, 2, 3, 4, 5]]      )
           lowercase							:     Tuple       	=      model(snake_case      )[0]

           # TODO Replace vocab size
           lowercase							:     int       	=      50000

           lowercase							:     Optional[Any]       	=      [1, 6, vocab_size]
           self.assertEqual(output.shape		,snake_case      )

           print(output[:, :3, :3]      )

           # TODO Replace values below with what was printed above.
           lowercase							:     Any       	=      tf.constant(
               [
                   [
                       [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                       [-1.5_133_783, 0.197_433, 0.15_190_607],
                       [-5.0_135_403, -3.900_256, -0.84_038_764],
                   ]
               ]      )
           tf.debugging.assert_near(output[:, :3, :3]		,snake_case		,atol=1e-4      )






@require_tf
class __snake_case		(      unittest.TestCase     ):
    _a		:						Dict=				1E-4
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      tf.constant([[4, 10]]      )
           lowercase							:     int       	=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,embedding_dim=6      )

           lowercase							:     Optional[Any]       	=      emba(input_ids.shape      )
           lowercase							:     Optional[int]       	=      tf.constant(
               [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]      )

           tf.debugging.assert_near(snake_case		,snake_case		,atol=self.tolerance      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      tf.constant(
               [
                   [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
                   [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
                   [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
               ]      )
           lowercase							:     str       	=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=512		,embedding_dim=512      )
           emba([2, 16, 512]      )
           lowercase							:     Union[str, Any]       	=      emba.weight[:3, :5]

           tf.debugging.assert_near(snake_case		,snake_case		,atol=self.tolerance      )






@require_tf
class __snake_case		(      unittest.TestCase     ):
    _a		:						int=				1E-4
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      tf.reshape(tf.range(2 * 12 * 16 * 64		,dtype=tf.floataa      )		,shape=(2, 12, 16, 64)      ) / 100

           lowercase							:     Tuple       	=      -tf.reshape(tf.range(2 * 12 * 16 * 64		,dtype=tf.floataa      )		,shape=(2, 12, 16, 64)      ) / 100

           lowercase							:     Tuple       	=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,embedding_dim=64      )
           lowercase							:     Union[str, Any]       	=      embed_positions([2, 16, 768]      )[None, None, :, :]

           lowercase	,			lowercase							:     Union[str, Any]       	=      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
               snake_case		,snake_case		,snake_case      )

           lowercase							:     Optional[Any]       	=      tf.constant(
               [
                   [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
                   [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
                   [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
                   [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
                   [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
                   [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
               ]      )
           lowercase							:     Optional[Any]       	=      tf.constant(
               [
                   [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
                   [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
                   [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
                   [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
                   [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
                   [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
               ]      )

           tf.debugging.assert_near(query_layer[0, 0, :6, :8]		,snake_case		,atol=self.tolerance      )
           tf.debugging.assert_near(key_layer[0, 0, :6, :8]		,snake_case		,atol=self.tolerance      )


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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      []
       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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Optional[Any]       	=      []
       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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      []
       token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")				)
       return token







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Optional[Any]       	=      []
       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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      """imagenet-1k-id2label.json"""
       lowercase							:     List[str]       	=      1_000

       lowercase							:     int       	=      """huggingface/label-files"""
       lowercase							:     Union[str, Any]       	=      num_labels
       lowercase							:     Optional[Any]       	=      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="""dataset"""				)				)					,	"""r"""				)				)
       lowercase							:     List[Any]       	=      {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}

       lowercase							:     Dict       	=      idalabel
       lowercase							:     List[str]       	=      {v: k for k, v in idalabel.items()}

       lowercase							:     List[str]       	=      CvtConfig(num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__				)

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

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

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              lowercase							:     int       	=      [2, 2, 20]
              lowercase							:     Optional[int]       	=      [3, 12, 16]
              lowercase							:     str       	=      [192, 768, 1_024]

       lowercase							:     Union[str, Any]       	=      CvtForImageClassification(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				)
       lowercase							:     Optional[Any]       	=      image_size
       lowercase							:     Union[str, Any]       	=      torch.load(SCREAMING_SNAKE_CASE__					,	map_location=torch.device("""cpu"""				)				)

       lowercase							:     Optional[Any]       	=      OrderedDict()
       lowercase							:     Tuple       	=      []

       for idx in range(len(config.depth				)				):
              if config.cls_token[idx]:
                     lowercase							:     Optional[Any]       	=      list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE__				)
              lowercase							:     str       	=      list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE__				)
              for cnt in range(config.depth[idx]				):
                     lowercase							:     List[str]       	=      list_of_state_dict + attention(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     Optional[Any]       	=      original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


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

if __name__ == "__main__":
  lowercase  :					Tuple						    =							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=384,
      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."""
  )

  lowercase  :					Optional[int]						    =							parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
