



"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : str						=			[]
						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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : int						=			[]
						token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""")    )
						return token
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Union[str, 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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[Any]						=			1_0_0_0

						snake_case_   : Any						=			"""huggingface/label-files"""
						snake_case_   : Tuple						=			num_labels
						snake_case_   : Dict						=			json.load(open(cached_download(hf_hub_url(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
						snake_case_   : str						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}

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

						snake_case_   : Dict						=			CvtConfig(num_labels=__UpperCamelCase					, idalabel=__UpperCamelCase					, labelaid=__UpperCamelCase    )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "13":
												snake_case_   : Any						=			[1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "21":
												snake_case_   : Any						=			[1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												snake_case_   : Optional[int]						=			[2, 2, 2_0]
												snake_case_   : str						=			[3, 1_2, 1_6]
												snake_case_   : Any						=			[1_9_2, 7_6_8, 1_0_2_4]

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

						snake_case_   : Any						=			OrderedDict()
						snake_case_   : Tuple						=			[]

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

						snake_case_   : str						=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__UpperCamelCase    )
						for i in range(len(__UpperCamelCase    )    ):
												snake_case_   : Union[str, Any]						=			original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(__UpperCamelCase    )
						model.save_pretrained(__UpperCamelCase    )
						image_processor.save_pretrained(__UpperCamelCase    )


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

if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[int]      					=					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.'''
					)

					__lowerCAmelCase       :					Dict      					=					parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			parent
								snake_case_   : Dict						=			batch_size
								snake_case_   : Any						=			seq_length
								snake_case_   : Tuple						=			is_training
								snake_case_   : Dict						=			use_attention_mask
								snake_case_   : int						=			use_token_type_ids
								snake_case_   : List[Any]						=			use_labels
								snake_case_   : List[str]						=			vocab_size
								snake_case_   : str						=			hidden_size
								snake_case_   : Optional[Any]						=			num_hidden_layers
								snake_case_   : List[Any]						=			num_attention_heads
								snake_case_   : Any						=			intermediate_size
								snake_case_   : Optional[Any]						=			hidden_act
								snake_case_   : Tuple						=			hidden_dropout_prob
								snake_case_   : List[Any]						=			attention_probs_dropout_prob
								snake_case_   : int						=			max_position_embeddings
								snake_case_   : Dict						=			type_vocab_size
								snake_case_   : Dict						=			type_sequence_label_size
								snake_case_   : Optional[Any]						=			initializer_range
								snake_case_   : Tuple						=			num_choices





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







								'''simple docstring'''

								snake_case_   : int						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : Tuple						=			None
								if self.use_attention_mask:
														snake_case_   : Optional[Any]						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : List[str]						=			None
								if self.use_token_type_ids:
														snake_case_   : int						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Tuple						=			RobertaPreLayerNormConfig(
								    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							,    is_decoder=_lowercase							,    initializer_range=self.initializer_range							,    )

								return config, input_ids, token_type_ids, attention_mask





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Any						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : int						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Any						=			config_and_inputs

								snake_case_   : Union[str, Any]						=			True
								snake_case_   : List[Any]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
								snake_case_   : Optional[int]						=			ids_tensor([self.batch_size, self.seq_length]							,    vocab_size=2   )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

		_lowerCamelCase			  =       (
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Any						=			FlaxRobertaPreLayerNormModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,    from_pt=_lowercase   )
														snake_case_   : Tuple						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,    from_pt=_lowercase   )

								snake_case_   : List[str]						=			np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							,    dtype=jnp.intaa   )
								snake_case_   : str						=			model(_lowercase   )[0]
								snake_case_   : int						=			[1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape   )							,    _lowercase   )
								# compare the actual values for a slice.
								snake_case_   : Tuple						=			np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]							,    dtype=np.floataa   )
								self.assertTrue(np.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Any						=			FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,    from_pt=_lowercase   )

								snake_case_   : Dict						=			np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							,    dtype=jnp.intaa   )
								snake_case_   : Any						=			model(_lowercase   )[0]
								# compare the actual values for a slice.
								snake_case_   : Optional[Any]						=			np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]							,    dtype=np.floataa   )
								self.assertTrue(np.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )

