

'''simple docstring'''



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Dict=99					,							__SCREAMING_SNAKE_CASE					:       Dict=13					,							__SCREAMING_SNAKE_CASE					:       str=7					,							__SCREAMING_SNAKE_CASE					:       int=9					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=32					,							__SCREAMING_SNAKE_CASE					:       List[Any]=5					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=4					,							__SCREAMING_SNAKE_CASE					:       List[Any]=37					,							__SCREAMING_SNAKE_CASE					:       Any=8					,							__SCREAMING_SNAKE_CASE					:       Any=0.1					,							__SCREAMING_SNAKE_CASE					:       Any=0.002					,							__SCREAMING_SNAKE_CASE					:       List[str]=1					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0					,							__SCREAMING_SNAKE_CASE					:       str=0					,							__SCREAMING_SNAKE_CASE					:       List[Any]=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      encoder_seq_length
      __SCREAMING_SNAKE_CASE     =      decoder_seq_length
      # For common tests
      __SCREAMING_SNAKE_CASE     =      self.decoder_seq_length
      __SCREAMING_SNAKE_CASE     =      is_training
      __SCREAMING_SNAKE_CASE     =      use_attention_mask
      __SCREAMING_SNAKE_CASE     =      use_labels
      __SCREAMING_SNAKE_CASE     =      vocab_size
      __SCREAMING_SNAKE_CASE     =      hidden_size
      __SCREAMING_SNAKE_CASE     =      num_hidden_layers
      __SCREAMING_SNAKE_CASE     =      num_attention_heads
      __SCREAMING_SNAKE_CASE     =      d_ff
      __SCREAMING_SNAKE_CASE     =      relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE     =      dropout_rate
      __SCREAMING_SNAKE_CASE     =      initializer_factor
      __SCREAMING_SNAKE_CASE     =      eos_token_id
      __SCREAMING_SNAKE_CASE     =      pad_token_id
      __SCREAMING_SNAKE_CASE     =      decoder_start_token_id
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      decoder_layers
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return TaConfig.from_pretrained("""google/umt5-base"""			)
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       int=None					,							__SCREAMING_SNAKE_CASE					:       List[Any]=None					,							__SCREAMING_SNAKE_CASE					:       Any=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=None					,							)  -> Tuple:


      """simple docstring"""




      if attention_mask is None:
           __SCREAMING_SNAKE_CASE     =      input_ids.ne(config.pad_token_id			)
      if decoder_attention_mask is None:
           __SCREAMING_SNAKE_CASE     =      decoder_input_ids.ne(config.pad_token_id			)
      if head_mask is None:
           __SCREAMING_SNAKE_CASE     =      torch.ones(config.num_hidden_layers					,							config.num_attention_heads					,							device=__SCREAMING_SNAKE_CASE			)
      if decoder_head_mask is None:
           __SCREAMING_SNAKE_CASE     =      torch.ones(config.num_decoder_layers					,							config.num_attention_heads					,							device=__SCREAMING_SNAKE_CASE			)
      if cross_attn_head_mask is None:
           __SCREAMING_SNAKE_CASE     =      torch.ones(
               config.num_decoder_layers					,							config.num_attention_heads					,							device=__SCREAMING_SNAKE_CASE			)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size, self.encoder_seq_length]					,							self.vocab_size			)
      __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size, self.decoder_seq_length]					,							self.vocab_size			)

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

      __SCREAMING_SNAKE_CASE     =      self.get_config()
      __SCREAMING_SNAKE_CASE     =      config.num_attention_heads
      __SCREAMING_SNAKE_CASE     =      self.prepare_inputs_dict(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      return config, input_dict
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.prepare_config_and_inputs()
      return config, inputs_dict
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      return TaConfig(
          vocab_size=166					,							d_model=self.hidden_size					,							d_ff=self.d_ff					,							d_kv=self.hidden_size // self.num_attention_heads					,							num_layers=self.num_hidden_layers					,							num_decoder_layers=self.decoder_layers					,							num_heads=self.num_attention_heads					,							relative_attention_num_buckets=self.relative_attention_num_buckets					,							dropout_rate=self.dropout_rate					,							initializer_factor=self.initializer_factor					,							eos_token_id=self.eos_token_id					,							bos_token_id=self.pad_token_id					,							pad_token_id=self.pad_token_id					,							decoder_start_token_id=self.decoder_start_token_id					,							)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Optional[int]:


      """simple docstring"""




      return TaConfig(
          vocab_size=self.vocab_size					,							d_model=self.hidden_size					,							d_ff=self.d_ff					,							d_kv=self.hidden_size // self.num_attention_heads					,							num_layers=self.num_hidden_layers					,							num_decoder_layers=self.decoder_layers					,							num_heads=self.num_attention_heads					,							relative_attention_num_buckets=self.relative_attention_num_buckets					,							dropout_rate=self.dropout_rate					,							initializer_factor=self.initializer_factor					,							eos_token_id=self.eos_token_id					,							bos_token_id=self.pad_token_id					,							pad_token_id=self.pad_token_id					,							decoder_start_token_id=self.decoder_start_token_id					,							)
 def       UpperCAmelCase__		(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      UMTaModel(config=__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(
          input_ids=__SCREAMING_SNAKE_CASE					,							decoder_input_ids=__SCREAMING_SNAKE_CASE					,							attention_mask=__SCREAMING_SNAKE_CASE					,							decoder_attention_mask=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      model(input_ids=__SCREAMING_SNAKE_CASE					,							decoder_input_ids=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      result.last_hidden_state
      __SCREAMING_SNAKE_CASE     =      result.past_key_values
      __SCREAMING_SNAKE_CASE     =      result.encoder_last_hidden_state

      self.parent.assertEqual(encoder_output.size()					,							(self.batch_size, self.encoder_seq_length, self.hidden_size)			)
      self.parent.assertEqual(decoder_output.size()					,							(self.batch_size, self.decoder_seq_length, self.hidden_size)			)
      # There should be `num_layers` key value embeddings stored in decoder_past
      self.parent.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							config.num_layers			)
      # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
      self.parent.assertEqual(len(decoder_past[0]			)					,							4			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      UMTaModel(config=__SCREAMING_SNAKE_CASE			).get_decoder().to(__SCREAMING_SNAKE_CASE			).eval()
      # first forward pass
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE					,							use_cache=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE					,							use_cache=__SCREAMING_SNAKE_CASE			)

      self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			)			)
      self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			) + 1			)

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __SCREAMING_SNAKE_CASE     =      ids_tensor((self.batch_size, 1)					,							config.vocab_size			)

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

      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE					,							past_key_values=__SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]

      # select random slice
      __SCREAMING_SNAKE_CASE     =      ids_tensor((1,)					,							output_from_past.shape[-1]			).item()
      __SCREAMING_SNAKE_CASE     =      output_from_no_past[:, -1, random_slice_idx].detach()
      __SCREAMING_SNAKE_CASE     =      output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)


 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      UMTaModel(config=__SCREAMING_SNAKE_CASE			).to(__SCREAMING_SNAKE_CASE			).half().eval()
      __SCREAMING_SNAKE_CASE     =      model(**__SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
      self.parent.assertFalse(torch.isnan(__SCREAMING_SNAKE_CASE			).any().item()			)







@require_torch
class 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 lowerCAmelCase__  					=      (UMTaForConditionalGeneration,) if is_torch_available() else ()
 lowerCAmelCase__  					=      (
     {
         "conversational": UMTaForConditionalGeneration,
         "feature-extraction": UMTaModel,
         "summarization": UMTaForConditionalGeneration,
         "text2text-generation": UMTaForConditionalGeneration,
         "translation": UMTaForConditionalGeneration,
         "question-answering": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 lowerCAmelCase__  					=      [0.8, 0.9]
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      UMTaModelTester(self			)
 @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""			)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE     =      UMTaModel(config_and_inputs[0]			).to(__SCREAMING_SNAKE_CASE			)
      with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               __SCREAMING_SNAKE_CASE					,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,							f'{tmpdirname}/t5_test.onnx'					,							export_params=__SCREAMING_SNAKE_CASE					,							opset_version=9					,							input_names=["""input_ids""", """decoder_input_ids"""]					,							)
 @unittest.skipIf(torch_device == """cpu"""					,							"""Cant do half precision"""			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE     =      config_and_inputs[0]
      __SCREAMING_SNAKE_CASE     =      UMTaForConditionalGeneration(__SCREAMING_SNAKE_CASE			).eval()
      model.to(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      {
          """head_mask""": torch.zeros(config.num_layers					,							config.num_heads					,							device=__SCREAMING_SNAKE_CASE			),
          """decoder_head_mask""": torch.zeros(config.num_decoder_layers					,							config.num_heads					,							device=__SCREAMING_SNAKE_CASE			),
          """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers					,							config.num_heads					,							device=__SCREAMING_SNAKE_CASE			),
      }

      for attn_name, (name, mask) in zip(__SCREAMING_SNAKE_CASE					,							head_masking.items()			):
           __SCREAMING_SNAKE_CASE     =      {name: mask}
           # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
           if name == "head_mask":
                __SCREAMING_SNAKE_CASE     =      torch.ones(
                    config.num_decoder_layers					,							config.num_heads					,							device=__SCREAMING_SNAKE_CASE			)

           __SCREAMING_SNAKE_CASE     =      model.generate(
               config_and_inputs[1]["""input_ids"""]					,							num_beams=1					,							max_length=3					,							output_attentions=__SCREAMING_SNAKE_CASE					,							return_dict_in_generate=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
           # We check the state of decoder_attentions and cross_attentions just from the last step
           __SCREAMING_SNAKE_CASE     =      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
           self.assertEqual(sum([w.sum().item() for w in attn_weights]			)					,							0.0			)


 @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Optional[Any]:


      """simple docstring"""




      pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @slow
 @unittest.skip(
     """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""					,							return_dict=__SCREAMING_SNAKE_CASE			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoTokenizer.from_pretrained("""google/umt5-small"""					,							use_fast=__SCREAMING_SNAKE_CASE					,							legacy=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      [
          """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
          """No se como puedo <extra_id_0>.""",
          """This is the reason why we <extra_id_0> them.""",
          """The <extra_id_0> walks in <extra_id_1>, seats""",
          """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
      ]
      __SCREAMING_SNAKE_CASE     =      tokenizer(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""					,							padding=__SCREAMING_SNAKE_CASE			).input_ids
      # fmt: off
      __SCREAMING_SNAKE_CASE     =      torch.tensor(
          [
              [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
          ]			)
      # fmt: on
      torch.testing.assert_allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      model.generate(input_ids.to(__SCREAMING_SNAKE_CASE			)			)
      __SCREAMING_SNAKE_CASE     =      [
          """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
          """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
          """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
          """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
          """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
      ]
      __SCREAMING_SNAKE_CASE     =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)

UpperCAmelCase :       str			   =     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "convnextv2"
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       int=3					,							__SCREAMING_SNAKE_CASE					:       Dict=4					,							__SCREAMING_SNAKE_CASE					:       List[Any]=4					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="gelu"					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=0.02					,							__SCREAMING_SNAKE_CASE					:       Dict=1E-12					,							__SCREAMING_SNAKE_CASE					:       List[str]=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=224					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							)  -> Union[str, Any]:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_stages
      __SCREAMING_SNAKE_CASE     =      [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      __SCREAMING_SNAKE_CASE     =      [3, 3, 9, 3] if depths is None else depths
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(self.depths			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






