


'''simple docstring'''







import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
  from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A=None      ,			__A=None      ,			__A=None      ,			__A=None      ,			__A=None      ,			)							->		Union[str, Any]:


      '''simple docstring'''



      if attention_mask is None:
            UpperCamelCase__										=				input_ids.ne(config.pad_token_id    )
      if decoder_attention_mask is None:
            UpperCamelCase__										=				decoder_input_ids.ne(config.pad_token_id    )
      if head_mask is None:
            UpperCamelCase__										=				torch.ones(config.encoder_layers      ,			config.encoder_attention_heads      ,			device=_lowerCamelCase    )
      if decoder_head_mask is None:
            UpperCamelCase__										=				torch.ones(config.decoder_layers      ,			config.decoder_attention_heads      ,			device=_lowerCamelCase    )
      if cross_attn_head_mask is None:
            UpperCamelCase__										=				torch.ones(config.decoder_layers      ,			config.decoder_attention_heads      ,			device=_lowerCamelCase    )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }
class 					lowercase_     :


    def __init__(     self  ,					a  ,					a=13  ,					a=7  ,					a=True  ,					a=False  ,					a=99  ,					a=16  ,					a=2  ,					a=4  ,					a=4  ,					a="relu"  ,					a=0.1  ,					a=0.1  ,					a=0.0  ,					a=0.0  ,					a=20  ,					a=2  ,					a=1  ,					a=0  ,					):
          UpperCamelCase__										=				parent
          UpperCamelCase__										=				batch_size
          UpperCamelCase__										=				seq_length
          UpperCamelCase__										=				is_training
          UpperCamelCase__										=				use_labels
          UpperCamelCase__										=				vocab_size
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				encoder_layerdrop
          UpperCamelCase__										=				decoder_layerdrop
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				eos_token_id
          UpperCamelCase__										=				pad_token_id
          UpperCamelCase__										=				bos_token_id


    def    __a							(     self    ):
          UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size    )
          UpperCamelCase__										=				self.eos_token_id  # Eos Token
          UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size    )

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          UpperCamelCase__										=				input_ids.clamp(self.pad_token_id + 1    )
          UpperCamelCase__										=				decoder_input_ids.clamp(self.pad_token_id + 1    )

          UpperCamelCase__										=				self.get_config()
          UpperCamelCase__										=				prepare_mam_aaa_inputs_dict(lowercase_  ,					lowercase_  ,					lowercase_    )
          return config, inputs_dict


    def    __a							(     self    ):
          return MaMaaaConfig(
              vocab_size=self.vocab_size  ,					d_model=self.hidden_size  ,					encoder_layers=self.num_hidden_layers  ,					decoder_layers=self.num_hidden_layers  ,					encoder_attention_heads=self.num_attention_heads  ,					decoder_attention_heads=self.num_attention_heads  ,					encoder_ffn_dim=self.intermediate_size  ,					decoder_ffn_dim=self.intermediate_size  ,					dropout=self.hidden_dropout_prob  ,					attention_dropout=self.attention_probs_dropout_prob  ,					encoder_layerdrop=self.encoder_layerdrop  ,					decoder_layerdrop=self.decoder_layerdrop  ,					max_position_embeddings=self.max_position_embeddings  ,					eos_token_id=self.eos_token_id  ,					bos_token_id=self.bos_token_id  ,					pad_token_id=self.pad_token_id  ,					)


    def    __a							(     self    ):
          UpperCamelCase__										=				self.prepare_config_and_inputs()
          return config, inputs_dict


    def    __a							(     self  ,					a  ,					a    ):
          UpperCamelCase__										=				MaMaaaModel(config=lowercase_    ).get_decoder().to(lowercase_    ).eval()
          UpperCamelCase__										=				inputs_dict["input_ids"]
          UpperCamelCase__										=				inputs_dict["attention_mask"]
          UpperCamelCase__										=				inputs_dict["head_mask"]

          # first forward pass
          UpperCamelCase__										=				model(lowercase_  ,					attention_mask=lowercase_  ,					head_mask=lowercase_  ,					use_cache=lowercase_    )

          UpperCamelCase__										=				outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          UpperCamelCase__										=				ids_tensor((self.batch_size, 3)  ,					config.vocab_size    )
          UpperCamelCase__										=				ids_tensor((self.batch_size, 3)  ,					2    )

          # append to next input_ids and
          UpperCamelCase__										=				torch.cat([input_ids, next_tokens]  ,					dim=-1    )
          UpperCamelCase__										=				torch.cat([attention_mask, next_attn_mask]  ,					dim=-1    )

          UpperCamelCase__										=				model(lowercase_  ,					attention_mask=lowercase_    )["last_hidden_state"]
          UpperCamelCase__										=				model(lowercase_  ,					attention_mask=lowercase_  ,					past_key_values=lowercase_    )[
              "last_hidden_state"
          ]

          # select random slice
          UpperCamelCase__										=				ids_tensor((1,)  ,					output_from_past.shape[-1]    ).item()
          UpperCamelCase__										=				output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCamelCase__										=				output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(lowercase_  ,					lowercase_  ,					atol=1e-2    )    )


    def    __a							(     self  ,					a  ,					a    ):
          UpperCamelCase__										=				MaMaaaModel(config=lowercase_    ).to(lowercase_    ).eval()
          UpperCamelCase__										=				model(**lowercase_    )

          UpperCamelCase__										=				outputs.encoder_last_hidden_state
          UpperCamelCase__										=				outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCamelCase__										=				model.get_encoder()
                encoder.save_pretrained(lowercase_    )
                UpperCamelCase__										=				MaMaaaEncoder.from_pretrained(lowercase_    ).to(lowercase_    )

          UpperCamelCase__										=				encoder(inputs_dict["input_ids"]  ,					attention_mask=inputs_dict["attention_mask"]    )[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3    )

          with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCamelCase__										=				model.get_decoder()
                decoder.save_pretrained(lowercase_    )
                UpperCamelCase__										=				MaMaaaDecoder.from_pretrained(lowercase_    ).to(lowercase_    )

          UpperCamelCase__										=				decoder(
              input_ids=inputs_dict["decoder_input_ids"]  ,					attention_mask=inputs_dict["decoder_attention_mask"]  ,					encoder_hidden_states=lowercase_  ,					encoder_attention_mask=inputs_dict["attention_mask"]  ,					)[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3    )

@require_torch
class 					lowercase_     (    __A  ,						__A  ,						__A  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    __UpperCAmelCase   			=	(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    __UpperCAmelCase   			=	(
        {
            "conversational": MaMaaaForConditionalGeneration,
            "feature-extraction": MaMaaaModel,
            "summarization": MaMaaaForConditionalGeneration,
            "text2text-generation": MaMaaaForConditionalGeneration,
            "translation": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          if pipeline_test_casse_name == "TranslationPipelineTests":
                # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                return True

          return False


    def    __a							(     self    ):
          UpperCamelCase__										=				MaMaaaModelTester(self    )
          UpperCamelCase__										=				ConfigTester(self  ,					config_class=lowercase_    )


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


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                UpperCamelCase__										=				model_class(lowercase_    )

                with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(lowercase_    )
                      UpperCamelCase__										=				model_class.from_pretrained(lowercase_  ,					output_loading_info=lowercase_    )
                self.assertEqual(info["missing_keys"]  ,					[]    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase_    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*lowercase_    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                UpperCamelCase__										=				model_class(lowercase_    )
                model.to(lowercase_    )
                model.eval()

                UpperCamelCase__										=				copy.deepcopy(self._prepare_for_class(lowercase_  ,					lowercase_    )    )

                if not self.is_encoder_decoder:
                      UpperCamelCase__										=				inputs["input_ids"]
                      del inputs["input_ids"]
                else:
                      UpperCamelCase__										=				inputs["input_ids"]
                      UpperCamelCase__										=				inputs.get("decoder_input_ids"  ,					lowercase_    )
                      del inputs["input_ids"]
                      inputs.pop("decoder_input_ids"  ,					lowercase_    )

                UpperCamelCase__										=				model.get_input_embeddings()
                if not self.is_encoder_decoder:
                      UpperCamelCase__										=				wte(lowercase_    )
                else:
                      UpperCamelCase__										=				wte(lowercase_    )
                      UpperCamelCase__										=				wte(lowercase_    )

                with torch.no_grad():
                      model(**lowercase_    )[0]


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          UpperCamelCase__										=				input_dict["input_ids"]
          UpperCamelCase__										=				input_ids.ne(1    ).to(lowercase_    )
          UpperCamelCase__										=				MaMaaaForConditionalGeneration(lowercase_    ).eval().to(lowercase_    )
          if torch_device == "cuda":
                model.half()
          model.generate(lowercase_  ,					attention_mask=lowercase_    )
          model.generate(num_beams=4  ,					do_sample=lowercase_  ,					early_stopping=lowercase_  ,					num_return_sequences=3    )







def _UpperCamelCase       (     __A    )							->		List[str]:


      '''simple docstring'''



      return torch.tensor(_lowerCamelCase      ,			dtype=torch.long      ,			device=_lowerCamelCase    )


a__  :					Dict        =       1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 					lowercase_     (    unittest.TestCase   ):


    @cached_property
    def    __a							(     self    ):
          return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"    )


    def    __a							(     self    ):
          UpperCamelCase__										=				MaMaaaModel.from_pretrained("facebook/m2m100_418M"    ).to(lowercase_    )
          UpperCamelCase__										=				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]    )
          UpperCamelCase__										=				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]    )
          UpperCamelCase__										=				prepare_mam_aaa_inputs_dict(model.config  ,					lowercase_  ,					lowercase_    )
          with torch.no_grad():
                UpperCamelCase__										=				model(**lowercase_    )[0]
          UpperCamelCase__										=				torch.Size((1, 11, 10_24)    )
          self.assertEqual(output.shape  ,					lowercase_    )
          # change to expected output here
          UpperCamelCase__										=				torch.tensor(
              [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]  ,					device=lowercase_    )
          self.assertTrue(torch.allclose(output[:, :3, :3]  ,					lowercase_  ,					atol=lowercase_    )    )


    def    __a							(     self    ):
          UpperCamelCase__										=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"    ).to(lowercase_    )

          # change to intended input
          UpperCamelCase__										=				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]    )
          UpperCamelCase__										=				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]    )
          UpperCamelCase__										=				prepare_mam_aaa_inputs_dict(model.config  ,					lowercase_  ,					lowercase_    )
          with torch.no_grad():
                UpperCamelCase__										=				model(**lowercase_    )[0]
          UpperCamelCase__										=				torch.Size((1, 11, model.config.vocab_size)    )
          self.assertEqual(output.shape  ,					lowercase_    )
          # change to expected output here
          UpperCamelCase__										=				torch.tensor(
              [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]  ,					device=lowercase_    )
          self.assertTrue(torch.allclose(output[:, :3, :3]  ,					lowercase_  ,					atol=lowercase_    )    )


    def    __a							(     self    ):
          UpperCamelCase__										=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"    ).to(lowercase_    )
          UpperCamelCase__										=				MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  ,					src_lang="fr"  ,					tgt_lang="en"    )

          UpperCamelCase__										=				[
              "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
              "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
              "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
              " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
              " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          UpperCamelCase__										=				tokenizer(lowercase_  ,					padding=lowercase_  ,					return_tensors="pt"    )

          UpperCamelCase__										=				model.generate(
              input_ids=dct["input_ids"].to(lowercase_    )  ,					attention_mask=dct["attention_mask"].to(lowercase_    )  ,					num_beams=5  ,					forced_bos_token_id=tokenizer.get_lang_id("en"    )  ,					)

          UpperCamelCase__										=				[
              "The NSA case highlights the total absence of intelligence debate",
              "I think there are two levels of response from the French government.",
              "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
              " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
              " communications in France.",
          ]

          UpperCamelCase__										=				tokenizer.batch_decode(
              hypotheses_batch.tolist()  ,					clean_up_tokenization_spaces=lowercase_  ,					skip_special_tokens=lowercase_    )
          assert generated == expected_en






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 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   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 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       int):
    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 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Tuple    					=				[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token"))
    return token

def 			lowercase_				(			):
    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 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Optional[Any]    					=				"imagenet-1k-id2label.json"
    lowercase__ :   List[str]    					=				1000

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

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

    lowercase__ :   Optional[int]    					=				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__ :   Any    					=				[1, 2, 10]

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowercase__ :   Union[str, Any]    					=				[2, 2, 20]
        lowercase__ :   Optional[Any]    					=				[3, 12, 16]
        lowercase__ :   Optional[Any]    					=				[192, 768, 1024]

    lowercase__ :   Union[str, Any]    					=				CvtForImageClassification(_lowerCamelCase)
    lowercase__ :   Tuple    					=				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    lowercase__ :   int    					=				image_size
    lowercase__ :   Dict    					=				torch.load(_lowerCamelCase		,	map_location=torch.device("cpu"))

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

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

    lowercase__ :   List[str]    					=				list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_lowerCamelCase)
    for i in range(len(_lowerCamelCase)):
        lowercase__ :   Dict    					=				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__":
      UpperCamelCase  							=						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.'''
      )

      UpperCamelCase  							=						parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



