






"""simple docstring"""






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
  raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
  raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
UpperCAmelCase_   : List[str]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Optional[int]   			= """The Nymphenburg Palace is a beautiful palace in Munich!"""



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		{
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 10_24,
        '''hidden_size''': 7_68,
        '''max_length''': 5_12,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 10_24,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1e-5,
        '''token_type_vocab_size''': 2,
    }

    SCREAMING_SNAKE_CASE_      :  str    						=		bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    SCREAMING_SNAKE_CASE_      :  Dict    						=		BERTEncoder(
        attention_cell=predefined_args['''attention_cell''']    ,				num_layers=predefined_args['''num_layers''']    ,				units=predefined_args['''units''']    ,				hidden_size=predefined_args['''hidden_size''']    ,				max_length=predefined_args['''max_length''']    ,				num_heads=predefined_args['''num_heads''']    ,				scaled=predefined_args['''scaled''']    ,				dropout=predefined_args['''dropout''']    ,				output_attention=__a    ,				output_all_encodings=__a    ,				use_residual=predefined_args['''use_residual''']    ,				activation=predefined_args.get('''activation'''    ,				'''gelu'''							)    ,				layer_norm_eps=predefined_args.get('''layer_norm_eps'''    ,				__a							)    ,				)

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    SCREAMING_SNAKE_CASE_      :  str    						=		'''openwebtext_ccnews_stories_books_cased'''

    # Specify download folder to Gluonnlp's vocab
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		os.path.join(get_home_dir()    ,				'''models'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		_load_vocab(__a    ,				__a    ,				__a    ,				cls=__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		nlp.model.BERTModel(
        __a    ,				len(__a							)    ,				units=predefined_args['''units''']    ,				embed_size=predefined_args['''embed_size''']    ,				embed_dropout=predefined_args['''embed_dropout''']    ,				word_embed=predefined_args['''word_embed''']    ,				use_pooler=__a    ,				use_token_type_embed=__a    ,				token_type_vocab_size=predefined_args['''token_type_vocab_size''']    ,				use_classifier=__a    ,				use_decoder=__a    ,				)

    original_bort.load_parameters(__a    ,				cast_dtype=__a    ,				ignore_extra=__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		original_bort._collect_params_with_prefix()

    # Build our config 🤗
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{
        '''architectures''': ['''BertForMaskedLM'''],
        '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_act''': '''gelu''',
        '''hidden_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_size''': predefined_args['''embed_size'''],
        '''initializer_range''': 0.02,
        '''intermediate_size''': predefined_args['''hidden_size'''],
        '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
        '''max_position_embeddings''': predefined_args['''max_length'''],
        '''model_type''': '''bort''',
        '''num_attention_heads''': predefined_args['''num_heads'''],
        '''num_hidden_layers''': predefined_args['''num_layers'''],
        '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
        '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
        '''vocab_size''': len(__a							),
    }

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		BertConfig.from_dict(__a							)
    SCREAMING_SNAKE_CASE_      :  str    						=		BertForMaskedLM(__a							)
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(__a							) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()							)							)

    # Check param shapes and map new HF param back
    def check_and_map_params(__a    ,				__a							):
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		hf_param.shape

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		to_torch(params[gluon_param]							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

        return gluon_param

    SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight    ,				'''word_embed.0.weight'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight    ,				'''encoder.position_weight'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias    ,				'''encoder.layer_norm.beta'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight    ,				'''encoder.layer_norm.gamma'''							)

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data							)

    for i in range(hf_bort_config.num_hidden_layers							):
        SCREAMING_SNAKE_CASE_      :  BertLayer    						=		hf_bort_model.bert.encoder.layer[i]

        # self attention
        SCREAMING_SNAKE_CASE_      :  BertSelfAttention    						=		layer.attention.self

        SCREAMING_SNAKE_CASE_      :  Dict    						=		check_and_map_params(
            self_attn.key.bias.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'							)

        SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
            self_attn.key.weight.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		check_and_map_params(
            self_attn.query.bias.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'							)
        SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
            self_attn.query.weight.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		check_and_map_params(
            self_attn.value.bias.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		check_and_map_params(
            self_attn.value.weight.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'							)

        # self attention output
        SCREAMING_SNAKE_CASE_      :  BertSelfOutput    						=		layer.attention.output

        SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_and_map_params(
            self_output.dense.bias    ,				f'encoder.transformer_cells.{i}.proj.bias'							)
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_and_map_params(
            self_output.dense.weight    ,				f'encoder.transformer_cells.{i}.proj.weight'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		check_and_map_params(
            self_output.LayerNorm.bias    ,				f'encoder.transformer_cells.{i}.layer_norm.beta'							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		check_and_map_params(
            self_output.LayerNorm.weight    ,				f'encoder.transformer_cells.{i}.layer_norm.gamma'							)

        # intermediate
        SCREAMING_SNAKE_CASE_      :  BertIntermediate    						=		layer.intermediate

        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		check_and_map_params(
            intermediate.dense.bias    ,				f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		check_and_map_params(
            intermediate.dense.weight    ,				f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'							)

        # output
        SCREAMING_SNAKE_CASE_      :  BertOutput    						=		layer.output

        SCREAMING_SNAKE_CASE_      :  Dict    						=		check_and_map_params(
            bert_output.dense.bias    ,				f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		check_and_map_params(
            bert_output.dense.weight    ,				f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'							)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		check_and_map_params(
            bert_output.LayerNorm.bias    ,				f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'							)
        SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
            bert_output.LayerNorm.weight    ,				f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'							)

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    SCREAMING_SNAKE_CASE_      :  int    						=		RobertaTokenizer.from_pretrained('''roberta-base'''							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer.encode_plus(__a							)['''input_ids''']

    # Get gluon output
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		mx.nd.array([input_ids]							)
    SCREAMING_SNAKE_CASE_      :  int    						=		original_bort(inputs=__a    ,				token_types=[]							)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		BertModel.from_pretrained(__a							)
    hf_bort_model.eval()

    SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer.encode_plus(__a    ,				return_tensors='''pt'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		hf_bort_model(**__a							)[0]

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		output_gluon[0].asnumpy()
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		output_hf[0].detach().numpy()

    SCREAMING_SNAKE_CASE_      :  int    						=		np.max(np.abs(hf_layer - gluon_layer							)							).item()
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		np.allclose(__a    ,				__a    ,				atol=1e-3							)

    if success:
        print('''✔️ Both model do output the same tensors'''							)
    else:
        print('''❌ Both model do **NOT** output the same tensors'''							)
        print('''Absolute difference is:'''    ,				__a							)


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_MAPPING,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerModel,
    )
    from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase					,  'hidden_sizes'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_attention_heads'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_encoder_blocks'))





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=64					,  lowercase=3					,  lowercase=4					,  lowercase=[2, 2, 2, 2]					,  lowercase=[8, 4, 2, 1]					,  lowercase=[16, 32, 64, 128]					,  lowercase=[1, 4, 8, 16]					,  lowercase=[1, 2, 4, 8]					,  lowercase=True					,  lowercase=True					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=0.02					,  lowercase=3					,  lowercase=None					,  )			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	int =				batch_size
          a__						:	Tuple =				image_size
          a__						:	Union[str, Any] =				num_channels
          a__						:	str =				num_encoder_blocks
          a__						:	Dict =				sr_ratios
          a__						:	Dict =				depths
          a__						:	Union[str, Any] =				hidden_sizes
          a__						:	str =				downsampling_rates
          a__						:	Tuple =				num_attention_heads
          a__						:	Optional[Any] =				is_training
          a__						:	Union[str, Any] =				use_labels
          a__						:	Any =				hidden_act
          a__						:	Optional[int] =				hidden_dropout_prob
          a__						:	int =				attention_probs_dropout_prob
          a__						:	Optional[Any] =				initializer_range
          a__						:	Tuple =				num_labels
          a__						:	Union[str, Any] =				scope

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[int] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	str =				None
          if self.use_labels:
                 a__						:	Dict =				ids_tensor([self.batch_size, self.image_size, self.image_size]					,  self.num_labels)

          a__						:	Any =				self.get_config()
          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return SegformerConfig(
              image_size=self.image_size					,  num_channels=self.num_channels					,  num_encoder_blocks=self.num_encoder_blocks					,  depths=self.depths					,  hidden_sizes=self.hidden_sizes					,  num_attention_heads=self.num_attention_heads					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Dict =				SegformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(lowercase)
          a__						:	Optional[Any] =				self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape					,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_labels
          a__						:	List[str] =				SegformerForSemanticSegmentation(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          a__						:	int =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				1
          a__						:	Optional[int] =				SegformerForSemanticSegmentation(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				torch.randint(0					,  1					,  (self.batch_size, self.image_size, self.image_size)).to(lowercase)
          a__						:	Optional[Any] =				model(lowercase					,  labels=lowercase)
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Any =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	str =				config_and_inputs
          a__						:	Optional[int] =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   __A   :				List[str]         =  (
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  True
   __A   :				Any         =  False
   __A   :				Any         =  False
   __A   :				str         =  False

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				SegformerModelTester(self)
          a__						:	Optional[Any] =				SegformerConfigTester(self					,  config_class=lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	int =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*lowercase)

   @unittest.skip('SegFormer does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				model_class(lowercase)
                 a__						:	Dict =				inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__						:	Optional[int] =				[*signature.parameters.keys()]

                 a__						:	Union[str, Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__						:	Dict =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 a__						:	str =				True
                 a__						:	List[str] =				False
                 a__						:	int =				True
                 a__						:	List[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[Any] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 a__						:	Dict =				sum(self.model_tester.depths)
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 a__						:	Dict =				True
                 a__						:	int =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[int] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first attentions (first block, first layer)
                 a__						:	Tuple =				(self.model_tester.image_size // 4) ** 2
                 a__						:	List[str] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

                 # verify the last attentions (last block, last layer)
                 a__						:	str =				(self.model_tester.image_size // 32) ** 2
                 a__						:	Optional[int] =				(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                 self.assertListEqual(
                     list(attentions[-1].shape[-3:])					,  [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,  )
                 a__						:	Dict =				len(lowercase)

                 # Check attention is always last and order is fine
                 a__						:	List[Any] =				True
                 a__						:	Any =				True
                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

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

                 a__						:	int =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)
                 # verify the first attentions (first block, first layer)
                 a__						:	List[Any] =				(self.model_tester.image_size // 4) ** 2
                 a__						:	Union[str, Any] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	Union[str, Any] =				outputs.hidden_states

                 a__						:	Any =				self.model_tester.num_encoder_blocks
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:])					,  [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]					,  )

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				True
                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__						:	int =				True

                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          if not self.model_tester.is_training:
                 return

          a__ ,							a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 if model_class in get_values(lowercase):
                        continue

                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.train()
                 a__						:	str =				self._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)
                 a__						:	Optional[int] =				model(**lowercase).loss
                 loss.backward()

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				SegformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	str =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





@require_torch
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	int =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Optional[int] =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[int] =				model(lowercase)

          a__						:	Union[str, Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Dict =				torch.tensor(
              [
                  [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                  [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                  [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-4))

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained(
              'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(lowercase)

          a__						:	Dict =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[Any] =				model(lowercase)

          a__						:	List[Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor(
              [
                  [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                  [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                  [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-1))

   @slow
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Any =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	Optional[int] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Union[str, Any] =				model(lowercase)

          a__						:	int =				outputs.logits.detach().cpu()

          a__						:	List[str] =				image_processor.post_process_semantic_segmentation(outputs=lowercase					,  target_sizes=[(500, 300)])
          a__						:	Optional[Any] =				torch.Size((500, 300))
          self.assertEqual(segmentation[0].shape					,  lowercase)

          a__						:	Any =				image_processor.post_process_semantic_segmentation(outputs=lowercase)
          a__						:	Union[str, Any] =				torch.Size((128, 128))
          self.assertEqual(segmentation[0].shape					,  lowercase)


