




from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
  )

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor







class 						lowercase							:

     def __init__(  self		,     _a		,     _a=2		,     _a=3		,     _a=4		,     _a=2		,     _a=7		,     _a=True		,     _a=True		,     _a=True		,     _a=True		,     _a=99		,     _a=36		,     _a=2		,     _a=4		,     _a=37		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=16		,     _a=2		,     _a=0.02		,     _a=6		,     _a=6		,     _a=3		,     _a=4		,     _a=None		,     _a=1000		,     )						->							int:
           _A							:     Tuple			=						parent
           _A							:     int			=						batch_size
           _A							:     Union[str, Any]			=						num_channels
           _A							:     int			=						image_size
           _A							:     Tuple			=						patch_size
           _A							:     Union[str, Any]			=						is_training
           _A							:     List[Any]			=						use_input_mask
           _A							:     Tuple			=						use_token_type_ids
           _A							:     Optional[int]			=						use_labels
           _A							:     Any			=						vocab_size
           _A							:     Union[str, Any]			=						hidden_size
           _A							:     Dict			=						num_hidden_layers
           _A							:     Dict			=						num_attention_heads
           _A							:     List[Any]			=						intermediate_size
           _A							:     Optional[Any]			=						hidden_act
           _A							:     Optional[Any]			=						hidden_dropout_prob
           _A							:     Dict			=						attention_probs_dropout_prob
           _A							:     Any			=						max_position_embeddings
           _A							:     int			=						type_vocab_size
           _A							:     Tuple			=						type_sequence_label_size
           _A							:     Optional[int]			=						initializer_range
           _A							:     List[Any]			=						coordinate_size
           _A							:     Dict			=						shape_size
           _A							:     Tuple			=						num_labels
           _A							:     str			=						num_choices
           _A							:     str			=						scope
           _A							:     Optional[Any]			=						range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           _A							:     Union[str, Any]			=						text_seq_length
           _A							:     Optional[Any]			=						(image_size // patch_size) ** 2 + 1
           _A							:     List[str]			=						self.text_seq_length + self.image_seq_length

     def 					a__	(  self				)						->							str:
           _A							:     Union[str, Any]			=						ids_tensor([self.batch_size, self.text_seq_length]		,     self.vocab_size				)

           _A							:     Any			=						ids_tensor([self.batch_size, self.text_seq_length, 4]		,     self.range_bbox				)
           _A							:     Dict			=						bbox.numpy()
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]				):
                 for j in range(bbox.shape[1]				):
                       if bbox[i, j, 3] < bbox[i, j, 1]:
                             _A							:     Union[str, Any]			=						bbox[i, j, 3]
                             _A							:     Optional[int]			=						bbox[i, j, 1]
                             _A							:     Dict			=						tmp_coordinate
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                             _A							:     Union[str, Any]			=						bbox[i, j, 2]
                             _A							:     Dict			=						bbox[i, j, 0]
                             _A							:     Union[str, Any]			=						tmp_coordinate
           _A							:     Any			=						tf.constant(_a				)

           _A							:     Union[str, Any]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           _A							:     List[Any]			=						None
           if self.use_input_mask:
                 _A							:     List[str]			=						random_attention_mask([self.batch_size, self.text_seq_length]				)

           _A							:     List[Any]			=						None
           if self.use_token_type_ids:
                 _A							:     str			=						ids_tensor([self.batch_size, self.text_seq_length]		,     self.type_vocab_size				)

           _A							:     Union[str, Any]			=						None
           _A							:     Union[str, Any]			=						None
           if self.use_labels:
                 _A							:     int			=						ids_tensor([self.batch_size]		,     self.type_sequence_label_size				)
                 _A							:     List[Any]			=						ids_tensor([self.batch_size, self.text_seq_length]		,     self.num_labels				)

           _A							:     Optional[int]			=						LayoutLMvaConfig(
               vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     initializer_range=self.initializer_range		,     coordinate_size=self.coordinate_size		,     shape_size=self.shape_size		,     input_size=self.image_size		,     patch_size=self.patch_size		,     )

           return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           _A							:     str			=						TFLayoutLMvaModel(config=_a				)

           # text + image
           _A							:     List[str]			=						model(_a		,     pixel_values=_a		,     training=_a				)
           _A							:     Union[str, Any]			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     training=_a		,     )
           _A							:     int			=						model(_a		,     bbox=_a		,     pixel_values=_a		,     training=_a				)

           self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)				)

           # text only
           _A							:     Dict			=						model(_a		,     training=_a				)
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.text_seq_length, self.hidden_size)				)

           # image only
           _A							:     Union[str, Any]			=						model({"""pixel_values""": pixel_values}		,     training=_a				)
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.image_seq_length, self.hidden_size)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						self.num_labels
           _A							:     List[str]			=						TFLayoutLMvaForSequenceClassification(config=_a				)
           _A							:     int			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     training=_a		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Any:
           _A							:     str			=						self.num_labels
           _A							:     Any			=						TFLayoutLMvaForTokenClassification(config=_a				)
           _A							:     Union[str, Any]			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     training=_a		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.text_seq_length, self.num_labels)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           _A							:     int			=						2
           _A							:     Dict			=						TFLayoutLMvaForQuestionAnswering(config=_a				)
           _A							:     Tuple			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     start_positions=_a		,     end_positions=_a		,     training=_a		,     )
           self.parent.assertEqual(result.start_logits.shape		,     (self.batch_size, self.seq_length)				)
           self.parent.assertEqual(result.end_logits.shape		,     (self.batch_size, self.seq_length)				)



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     int			=						self.prepare_config_and_inputs()
           ((_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A))							:     Any			=						config_and_inputs
           _A							:     List[Any]			=						{
               """input_ids""": input_ids,
               """bbox""": bbox,
               """pixel_values""": pixel_values,
               """token_type_ids""": token_type_ids,
               """attention_mask""": input_mask,
           }
           return config, inputs_dict







@require_tf
class 						lowercase							(   UpperCamelCase__,UpperCamelCase__,unittest.TestCase		):
     _a							 =						(
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     _a							 =						(
         {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     _a							 =						False
     _a							 =						False
     _a							 =						False

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           return True

     def 					a__	(  self		,     _a		,     _a		,     _a=False				)						->							dict:
           _A							:     int			=						copy.deepcopy(_a				)

           if model_class in get_values(_a				):
                 _A							:     List[Any]			=						{
                     k: tf.tile(tf.expand_dims(_a		,     1				)		,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
                     if isinstance(_a		,     tf.Tensor				) and v.ndim > 0
                     else v
                     for k, v in inputs_dict.items()
                 }

           if return_labels:
                 if model_class in get_values(_a				):
                       _A							:     List[str]			=						tf.ones(self.model_tester.batch_size		,     dtype=tf.intaa				)
                 elif model_class in get_values(_a				):
                       _A							:     Dict			=						tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa				)
                       _A							:     List[Any]			=						tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa				)
                 elif model_class in get_values(_a				):
                       _A							:     str			=						tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa				)
                 elif model_class in get_values(_a				):
                       _A							:     Tuple			=						tf.zeros(
                           (self.model_tester.batch_size, self.model_tester.text_seq_length)		,     dtype=tf.intaa				)

           return inputs_dict

     def 					a__	(  self				)						->							List[str]:
           _A							:     List[str]			=						TFLayoutLMvaModelTester(self				)
           _A							:     Optional[Any]			=						ConfigTester(self		,     config_class=_a		,     hidden_size=37				)

     def 					a__	(  self				)						->							Optional[Any]:
           self.config_tester.run_common_tests()

     def 					a__	(  self				)						->							List[str]:
           _A			,   _A							:     Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 _A							:     Dict			=						model_class(_a				)
                 if getattr(_a		,     """hf_compute_loss"""		,     _a				):
                       # The number of elements in the loss should be the same as the number of elements in the label
                       _A							:     int			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)
                       _A							:     int			=						prepared_for_class[
                           sorted(prepared_for_class.keys() - inputs_dict.keys()		,     reverse=_a				)[0]
                       ]
                       _A							:     int			=						added_label.shape.as_list()[:1]

                       # Test that model correctly compute the loss with kwargs
                       _A							:     List[str]			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)
                       _A							:     Union[str, Any]			=						prepared_for_class.pop("""input_ids"""				)

                       _A							:     Tuple			=						model(_a		,     **_a				)[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

                       # Test that model correctly compute the loss when we mask some positions
                       _A							:     Tuple			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)
                       _A							:     Any			=						prepared_for_class.pop("""input_ids"""				)
                       if "labels" in prepared_for_class:
                             _A							:     int			=						prepared_for_class["""labels"""].numpy()
                             if len(labels.shape				) > 1 and labels.shape[1] != 1:
                                   _A							:     Tuple			=						-100
                                   _A							:     Dict			=						tf.convert_to_tensor(_a				)
                                   _A							:     Any			=						model(_a		,     **_a				)[0]
                                   self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)
                                   self.assertTrue(not np.any(np.isnan(loss.numpy()				)				)				)

                # Test that model correctly compute the loss with a dict
                       _A							:     List[str]			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)
                       _A							:     int			=						model(_a				)[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

                       # Test that model correctly compute the loss with a tuple
                       _A							:     List[str]			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)

                       # Get keys that were added with the _prepare_for_class function
                       _A							:     str			=						prepared_for_class.keys() - inputs_dict.keys()
                       _A							:     Dict			=						inspect.signature(model.call				).parameters
                       _A							:     Dict			=						list(signature.keys()				)

                       # Create a dictionary holding the location of the tensors in the tuple
                       _A							:     Dict			=						{0: """input_ids"""}
                       for label_key in label_keys:
                             _A							:     str			=						signature_names.index(_a				)
                             _A							:     int			=						label_key
                       _A							:     Dict			=						sorted(tuple_index_mapping.items()				)
                       # Initialize a list with their default values, update the values and convert to a tuple
                       _A							:     List[str]			=						[]

                       for name in signature_names:
                             if name != "kwargs":
                                   list_input.append(signature[name].default				)

                       for index, value in sorted_tuple_index_mapping:
                             _A							:     Tuple			=						prepared_for_class[value]

                       _A							:     int			=						tuple(_a				)

                       # Send to model
                       _A							:     List[str]			=						model(tuple_input[:-1]				)[0]

                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

     def 					a__	(  self				)						->							Optional[int]:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     Optional[int]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(_a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Any:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 _A							:     Optional[Any]			=						type
                 self.model_tester.create_and_check_model(_a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Tuple:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Tuple:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							List[str]:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     Dict			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)



     @slow
     def 					a__	(  self				)						->							str:
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A							:     Tuple			=						TFLayoutLMvaModel.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)







def 				lowerCAmelCase_	(     ):
      _A							:     List[Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image







@require_tf
class 						lowercase							(   unittest.TestCase		):

     @cached_property
     def 					a__	(  self				)						->							Tuple:
           return LayoutLMvaImageProcessor(apply_ocr=_a				) if is_vision_available() else None



     @slow
     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Optional[int]			=						TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""				)

           _A							:     Tuple			=						self.default_image_processor
           _A							:     int			=						prepare_img()
           _A							:     List[Any]			=						image_processor(images=_a		,     return_tensors="""tf"""				).pixel_values

           _A							:     str			=						tf.constant([[1, 2]]				)
           _A							:     int			=						tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]				)		,     axis=0				)

           # forward pass
           _A							:     Tuple			=						model(input_ids=_a		,     bbox=_a		,     pixel_values=_a		,     training=_a				)

           # verify the logits
           _A							:     int			=						(1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape		,     _a				)

           _A							:     List[Any]			=						tf.constant(
               [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]				)

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,     _a		,     atol=1e-4				)				)







import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)







def 				lowerCAmelCase_	(     snake_case_     ):
      print("""Loading config file..."""     )

      def flatten_yaml_as_dict(snake_case_,snake_case_="",snake_case_="."     ):
            _A							:     Union[str, Any]			=						[]
            for k, v in d.items():
                  _A							:     Optional[int]			=						parent_key + sep + k if parent_key else k
                  if isinstance(snake_case_,collections.abc.MutableMapping     ):
                        items.extend(flatten_yaml_as_dict(snake_case_,snake_case_,sep=snake_case_     ).items()     )
                  else:
                        items.append((new_key, v)     )
            return dict(snake_case_     )

      _A							:     List[Any]			=						argparse.Namespace()
      with open(snake_case_,"""r"""     ) as yaml_file:
            try:
                  _A							:     List[Any]			=						yaml.load(snake_case_,Loader=yaml.FullLoader     )

                  _A							:     Optional[int]			=						flatten_yaml_as_dict(snake_case_     )
                  for k, v in flat_cfg.items():
                        setattr(snake_case_,snake_case_,snake_case_     )
            except yaml.YAMLError as exc:
                  logger.error("""Error while loading config file: {}. Error message: {}""".format(snake_case_,str(snake_case_     )     )     )
      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						MobileViTVaConfig()

      _A							:     Tuple			=						False

      # dataset
      if task_name.startswith("""imagenet1k_"""     ):
            _A							:     Dict			=						1000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     int			=						384
            else:
                  _A							:     int			=						256
            _A							:     List[str]			=						"""imagenet-1k-id2label.json"""
      elif task_name.startswith("""imagenet21k_to_1k_"""     ):
            _A							:     Union[str, Any]			=						21000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     str			=						384
            else:
                  _A							:     List[Any]			=						256
            _A							:     List[str]			=						"""imagenet-22k-id2label.json"""
      elif task_name.startswith("""ade20k_"""     ):
            _A							:     int			=						151
            _A							:     int			=						512
            _A							:     Optional[int]			=						"""ade20k-id2label.json"""
            _A							:     Any			=						True
      elif task_name.startswith("""voc_"""     ):
            _A							:     List[Any]			=						21
            _A							:     Dict			=						512
            _A							:     Dict			=						"""pascal-voc-id2label.json"""
            _A							:     int			=						True

      # orig_config
      _A							:     Any			=						load_orig_config_file(snake_case_     )
      assert getattr(snake_case_,"""model.classification.name""",-1     ) == "mobilevit_v2", "Invalid model"
      _A							:     List[Any]			=						getattr(snake_case_,"""model.classification.mitv2.width_multiplier""",1.0     )
      assert (
          getattr(snake_case_,"""model.classification.mitv2.attn_norm_layer""",-1     ) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      _A							:     str			=						getattr(snake_case_,"""model.classification.activation.name""","""swish"""     )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            _A							:     Optional[int]			=						getattr(snake_case_,"""model.segmentation.output_stride""",16     )
            if "_deeplabv3" in task_name:
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_rates""",[12, 24, 36]     )
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_out_channels""",512     )
                  _A							:     str			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_dropout""",0.1     )

    # id2label
      _A							:     List[Any]			=						"""huggingface/label-files"""
      _A							:     List[Any]			=						json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""     ),"""r"""     )     )
      _A							:     str			=						{int(snake_case_     ): v for k, v in idalabel.items()}
      _A							:     str			=						idalabel
      _A							:     Dict			=						{v: k for k, v in idalabel.items()}

      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						dct.pop(snake_case_     )
      _A							:     Union[str, Any]			=						val







def 				lowerCAmelCase_	(     snake_case_,snake_case_=False     ):
      if base_model:
            _A							:     Optional[int]			=						""""""
      else:
            _A							:     Dict			=						"""mobilevitv2."""

      _A							:     int			=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _A							:     Any			=						k[8:]
            else:
                  _A							:     List[str]			=						k

            if ".block." in k:
                  _A							:     Any			=						k_new.replace(""".block.""","""."""     )
            if ".conv." in k:
                  _A							:     List[Any]			=						k_new.replace(""".conv.""",""".convolution."""     )
            if ".norm." in k:
                  _A							:     Any			=						k_new.replace(""".norm.""",""".normalization."""     )

            if "conv_1." in k:
                  _A							:     int			=						k_new.replace("""conv_1.""",f'''{model_prefix}conv_stem.'''     )
            for i in [1, 2]:
                  if f'''layer_{i}.''' in k:
                        _A							:     Optional[Any]			=						k_new.replace(f'''layer_{i}.''',f'''{model_prefix}encoder.layer.{i-1}.layer.'''     )
            if ".exp_1x1." in k:
                  _A							:     Tuple			=						k_new.replace(""".exp_1x1.""",""".expand_1x1."""     )
            if ".red_1x1." in k:
                  _A							:     Optional[int]			=						k_new.replace(""".red_1x1.""",""".reduce_1x1."""     )

            for i in [3, 4, 5]:
                  if f'''layer_{i}.0.''' in k:
                        _A							:     Optional[int]			=						k_new.replace(f'''layer_{i}.0.''',f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''     )
                  if f'''layer_{i}.1.local_rep.0.''' in k:
                        _A							:     Union[str, Any]			=						k_new.replace(f'''layer_{i}.1.local_rep.0.''',f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''     )
                  if f'''layer_{i}.1.local_rep.1.''' in k:
                        _A							:     str			=						k_new.replace(f'''layer_{i}.1.local_rep.1.''',f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''     )

            for i in [3, 4, 5]:
                  if i == 3:
                        _A							:     Optional[int]			=						[0, 1]
                  elif i == 4:
                        _A							:     Union[str, Any]			=						[0, 1, 2, 3]
                  elif i == 5:
                        _A							:     Optional[Any]			=						[0, 1, 2]

                  for j in j_in:
                        if f'''layer_{i}.1.global_rep.{j}.''' in k:
                              _A							:     Union[str, Any]			=						k_new.replace(
                                  f'''layer_{i}.1.global_rep.{j}.''',f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''     )
                  if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
                        _A							:     List[str]			=						k_new.replace(
                            f'''layer_{i}.1.global_rep.{j+1}.''',f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''     )

                  if f'''layer_{i}.1.conv_proj.''' in k:
                        _A							:     Optional[Any]			=						k_new.replace(f'''layer_{i}.1.conv_proj.''',f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''     )

            if "pre_norm_attn.0." in k:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_attn.0.""","""layernorm_before."""     )
            if "pre_norm_attn.1." in k:
                  _A							:     str			=						k_new.replace("""pre_norm_attn.1.""","""attention."""     )
            if "pre_norm_ffn.0." in k:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_ffn.0.""","""layernorm_after."""     )
            if "pre_norm_ffn.1." in k:
                  _A							:     Dict			=						k_new.replace("""pre_norm_ffn.1.""","""ffn.conv1."""     )
            if "pre_norm_ffn.3." in k:
                  _A							:     List[str]			=						k_new.replace("""pre_norm_ffn.3.""","""ffn.conv2."""     )

            if "classifier.1." in k:
                  _A							:     List[str]			=						k_new.replace("""classifier.1.""","""classifier."""     )

            if "seg_head." in k:
                  _A							:     List[Any]			=						k_new.replace("""seg_head.""","""segmentation_head."""     )
            if ".aspp_layer." in k:
                  _A							:     List[Any]			=						k_new.replace(""".aspp_layer.""","""."""     )
            if ".aspp_pool." in k:
                  _A							:     Optional[Any]			=						k_new.replace(""".aspp_pool.""","""."""     )

            rename_keys.append((k, k_new)     )
      return rename_keys







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Tuple			=						[]
      for k in state_dict.keys():
            if k.startswith("""seg_head.aux_head."""     ):
                  keys_to_ignore.append(snake_case_     )
      for k in keys_to_ignore:
            state_dict.pop(snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Dict			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _A							:     List[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						get_mobilevitva_config(snake_case_,snake_case_     )

      # load original state_dict
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )

      # load huggingface model
      if task_name.startswith("""ade20k_"""     ) or task_name.startswith("""voc_"""     ):
            _A							:     Optional[Any]			=						MobileViTVaForSemanticSegmentation(snake_case_     ).eval()
            _A							:     str			=						False
      else:
            _A							:     int			=						MobileViTVaForImageClassification(snake_case_     ).eval()
            _A							:     List[Any]			=						False

      # remove and rename some keys of load the original model
      _A							:     List[Any]			=						checkpoint
      remove_unused_keys(snake_case_     )
      _A							:     Optional[Any]			=						create_rename_keys(snake_case_,base_model=snake_case_     )
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(snake_case_,snake_case_,snake_case_     )

      # load modified state_dict
      model.load_state_dict(snake_case_     )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      _A							:     str			=						MobileViTImageProcessor(crop_size=config.image_size,size=config.image_size + 32     )
      _A							:     List[Any]			=						image_processor(images=prepare_img(),return_tensors="""pt"""     )
      _A							:     Optional[Any]			=						model(**snake_case_     )

      # verify classification model
      if task_name.startswith("""imagenet"""     ):
            _A							:     List[Any]			=						outputs.logits
            _A							:     Optional[int]			=						logits.argmax(-1     ).item()
            print("""Predicted class:""",model.config.idalabel[predicted_class_idx]     )
            if task_name.startswith("""imagenet1k_256"""     ) and config.width_multiplier == 1.0:
                  # expected_logits for base variant
                  _A							:     int			=						torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]     )
                  assert torch.allclose(logits[0, :3],snake_case_,atol=1e-4     )

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


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  _snake_case										=       parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )

