'''simple docstring'''

import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


A_     :						str    							={
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

A_     :						int    							={
    '''169M''': 7_68,
    '''430M''': 10_24,
    '''1B5''': 20_48,
    '''3B''': 25_60,
    '''7B''': 40_96,
    '''14B''': 51_20,
}



def snake_case_      (					__snake_case  :							List[Any])			->					Dict:
   lowerCAmelCase_			=      list(state_dict.keys())
   for name in state_dict_keys:
      lowerCAmelCase_			=      state_dict.pop(__snake_case)
      # emb -> embedding
      if name.startswith('''emb.'''):
         lowerCAmelCase_			=      name.replace('''emb.'''	,   '''embeddings.''')
      # ln_0 -> pre_ln (only present at block 0)
      if name.startswith('''blocks.0.ln0'''):
         lowerCAmelCase_			=      name.replace('''blocks.0.ln0'''	,   '''blocks.0.pre_ln''')
      # att -> attention
      lowerCAmelCase_			=      re.sub(R'''blocks\.(\d+)\.att'''	,   R'''blocks.\1.attention'''	,   __snake_case)
      # ffn -> feed_forward
      lowerCAmelCase_			=      re.sub(R'''blocks\.(\d+)\.ffn'''	,   R'''blocks.\1.feed_forward'''	,   __snake_case)
      # time_mix_k -> time_mix_key and reshape
      if name.endswith('''.time_mix_k'''):
         lowerCAmelCase_			=      name.replace('''.time_mix_k'''	,   '''.time_mix_key''')
      # time_mix_v -> time_mix_value and reshape
      if name.endswith('''.time_mix_v'''):
         lowerCAmelCase_			=      name.replace('''.time_mix_v'''	,   '''.time_mix_value''')
      # time_mix_r -> time_mix_key and reshape
      if name.endswith('''.time_mix_r'''):
         lowerCAmelCase_			=      name.replace('''.time_mix_r'''	,   '''.time_mix_receptance''')

      if name != "head.weight":
         lowerCAmelCase_			=      '''rwkv.''' + name

      lowerCAmelCase_			=      weight
   return state_dict



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							Optional[Any]	,   __snake_case  :							Optional[int]	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							Optional[Any]=None	,   __snake_case  :							int=False	,   __snake_case  :							Optional[int]=None)			->					Union[str, Any]:
   # 1. If possible, build the tokenizer.
   if tokenizer_file is None:
      print('''No `--tokenizer_file` provided, we will use the default tokenizer.''')
      lowerCAmelCase_			=      50277
      lowerCAmelCase_			=      AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''')
   else:
      lowerCAmelCase_			=      PreTrainedTokenizerFast(tokenizer_file=__snake_case)
      lowerCAmelCase_			=      len(__snake_case)
   tokenizer.save_pretrained(__snake_case)

   # 2. Build the config
   lowerCAmelCase_			=      list(NUM_HIDDEN_LAYERS_MAPPING.keys())
   if size is None:
      # Try to infer size from the checkpoint name
      for candidate in possible_sizes:
         if candidate in checkpoint_file:
            lowerCAmelCase_			=      candidate
            break
      if size is None:
         raise ValueError('''Could not infer the size, please provide it with the `--size` argument.''')
   if size not in possible_sizes:
      raise ValueError(F'''`size` should be one of {possible_sizes}, got {size}.''')

   lowerCAmelCase_			=      RwkvConfig(
       vocab_size=__snake_case	,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]	,   hidden_size=HIDEN_SIZE_MAPPING[size]	,   )
   config.save_pretrained(__snake_case)

   # 3. Download model file then convert state_dict
   lowerCAmelCase_			=      hf_hub_download(__snake_case	,   __snake_case)
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
   lowerCAmelCase_			=      convert_state_dict(__snake_case)

   # 4. Split in shards and save
   lowerCAmelCase_	,lowerCAmelCase_			=      shard_checkpoint(__snake_case)
   for shard_file, shard in shards.items():
      torch.save(__snake_case	,   os.path.join(__snake_case	,   __snake_case))

   if index is not None:
      lowerCAmelCase_			=      os.path.join(__snake_case	,   __snake_case)
      # Save the index as well
      with open(__snake_case	,   '''w'''	,   encoding='''utf-8''') as f:
         lowerCAmelCase_			=      json.dumps(__snake_case	,   indent=2	,   sort_keys=__snake_case) + '''\n'''
         f.write(__snake_case)

      # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
      print(
          '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.''')
      lowerCAmelCase_			=      list(shards.keys())

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         lowerCAmelCase_			=      torch.load(os.path.join(__snake_case	,   __snake_case))
         torch.save({k: v.cpu().clone() for k, v in state_dict.items()}	,   os.path.join(__snake_case	,   __snake_case))

   del state_dict
   gc.collect()

   if push_to_hub:
      if model_name is None:
         raise ValueError('''Please provide a `model_name` to push the model to the Hub.''')
      lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(__snake_case)
      model.push_to_hub(__snake_case	,   max_shard_size='''2GB''')
      tokenizer.push_to_hub(__snake_case)


if __name__ == "__main__":
 A_     :						List[str]    							=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
 )
 parser.add_argument(
     '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
 )
 parser.add_argument(
     '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
 )
 parser.add_argument(
     '''--tokenizer_file''',
     default=None,
     type=str,
     help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
 )
 parser.add_argument(
     '''--size''',
     default=None,
     type=str,
     help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
 )
 parser.add_argument(
     '''--push_to_hub''',
     action='''store_true''',
     help='''Push to the Hub the converted model.''',
 )
 parser.add_argument(
     '''--model_name''',
     default=None,
     type=str,
     help='''Name of the pushed model on the Hub, including the username / organization.''',
 )

 A_     :						List[str]    							=parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )






'''simple docstring'''

import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=13    ,     _lowerCamelCase=32    ,     _lowerCamelCase=2    ,     _lowerCamelCase=3    ,     _lowerCamelCase=16    ,     _lowerCamelCase=[32, 64, 128]    ,     _lowerCamelCase=[1, 2, 1]    ,     _lowerCamelCase=[2, 2, 4]    ,     _lowerCamelCase=2    ,     _lowerCamelCase=2.0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=1E-5    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase=10    ,     _lowerCamelCase=8    ,     _lowerCamelCase=["stage1", "stage2"]    ,     _lowerCamelCase=[1, 2]    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      patch_size
     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      embed_dim
     lowerCAmelCase_			=      hidden_sizes
     lowerCAmelCase_			=      depths
     lowerCAmelCase_			=      num_heads
     lowerCAmelCase_			=      window_size
     lowerCAmelCase_			=      mlp_ratio
     lowerCAmelCase_			=      qkv_bias
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      drop_path_rate
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      use_absolute_embeddings
     lowerCAmelCase_			=      patch_norm
     lowerCAmelCase_			=      layer_norm_eps
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      scope
     lowerCAmelCase_			=      use_labels
     lowerCAmelCase_			=      type_sequence_label_size
     lowerCAmelCase_			=      encoder_stride
     lowerCAmelCase_			=      out_features
     lowerCAmelCase_			=      out_indices





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     lowerCAmelCase_			=      None
     if self.use_labels:
        lowerCAmelCase_			=      ids_tensor([self.batch_size]    ,     self.type_sequence_label_size )

     lowerCAmelCase_			=      self.get_config()

     return config, pixel_values, labels





  def 						UpperCAmelCase_	(	self ):
     return FocalNetConfig(
         image_size=self.image_size    ,     patch_size=self.patch_size    ,     num_channels=self.num_channels    ,     embed_dim=self.embed_dim    ,     hidden_sizes=self.hidden_sizes    ,     depths=self.depths    ,     num_heads=self.num_heads    ,     window_size=self.window_size    ,     mlp_ratio=self.mlp_ratio    ,     qkv_bias=self.qkv_bias    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     drop_path_rate=self.drop_path_rate    ,     hidden_act=self.hidden_act    ,     use_absolute_embeddings=self.use_absolute_embeddings    ,     path_norm=self.patch_norm    ,     layer_norm_eps=self.layer_norm_eps    ,     initializer_range=self.initializer_range    ,     encoder_stride=self.encoder_stride    ,     out_features=self.out_features    ,     out_indices=self.out_indices    ,     )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      FocalNetModel(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )

     lowerCAmelCase_			=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
     lowerCAmelCase_			=      int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

     self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, expected_seq_len, expected_dim) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      FocalNetBackbone(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps )    ,     len(config.out_features ) )
     self.parent.assertListEqual(list(result.feature_maps[0].shape )    ,     [self.batch_size, self.image_size, 8, 8] )

     # verify channels
     self.parent.assertEqual(len(model.channels )    ,     len(config.out_features ) )
     self.parent.assertListEqual(model.channels    ,     config.hidden_sizes[:-1] )

     # verify backbone works with out_features=None
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      FocalNetBackbone(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps )    ,     1 )
     self.parent.assertListEqual(list(result.feature_maps[0].shape )    ,     [self.batch_size, self.image_size * 2, 4, 4] )

     # verify channels
     self.parent.assertEqual(len(model.channels )    ,     1 )
     self.parent.assertListEqual(model.channels    ,     [config.hidden_sizes[-1]] )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      FocalNetForMaskedImageModeling(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase )
     self.parent.assertEqual(
         result.reconstruction.shape    ,     (self.batch_size, self.num_channels, self.image_size, self.image_size) )

     # test greyscale images
     lowerCAmelCase_			=      1
     lowerCAmelCase_			=      FocalNetForMaskedImageModeling(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

     lowerCAmelCase_			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowerCAmelCase_			=      model(_lowerCamelCase )
     self.parent.assertEqual(result.reconstruction.shape    ,     (self.batch_size, 1, self.image_size, self.image_size) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.type_sequence_label_size
     lowerCAmelCase_			=      FocalNetForImageClassification(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()
     lowerCAmelCase_			=      model(_lowerCamelCase    ,     labels=_lowerCamelCase )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     lowerCAmelCase_			=      1
     lowerCAmelCase_			=      FocalNetForImageClassification(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

     lowerCAmelCase_			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowerCAmelCase_			=      model(_lowerCamelCase )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.type_sequence_label_size) )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.prepare_config_and_inputs()

     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      config_and_inputs
     lowerCAmelCase_			=      {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class     __UpperCAmelCase       ( __a						,       __a						,       unittest.TestCase		):
  __A :				Tuple											= (
      (
          FocalNetModel,
          FocalNetForImageClassification,
          FocalNetForMaskedImageModeling,
          FocalNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  __A :				Any											= (
      {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
      if is_torch_available()
      else {}
  )
  __A :				List[str]											= False

  __A :				Optional[Any]											= False
  __A :				Union[str, Any]											= False
  __A :				List[str]											= False
  __A :				Optional[Any]											= False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      FocalNetModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=_lowerCamelCase    ,     embed_dim=37    ,     has_text_modality=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()





  def 						UpperCAmelCase_	(	self ):
     return





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )





  @unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      model_class(_lowerCamelCase )
        self.assertIsInstance(model.get_input_embeddings()    ,     (nn.Module) )
        lowerCAmelCase_			=      model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_lowerCamelCase    ,     nn.Linear ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      model_class(_lowerCamelCase )
        lowerCAmelCase_			=      inspect.signature(model.forward )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCAmelCase_			=      [*signature.parameters.keys()]

        lowerCAmelCase_			=      ['''pixel_values''']
        self.assertListEqual(arg_names[:1]    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      model_class(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

     with torch.no_grad():
        lowerCAmelCase_			=      model(**self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase ) )

     lowerCAmelCase_			=      outputs.hidden_states

     lowerCAmelCase_			=      getattr(
         self.model_tester    ,     '''expected_num_hidden_layers'''    ,     len(self.model_tester.depths ) + 1 )
     self.assertEqual(len(_lowerCamelCase )    ,     _lowerCamelCase )

     # FocalNet has a different seq_length
     lowerCAmelCase_			=      (
         config.patch_size
         if isinstance(config.patch_size    ,     collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

     lowerCAmelCase_			=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

     lowerCAmelCase_			=      outputs.reshaped_hidden_states
     self.assertEqual(len(_lowerCamelCase )    ,     _lowerCamelCase )

     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      reshaped_hidden_states[0].shape
     lowerCAmelCase_			=      (
         reshaped_hidden_states[0].view(_lowerCamelCase    ,     _lowerCamelCase    ,     height * width ).permute(0    ,     2    ,     1 )
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:] )    ,     [num_patches, self.model_tester.embed_dim]    ,     )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     lowerCAmelCase_			=      (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size    ,     collections.abc.Iterable )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      True
        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

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

        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()
     lowerCAmelCase_			=      3

     lowerCAmelCase_			=      (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size    ,     collections.abc.Iterable )
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     lowerCAmelCase_			=      (
         config.patch_size
         if isinstance(config.patch_size    ,     collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

     lowerCAmelCase_			=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     lowerCAmelCase_			=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes[:-1]:
        lowerCAmelCase_			=      True
        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     (padded_height, padded_width) )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowerCAmelCase_			=      True
        self.check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     (padded_height, padded_width) )





  @slow
  def 						UpperCAmelCase_	(	self ):
     for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_			=      FocalNetModel.from_pretrained(_lowerCamelCase )
        self.assertIsNotNone(_lowerCamelCase )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     lowerCAmelCase_			=      _config_zero_init(_lowerCamelCase )
     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      model_class(config=_lowerCamelCase )
        for name, param in model.named_parameters():
           if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()    ,     [0.0, 1.0]    ,     msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,     )






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





  @cached_property
  def 						UpperCAmelCase_	(	self ):
     # TODO update organization
     return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None







  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      self.default_image_processor

     lowerCAmelCase_			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     lowerCAmelCase_			=      image_processor(images=_lowerCamelCase    ,     return_tensors='''pt''' ).to(_lowerCamelCase )

     # forward pass
     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )

     # verify the logits
     lowerCAmelCase_			=      torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape    ,     _lowerCamelCase )
     lowerCAmelCase_			=      torch.tensor([0.21_66, -0.43_68, 0.21_91] ).to(_lowerCamelCase )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,     _lowerCamelCase    ,     atol=1E-4 ) )
     self.assertTrue(outputs.logits.argmax(dim=-1 ).item()    ,     281 )






@require_torch
class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				Dict											= (FocalNetBackbone,) if is_torch_available() else ()
  __A :				List[str]											= FocalNetConfig

  __A :				Optional[int]											= False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      FocalNetModelTester(self )





