




from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase_									=	logging.get_logger(__name__)


UpperCAmelCase_									=	OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

UpperCAmelCase_									=	OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCAmelCase_									=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_									=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_									=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_									=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_									=	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Optional[int]    		=   FLAX_MODEL_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModel)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Any    		=   FLAX_MODEL_FOR_PRETRAINING_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Dict    		=   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Union[str, Any]    		=   FLAX_MODEL_FOR_MASKED_LM_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	int    		=   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



UpperCAmelCase_									=	auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	List[str]    		=   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



UpperCAmelCase_									=	auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	List[Any]    		=   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Any    		=   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



UpperCAmelCase_									=	auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Union[str, Any]    		=   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Dict    		=   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



UpperCAmelCase_									=	auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Dict    		=   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



UpperCAmelCase_									=	auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	Tuple    		=   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



UpperCAmelCase_									=	auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')





class     lowerCamelCase__(					_BaseAutoModelClass):
      UpperCAmelCase__      :	List[str]    		=   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



UpperCAmelCase_									=	auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)






"""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      __UpperCAmelCase      (    __lowerCamelCase	)							->      Any:
		lowercase__   :    Optional[int]				=			[]
		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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Dict:
		lowercase__   :    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      __UpperCAmelCase      (    __lowerCamelCase	)							->      Tuple:
		lowercase__   :    List[str]				=			[]
		token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')	)
		return token



def      __UpperCAmelCase      (    )							->      Optional[int]:
		lowercase__   :    List[str]				=			[]
		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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:
		lowercase__   :    List[Any]				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Optional[Any]				=			10_00

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

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

		lowercase__   :    Any				=			CvtConfig(num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

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

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

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

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

		lowercase__   :    int				=			OrderedDict()
		lowercase__   :    List[Any]				=			[]

		for idx in range(len(config.depth	)	):
				if config.cls_token[idx]:
						lowercase__   :    Any				=			list_of_state_dict + cls_token(__lowerCamelCase	)
				lowercase__   :    Any				=			list_of_state_dict + embeddings(__lowerCamelCase	)
				for cnt in range(config.depth[idx]	):
						lowercase__   :    Tuple				=			list_of_state_dict + attention(__lowerCamelCase						,     __lowerCamelCase	)

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

		model.load_state_dict(__lowerCamelCase	)
		model.save_pretrained(__lowerCamelCase	)
		image_processor.save_pretrained(__lowerCamelCase	)


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

if __name__ == "__main__":
							lowerCAmelCase_   						=   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_   						=   parser.parse_args()
							convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
