





'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        TimesformerForVideoClassification,
        TimesformerModel,
    )
    from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor







class __a			:





   def __init__(       self					:      List[Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Optional[Any]=13					,lowerCamelCase					:      Dict=10					,lowerCamelCase					:      Tuple=3					,lowerCamelCase					:      Dict=2					,lowerCamelCase					:      Optional[Any]=2					,lowerCamelCase					:      int=True					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Optional[Any]=32					,lowerCamelCase					:      List[Any]=5					,lowerCamelCase					:      List[str]=4					,lowerCamelCase					:      str=37					,lowerCamelCase					:      List[Any]="gelu"					,lowerCamelCase					:      Dict=0.1					,lowerCamelCase					:      List[Any]=0.1					,lowerCamelCase					:      Tuple=10					,lowerCamelCase					:      Dict=0.02					,lowerCamelCase					:      Any="divided_space_time"					,lowerCamelCase					:      Any=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							patch_size
      __SCREAMING_SNAKE_CASE   				=							num_frames
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_type
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							num_labels

      # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
      __SCREAMING_SNAKE_CASE   				=							(image_size // patch_size) ** 2
      __SCREAMING_SNAKE_CASE   				=							(num_frames) * self.num_patches_per_frame + 1





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_labels     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, pixel_values, labels





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TimesformerConfig(
          image_size=self.image_size					,patch_size=self.patch_size					,num_channels=self.num_channels					,num_frames=self.num_frames					,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					,initializer_range=self.initializer_range					,attention_type=self.attention_type					,)
      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      return config





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TimesformerModel(config=A_     )
      model.to(A_     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(A_     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      int					,lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TimesformerForVideoClassification(A_     )
      model.to(A_     )
      model.eval()

      __SCREAMING_SNAKE_CASE   				=							model(A_     )

      # verify the logits shape
      __SCREAMING_SNAKE_CASE   				=							torch.Size((self.batch_size, self.num_labels)     )
      self.parent.assertEqual(result.logits.shape					,A_     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class __a			( __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, unittest.TestCase      ):
   __UpperCamelCase :			List[Any]						   =						(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
   __UpperCamelCase :			Optional[int]						   =						(
       {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
       if is_torch_available()
       else {}
   )

   __UpperCamelCase :			List[str]						   =						False
   __UpperCamelCase :			Any						   =						False
   __UpperCamelCase :			Union[str, Any]						   =						False
   __UpperCamelCase :			Dict						   =						False





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TimesformerModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(
          self					,config_class=A_					,has_text_modality=A_					,hidden_size=37     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      int=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(A_     )

      if return_labels:
         if model_class in get_values(A_     ):
            __SCREAMING_SNAKE_CASE   				=							torch.zeros(
                self.model_tester.batch_size					,dtype=torch.long					,device=A_     )

      return inputs_dict





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   @unittest.skip(reason="""TimeSformer does not use inputs_embeds"""     )
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      pass





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(A_     )
         self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)     )
         __SCREAMING_SNAKE_CASE   				=							model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(A_					,nn.Linear     )     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(A_     )
         __SCREAMING_SNAKE_CASE   				=							inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __SCREAMING_SNAKE_CASE   				=							[*signature.parameters.keys()]

         __SCREAMING_SNAKE_CASE   				=							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]					,A_     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*A_     )





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

      '''simple docstring'''

      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							TimesformerModel.from_pretrained(A_     )
         self.assertIsNotNone(A_     )





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

      '''simple docstring'''

      if not self.has_attentions:
         pass

      else:
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
         __SCREAMING_SNAKE_CASE   				=							True

         for model_class in self.all_model_classes:
            __SCREAMING_SNAKE_CASE   				=							self.model_tester.seq_length
            __SCREAMING_SNAKE_CASE   				=							self.model_tester.num_frames

            __SCREAMING_SNAKE_CASE   				=							True
            __SCREAMING_SNAKE_CASE   				=							False
            __SCREAMING_SNAKE_CASE   				=							True
            __SCREAMING_SNAKE_CASE   				=							model_class(A_     )
            model.to(A_     )
            model.eval()
            with torch.no_grad():
               __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(A_					,A_     )     )
            __SCREAMING_SNAKE_CASE   				=							outputs.attentions
            self.assertEqual(len(A_     )					,self.model_tester.num_hidden_layers     )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __SCREAMING_SNAKE_CASE   				=							True
            __SCREAMING_SNAKE_CASE   				=							model_class(A_     )
            model.to(A_     )
            model.eval()
            with torch.no_grad():
               __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(A_					,A_     )     )
            __SCREAMING_SNAKE_CASE   				=							outputs.attentions
            self.assertEqual(len(A_     )					,self.model_tester.num_hidden_layers     )

            # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
            self.assertListEqual(
                list(attentions[0].shape[-3:]     )					,[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]					,)
            __SCREAMING_SNAKE_CASE   				=							len(A_     )

            # Check attention is always last and order is fine
            __SCREAMING_SNAKE_CASE   				=							True
            __SCREAMING_SNAKE_CASE   				=							True
            __SCREAMING_SNAKE_CASE   				=							model_class(A_     )
            model.to(A_     )
            model.eval()
            with torch.no_grad():
               __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(A_					,A_     )     )

            self.assertEqual(out_len + 1					,len(A_     )     )

            __SCREAMING_SNAKE_CASE   				=							outputs.attentions

            self.assertEqual(len(A_     )					,self.model_tester.num_hidden_layers     )

            # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]     )					,[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]					,)





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase					:      List[Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      int     ):
         __SCREAMING_SNAKE_CASE   				=							model_class(A_     )
         model.to(A_     )
         model.eval()

         with torch.no_grad():
            __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(A_					,A_     )     )

         __SCREAMING_SNAKE_CASE   				=							outputs.hidden_states
         __SCREAMING_SNAKE_CASE   				=							self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(A_     )					,A_     )

         __SCREAMING_SNAKE_CASE   				=							self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]     )					,[seq_length, self.model_tester.hidden_size]					,)

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							True
         check_hidden_states_output(A_					,A_					,A_     )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __SCREAMING_SNAKE_CASE   				=							True

         check_hidden_states_output(A_					,A_					,A_     )





def     __magic_name__							(				)    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""				,						filename="""eating_spaghetti.npy"""				,						repo_type="""dataset"""   )
   __SCREAMING_SNAKE_CASE   				=							np.load(lowerCAmelCase_   )
   return list(lowerCAmelCase_   )







@require_torch
@require_vision
class __a			( unittest.TestCase      ):





   @cached_property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return (
          VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,image_std=[0.5, 0.5, 0.5]     )
          if is_vision_available()
          else None
      )





   @slow
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400"""     ).to(
          A_     )

      __SCREAMING_SNAKE_CASE   				=							self.default_image_processor
      __SCREAMING_SNAKE_CASE   				=							prepare_video()
      __SCREAMING_SNAKE_CASE   				=							image_processor(video[:8]					,return_tensors="""pt"""     ).to(A_     )

      # forward pass
      with torch.no_grad():
         __SCREAMING_SNAKE_CASE   				=							model(**A_     )

      # verify the logits
      __SCREAMING_SNAKE_CASE   				=							torch.Size((1, 400)     )
      self.assertEqual(outputs.logits.shape					,A_     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-0.3_016, -0.7_713, -0.4_205]     ).to(A_     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,A_					,atol=1E-4     )     )












import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A					:				str				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[1_4_4, 1_9_2, 2_4_0]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
    elif "mobilevit_xs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[9_6, 1_2_0, 1_4_4]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
    elif "mobilevit_xxs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[6_4, 8_0, 9_6]
        SCREAMING_SNAKE_CASE__											=			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
        SCREAMING_SNAKE_CASE__											=			0.05
        SCREAMING_SNAKE_CASE__											=			2.0

    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			5_1_2
        SCREAMING_SNAKE_CASE__											=			1_6
        SCREAMING_SNAKE_CASE__											=			2_1
        SCREAMING_SNAKE_CASE__											=			'''pascal-voc-id2label.json'''
    else:
        SCREAMING_SNAKE_CASE__											=			1_0_0_0
        SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE__											=			idalabel
    SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    return config


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Union[str, Any]:
    for i in range(1 ,							6	):
        if f'''layer_{i}.''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''layer_{i}.''' ,							f'''encoder.layer.{i - 1}.'''	)

    if "conv_1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''conv_1.''' ,							'''conv_stem.'''	)
    if ".block." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.block.''' ,							'''.'''	)

    if "exp_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''exp_1x1''' ,							'''expand_1x1'''	)
    if "red_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''red_1x1''' ,							'''reduce_1x1'''	)
    if ".local_rep.conv_3x3." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_3x3.''' ,							'''.conv_kxk.'''	)
    if ".local_rep.conv_1x1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_1x1.''' ,							'''.conv_1x1.'''	)
    if ".norm." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.norm.''' ,							'''.normalization.'''	)
    if ".conv." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv.''' ,							'''.convolution.'''	)
    if ".conv_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv_proj.''' ,							'''.conv_projection.'''	)

    for i in range(0 ,							2	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.layer.{j}.'''	)

    for i in range(2 ,							6	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.'''	)
                if "expand_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''expand_1x1''' ,							'''downsampling_layer.expand_1x1'''	)
                if "conv_3x3" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''conv_3x3''' ,							'''downsampling_layer.conv_3x3'''	)
                if "reduce_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''reduce_1x1''' ,							'''downsampling_layer.reduce_1x1'''	)

    for i in range(2 ,							5	):
        if f'''.global_rep.{i}.weight''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.weight''' ,							'''.layernorm.weight'''	)
        if f'''.global_rep.{i}.bias''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.bias''' ,							'''.layernorm.bias'''	)

    if ".global_rep." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.global_rep.''' ,							'''.transformer.'''	)
    if ".pre_norm_mha.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.0.''' ,							'''.layernorm_before.'''	)
    if ".pre_norm_mha.1.out_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.1.out_proj.''' ,							'''.attention.output.dense.'''	)
    if ".pre_norm_ffn.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.0.''' ,							'''.layernorm_after.'''	)
    if ".pre_norm_ffn.1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.1.''' ,							'''.intermediate.dense.'''	)
    if ".pre_norm_ffn.4." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.4.''' ,							'''.output.dense.'''	)
    if ".transformer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.transformer.''' ,							'''.transformer.layer.'''	)

    if ".aspp_layer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_layer.''' ,							'''.'''	)
    if ".aspp_pool." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_pool.''' ,							'''.'''	)
    if "seg_head." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''seg_head.''' ,							'''segmentation_head.'''	)
    if "segmentation_head.classifier.classifier." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''segmentation_head.classifier.classifier.''' ,							'''segmentation_head.classifier.'''	)

    if "classifier.fc." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''classifier.fc.''' ,							'''classifier.'''	)
    elif (not base_model) and ("segmentation_head." not in name):
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.''' + name

    return name


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Optional[Any]:
    if base_model:
        SCREAMING_SNAKE_CASE__											=			''''''
    else:
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.'''

    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__											=			orig_state_dict.pop(lowerCAmelCase_	)

        if key[:8] == "encoder.":
            SCREAMING_SNAKE_CASE__											=			key[8:]

        if "qkv" in key:
            SCREAMING_SNAKE_CASE__											=			key.split('''.'''	)
            SCREAMING_SNAKE_CASE__											=			int(key_split[0][6:]	) - 1
            SCREAMING_SNAKE_CASE__											=			int(key_split[3]	)
            SCREAMING_SNAKE_CASE__											=			model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}'''	)
            SCREAMING_SNAKE_CASE__											=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
            SCREAMING_SNAKE_CASE__											=			(
                f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
            )
            if "weight" in key:
                SCREAMING_SNAKE_CASE__											=			val[:dim, :]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE__											=			val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE__											=			val[:dim]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2]
                SCREAMING_SNAKE_CASE__											=			val[-dim:]
        else:
            SCREAMING_SNAKE_CASE__											=			val

    return orig_state_dict


def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			get_mobilevit_config(lowerCAmelCase_	)

    # load original state_dict
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location='''cpu'''	)

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			MobileViTForSemanticSegmentation(lowerCAmelCase_	).eval()
    else:
        SCREAMING_SNAKE_CASE__											=			MobileViTForImageClassification(lowerCAmelCase_	).eval()

    SCREAMING_SNAKE_CASE__											=			convert_state_dict(lowerCAmelCase_ ,							lowerCAmelCase_	)
    model.load_state_dict(lowerCAmelCase_	)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    SCREAMING_SNAKE_CASE__											=			MobileViTImageProcessor(crop_size=config.image_size ,							size=config.image_size + 3_2	)
    SCREAMING_SNAKE_CASE__											=			image_processor(images=prepare_img() ,							return_tensors='''pt'''	)
    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			outputs.logits

    if mobilevit_name.startswith('''deeplabv3_'''	):
        assert logits.shape == (1, 2_1, 3_2, 3_2)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                    [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                    [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                    [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                    [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                    [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                    [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                ]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3, :3, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)
    else:
        assert logits.shape == (1, 1_0_0_0)

        if mobilevit_name == "mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-0.98_66, 0.23_92, -1.12_41]	)
        elif mobilevit_name == "mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-2.47_61, -0.93_99, -1.95_87]	)
        elif mobilevit_name == "mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-1.93_64, -1.23_27, -0.46_53]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)

    Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
    print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(lowerCAmelCase_	)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
    image_processor.save_pretrained(lowerCAmelCase_	)

    if push_to_hub:
        SCREAMING_SNAKE_CASE__											=			{
            '''mobilevit_s''': '''mobilevit-small''',
            '''mobilevit_xs''': '''mobilevit-x-small''',
            '''mobilevit_xxs''': '''mobilevit-xx-small''',
            '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
            '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
            '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
        }

        print('''Pushing to the hub...'''	)
        SCREAMING_SNAKE_CASE__											=			model_mapping[mobilevit_name]
        image_processor.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)
        model.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)


if __name__ == "__main__":
  _A					:				str				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--mobilevit_name""",
      default="""mobilevit_s""",
      type=str,
      help=(
          """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
          """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
      ),
  )
  parser.add_argument(
      """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  _A					:				str				=			parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
