"""simple docstring"""







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
         LayoutLMForMaskedLM,
         LayoutLMForQuestionAnswering,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMModel,
     )






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=99 ,				_SCREAMING_SNAKE_CASE=32 ,				_SCREAMING_SNAKE_CASE=5 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=37 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=512 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=1000 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					seq_length
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_input_mask
      __lowerCamelCase    		=					use_token_type_ids
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					intermediate_size
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					max_position_embeddings
      __lowerCamelCase    		=					type_vocab_size
      __lowerCamelCase    		=					type_sequence_label_size
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					num_choices
      __lowerCamelCase    		=					scope
      __lowerCamelCase    		=					range_bbox







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length, 4] ,				self.range_bbox	)
      # 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]:
               __lowerCamelCase    		=					bbox[i, j, 3]
               __lowerCamelCase    		=					bbox[i, j, 1]
               __lowerCamelCase    		=					t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               __lowerCamelCase    		=					bbox[i, j, 2]
               __lowerCamelCase    		=					bbox[i, j, 0]
               __lowerCamelCase    		=					t

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					self.get_config()

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







   def    _A				(	self	):


      '''simple docstring'''

      return LayoutLMConfig(
          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 ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)
      self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				start_positions=_SCREAMING_SNAKE_CASE ,				end_positions=_SCREAMING_SNAKE_CASE ,				)
      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	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[str]    =    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   __UpperCAmelCase	:							List[Any]    =    (
       {
           "document-question-answering": LayoutLMForQuestionAnswering,
           "feature-extraction": LayoutLMModel,
           "fill-mask": LayoutLMForMaskedLM,
           "text-classification": LayoutLMForSequenceClassification,
           "token-classification": LayoutLMForTokenClassification,
           "zero-shot": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase	:							int    =    True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				hidden_size=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __lowerCamelCase    		=					type
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE	)







   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass



def       A__    (	)  ->							Optional[int]:
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   __lowerCamelCase    		=					torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]      , device=lowercase_						)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   __lowerCamelCase    		=					torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]      , device=lowercase_						)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the sequence output on [0, :3, :3]
      __lowerCamelCase    		=					torch.tensor(
          [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] ,				device=_SCREAMING_SNAKE_CASE ,				)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-3	)	)

      # test the pooled output on [1, :3]
      __lowerCamelCase    		=					torch.tensor([-0.6580, -0.0214, 0.8552] ,				device=_SCREAMING_SNAKE_CASE	)

      self.assertTrue(torch.allclose(outputs.pooler_output[1, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-3	)	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=2	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=torch.tensor([1, 1] ,				device=_SCREAMING_SNAKE_CASE	) ,				)

      # test whether we get a loss as a scalar
      __lowerCamelCase    		=					outputs.loss
      __lowerCamelCase    		=					torch.Size([]	)
      self.assertEqual(loss.shape ,				_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 2)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=13	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)

      # test the loss calculation to be around 2.65
      # expected_loss = torch.tensor(2.65, device=torch_device)

      # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
      # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 25, 13)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					torch.Size((2, 25)	)
      self.assertEqual(outputs.start_logits.shape ,				_SCREAMING_SNAKE_CASE	)
      self.assertEqual(outputs.end_logits.shape ,				_SCREAMING_SNAKE_CASE	)











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 ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__			:   Optional[Any]      			=    logging.get_logger(__name__)


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  Optional[Any]     					=	ConvNextConfig()

				if "tiny" in checkpoint_url:
								_UpperCAmelCase						:  Any     					=	[3, 3, 9, 3]
								_UpperCAmelCase						:  int     					=	[96, 192, 384, 768]
				if "small" in checkpoint_url:
								_UpperCAmelCase						:  str     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  List[Any]     					=	[96, 192, 384, 768]
				if "base" in checkpoint_url:
								_UpperCAmelCase						:  Dict     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  Tuple     					=	[128, 256, 512, 1_024]
				if "large" in checkpoint_url:
								_UpperCAmelCase						:  Optional[int]     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  Optional[int]     					=	[192, 384, 768, 1_536]
				if "xlarge" in checkpoint_url:
								_UpperCAmelCase						:  Tuple     					=	[3, 3, 27, 3]
								_UpperCAmelCase						:  int     					=	[256, 512, 1_024, 2_048]

				if "1k" in checkpoint_url:
								_UpperCAmelCase						:  int     					=	1_000
								_UpperCAmelCase						:  Union[str, Any]     					=	'''imagenet-1k-id2label.json'''
								_UpperCAmelCase						:  Any     					=	(1, 1_000)
				else:
								_UpperCAmelCase						:  Union[str, Any]     					=	21_841
								_UpperCAmelCase						:  int     					=	'''imagenet-22k-id2label.json'''
								_UpperCAmelCase						:  List[str]     					=	(1, 21_841)

				_UpperCAmelCase						:  List[str]     					=	'''huggingface/label-files'''
				_UpperCAmelCase						:  Any     					=	num_labels
				_UpperCAmelCase						:  str     					=	json.load(open(hf_hub_download(_UpperCamelCase      ,					_UpperCamelCase      ,					repo_type='''dataset'''			)      ,					'''r'''			)			)
				_UpperCAmelCase						:  List[Any]     					=	{int(_UpperCamelCase			): v for k, v in idalabel.items()}
				if "1k" not in checkpoint_url:
								# this dataset contains 21843 labels but the model only has 21841
								# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
								del idalabel[9_205]
								del idalabel[15_027]
				_UpperCAmelCase						:  List[str]     					=	idalabel
				_UpperCAmelCase						:  Any     					=	{v: k for k, v in idalabel.items()}
				_UpperCAmelCase						:  Union[str, Any]     					=	hidden_sizes
				_UpperCAmelCase						:  int     					=	depths

				return config, expected_shape


def 	_A	(   _UpperCamelCase			):
				if "downsample_layers.0.0" in name:
								_UpperCAmelCase						:  Any     					=	name.replace('''downsample_layers.0.0'''      ,					'''embeddings.patch_embeddings'''			)
				if "downsample_layers.0.1" in name:
								_UpperCAmelCase						:  int     					=	name.replace('''downsample_layers.0.1'''      ,					'''embeddings.norm'''			)  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								_UpperCAmelCase						:  Optional[Any]     					=	name.replace('''downsample_layers.1.0'''      ,					'''stages.1.downsampling_layer.0'''			)
				if "downsample_layers.1.1" in name:
								_UpperCAmelCase						:  Any     					=	name.replace('''downsample_layers.1.1'''      ,					'''stages.1.downsampling_layer.1'''			)
				if "downsample_layers.2.0" in name:
								_UpperCAmelCase						:  int     					=	name.replace('''downsample_layers.2.0'''      ,					'''stages.2.downsampling_layer.0'''			)
				if "downsample_layers.2.1" in name:
								_UpperCAmelCase						:  List[Any]     					=	name.replace('''downsample_layers.2.1'''      ,					'''stages.2.downsampling_layer.1'''			)
				if "downsample_layers.3.0" in name:
								_UpperCAmelCase						:  Union[str, Any]     					=	name.replace('''downsample_layers.3.0'''      ,					'''stages.3.downsampling_layer.0'''			)
				if "downsample_layers.3.1" in name:
								_UpperCAmelCase						:  Union[str, Any]     					=	name.replace('''downsample_layers.3.1'''      ,					'''stages.3.downsampling_layer.1'''			)
				if "stages" in name and "downsampling_layer" not in name:
								# stages.0.0. for instance should be renamed to stages.0.layers.0.
								_UpperCAmelCase						:  Any     					=	name[: len('''stages.0'''			)] + '''.layers''' + name[len('''stages.0'''			) :]
				if "stages" in name:
								_UpperCAmelCase						:  Dict     					=	name.replace('''stages'''      ,					'''encoder.stages'''			)
				if "norm" in name:
								_UpperCAmelCase						:  int     					=	name.replace('''norm'''      ,					'''layernorm'''			)
				if "gamma" in name:
								_UpperCAmelCase						:  str     					=	name.replace('''gamma'''      ,					'''layer_scale_parameter'''			)
				if "head" in name:
								_UpperCAmelCase						:  Optional[Any]     					=	name.replace('''head'''      ,					'''classifier'''			)

				return name


def 	_A	(   ):
				_UpperCAmelCase						:  Tuple     					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase						:  Union[str, Any]     					=	Image.open(requests.get(_UpperCamelCase      ,					stream=_UpperCamelCase			).raw			)
				return im


@torch.no_grad()
def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase					, _UpperCAmelCase						:  Union[str, Any]     					=	get_convnext_config(_UpperCamelCase			)
				# load original state_dict from URL
				_UpperCAmelCase						:  List[Any]     					=	torch.hub.load_state_dict_from_url(_UpperCamelCase			)['''model''']
				# rename keys
				for key in state_dict.copy().keys():
								_UpperCAmelCase						:  str     					=	state_dict.pop(_UpperCamelCase			)
								_UpperCAmelCase						:  int     					=	val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								_UpperCAmelCase						:  Any     					=	state_dict.pop(_UpperCamelCase			)
								if not key.startswith('''classifier'''			):
												_UpperCAmelCase						:  Dict     					=	'''convnext.''' + key
								_UpperCAmelCase						:  List[Any]     					=	val

				# load HuggingFace model
				_UpperCAmelCase						:  Optional[int]     					=	ConvNextForImageClassification(_UpperCamelCase			)
				model.load_state_dict(_UpperCamelCase			)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				_UpperCAmelCase						:  Optional[Any]     					=	224 if '''224''' in checkpoint_url else 384
				_UpperCAmelCase						:  int     					=	ConvNextImageProcessor(size=_UpperCamelCase			)
				_UpperCAmelCase						:  Union[str, Any]     					=	image_processor(images=prepare_img()      ,					return_tensors='''pt'''			).pixel_values

				_UpperCAmelCase						:  Any     					=	model(_UpperCamelCase			).logits

				# note: the logits below were obtained without center cropping
				if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
								_UpperCAmelCase						:  Optional[int]     					=	torch.tensor([-0.1_210, -0.6_605, 0.1_918]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
								_UpperCAmelCase						:  Optional[Any]     					=	torch.tensor([-0.4_473, -0.1_847, -0.6_365]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
								_UpperCAmelCase						:  List[str]     					=	torch.tensor([0.4_525, 0.7_539, 0.0_308]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
								_UpperCAmelCase						:  Any     					=	torch.tensor([0.3_561, 0.6_350, -0.0_384]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
								_UpperCAmelCase						:  str     					=	torch.tensor([0.4_174, -0.0_989, 0.1_489]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
								_UpperCAmelCase						:  Tuple     					=	torch.tensor([0.2_513, -0.1_349, -0.1_613]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
								_UpperCAmelCase						:  Dict     					=	torch.tensor([1.2_980, 0.3_631, -0.1_198]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
								_UpperCAmelCase						:  Optional[int]     					=	torch.tensor([1.2_963, 0.1_227, 0.1_723]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
								_UpperCAmelCase						:  str     					=	torch.tensor([1.7_956, 0.8_390, 0.2_820]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
								_UpperCAmelCase						:  List[str]     					=	torch.tensor([-0.2_822, -0.0_502, -0.0_878]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
								_UpperCAmelCase						:  List[Any]     					=	torch.tensor([-0.5_672, -0.0_730, -0.4_348]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
								_UpperCAmelCase						:  str     					=	torch.tensor([0.2_681, 0.2_365, 0.6_246]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
								_UpperCAmelCase						:  List[str]     					=	torch.tensor([-0.2_642, 0.3_931, 0.5_116]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
								_UpperCAmelCase						:  Tuple     					=	torch.tensor([-0.6_677, -0.1_873, -0.8_379]			)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
								_UpperCAmelCase						:  Any     					=	torch.tensor([-0.7_749, -0.2_967, -0.6_444]			)
				else:
								raise ValueError(F'''Unknown URL: {checkpoint_url}'''			)

				assert torch.allclose(logits[0, :3]      ,					_UpperCamelCase      ,					atol=1e-3			)
				assert logits.shape == expected_shape

				Path(_UpperCamelCase			).mkdir(exist_ok=_UpperCamelCase			)
				print(F'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(_UpperCamelCase			)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
				image_processor.save_pretrained(_UpperCamelCase			)

				print('''Pushing model to the hub...'''			)
				_UpperCAmelCase						:  Union[str, Any]     					=	'''convnext'''
				if "tiny" in checkpoint_url:
								model_name += "-tiny"
				elif "small" in checkpoint_url:
								model_name += "-small"
				elif "base" in checkpoint_url:
								model_name += "-base"
				elif "xlarge" in checkpoint_url:
								model_name += "-xlarge"
				elif "large" in checkpoint_url:
								model_name += "-large"
				if "224" in checkpoint_url:
								model_name += "-224"
				elif "384" in checkpoint_url:
								model_name += "-384"
				if "22k" in checkpoint_url and "1k" not in checkpoint_url:
								model_name += "-22k"
				if "22k" in checkpoint_url and "1k" in checkpoint_url:
								model_name += "-22k-1k"

				model.push_to_hub(
				    repo_path_or_name=Path(_UpperCamelCase      ,					_UpperCamelCase			)      ,					organization='''nielsr'''      ,					commit_message='''Add model'''      ,					)


if __name__ == "__main__":
					UpperCAmelCase__			:   List[Any]      			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--checkpoint_url',
					    default='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth',
					    type=str,
					    help='URL of the original ConvNeXT checkpoint you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to the output PyTorch model directory.',
					)

					UpperCAmelCase__			:   int      			=    parser.parse_args()
					convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
