




"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A_      			=       [
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
A_      			=       [
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

A_      			=       (
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

A_      			=       (
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

A_      			=       [
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]


def 					UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Tuple					):
  """simple docstring"""

  for tf_name, hf_name in patterns:
    _snake_case							:	Optional[Any]			=						k.replace(snake_case_	,     snake_case_					)
  return k


def 					UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						BigBirdPegasusConfig(**snake_case_					)
  _snake_case							:	Tuple			=						BigBirdPegasusForConditionalGeneration(snake_case_					)
  _snake_case							:	Union[str, Any]			=						torch_model.state_dict()
  _snake_case							:	List[str]			=						{}

  # separating decoder weights
  _snake_case							:	Any			=						{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""					)}
  _snake_case							:	Union[str, Any]			=						{k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""					)}

  for k, v in tqdm(decoder_weights.items()	,     """tf -> hf conversion"""					):
    _snake_case							:	Tuple			=						[k.endswith(snake_case_					) for ending in KEYS_TO_IGNORE]
    if any(snake_case_					):
      continue
    _snake_case							:	Optional[int]			=						DECODER_PATTERNS
    _snake_case							:	List[Any]			=						rename_state_dict_key(snake_case_	,     snake_case_					)
    if new_k not in state_dict:
      raise ValueError(F"could not find new key {new_k} in state dict. (converted from {k})"					)
    if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]					):
      _snake_case							:	List[Any]			=						v.T
    _snake_case							:	Dict			=						torch.from_numpy(snake_case_					)
    assert v.shape == state_dict[new_k].shape, F"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"

  for k, v in tqdm(remaining_weights.items()	,     """tf -> hf conversion"""					):
    _snake_case							:	Dict			=						[k.endswith(snake_case_					) for ending in KEYS_TO_IGNORE]
    if any(snake_case_					):
      continue
    _snake_case							:	Any			=						REMAINING_PATTERNS
    _snake_case							:	List[Any]			=						rename_state_dict_key(snake_case_	,     snake_case_					)
    if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
      raise ValueError(F"could not find new key {new_k} in state dict. (converted from {k})"					)
    if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]					):
      _snake_case							:	List[Any]			=						v.T
    _snake_case							:	Tuple			=						torch.from_numpy(snake_case_					)
    if k != "pegasus/embeddings/position_embeddings":
      assert v.shape == state_dict[new_k].shape, F"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"

  _snake_case							:	List[Any]			=						mapping["""model.embed_positions.weight"""]
  _snake_case							:	List[str]			=						mapping.pop("""model.embed_positions.weight"""					)
  _snake_case							:	Dict			=						torch_model.load_state_dict(snake_case_	,     strict=snake_case_					)
  _snake_case							:	List[str]			=						[
      k
      for k in missing
      if k
      not in [
          """final_logits_bias""",
          """model.encoder.embed_tokens.weight""",
          """model.decoder.embed_tokens.weight""",
          """lm_head.weight""",
      ]
  ]
  assert unexpected_missing == [], F"no matches found for the following torch keys {unexpected_missing}"
  assert extra == [], F"no matches found for the following tf keys {extra}"
  return torch_model


def 					UpperCAmelCase__ (snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						tf.train.list_variables(snake_case_					)
  _snake_case							:	Any			=						{}
  _snake_case							:	Optional[Any]			=						["""global_step"""]
  for name, shape in tqdm(snake_case_	,     desc="""converting tf checkpoint to dict"""					):
    _snake_case							:	Union[str, Any]			=						any(pat in name for pat in ignore_name					)
    if skip_key:
      continue
    _snake_case							:	List[str]			=						tf.train.load_variable(snake_case_	,     snake_case_					)
    _snake_case							:	Optional[int]			=						array
  return tf_weights


def 					UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	int			=						get_tf_weights_as_numpy(snake_case_					)
  _snake_case							:	Optional[Any]			=						convert_bigbird_pegasus(snake_case_	,     snake_case_					)
  torch_model.save_pretrained(snake_case_					)


if __name__ == "__main__":
     A_      			=       argparse.ArgumentParser()
     parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
     parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
     A_      			=       parser.parse_args()
     A_      			=       {}
     convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








"""simple docstring"""


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
         MODEL_MAPPING,
         BeitForImageClassification,
         BeitForMaskedImageModeling,
         BeitForSemanticSegmentation,
         BeitModel,
     )
     from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     import PIL
     from PIL import Image

     from transformers import BeitImageProcessor






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	Optional[Any],   a_:	Union[str, Any],   a_:	int=100,   a_:	int=13,   a_:	List[Any]=30,   a_:	str=2,   a_:	Optional[Any]=3,   a_:	Optional[int]=True,   a_:	Any=True,   a_:	Optional[Any]=32,   a_:	Tuple=4,   a_:	str=4,   a_:	List[Any]=37,   a_:	List[str]="gelu",   a_:	str=0.1,   a_:	Optional[int]=0.1,   a_:	Any=10,   a_:	List[str]=0.02,   a_:	Dict=3,   a_:	str=None,   a_:	Optional[int]=[0, 1, 2, 3],   ):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						parent
   _snake_case							:	Optional[Any]			=						100
   _snake_case							:	Any			=						batch_size
   _snake_case							:	List[Any]			=						image_size
   _snake_case							:	Optional[Any]			=						patch_size
   _snake_case							:	str			=						num_channels
   _snake_case							:	Tuple			=						is_training
   _snake_case							:	Tuple			=						use_labels
   _snake_case							:	Any			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	List[str]			=						num_attention_heads
   _snake_case							:	Union[str, Any]			=						intermediate_size
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	str			=						hidden_dropout_prob
   _snake_case							:	Optional[int]			=						attention_probs_dropout_prob
   _snake_case							:	Optional[Any]			=						type_sequence_label_size
   _snake_case							:	Any			=						initializer_range
   _snake_case							:	List[str]			=						scope
   _snake_case							:	int			=						out_indices
   _snake_case							:	Optional[Any]			=						num_labels

   # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   _snake_case							:	Dict			=						(image_size // patch_size) ** 2
   _snake_case							:	str			=						num_patches + 1


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



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

   _snake_case							:	List[Any]			=						None
   _snake_case							:	Tuple			=						None
   if self.use_labels:
     _snake_case							:	str			=						ids_tensor([self.batch_size],   self.type_sequence_label_size	)
     _snake_case							:	List[str]			=						ids_tensor([self.batch_size, self.image_size, self.image_size],   self.num_labels	)

   _snake_case							:	List[str]			=						self.get_config()

   return config, pixel_values, labels, pixel_labels


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return BeitConfig(
       vocab_size=self.vocab_size,   image_size=self.image_size,   patch_size=self.patch_size,   num_channels=self.num_channels,   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,   is_decoder=a_,   initializer_range=self.initializer_range,   out_indices=self.out_indices,   )


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	List[Any],   a_:	Any,   a_:	Optional[Any],   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						BeitModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Dict			=						model(a_	)
   self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)	)


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any],   a_:	Optional[Any],   a_:	Optional[int],   a_:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						BeitForMaskedImageModeling(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Union[str, Any]			=						model(a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 				UpperCamelCase_     (       self:	Any,   a_:	List[str],   a_:	Any,   a_:	List[Any],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.type_sequence_label_size
   _snake_case							:	Any			=						BeitForImageClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[Any]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size)	)

   # test greyscale images
   _snake_case							:	Any			=						1
   _snake_case							:	str			=						BeitForImageClassification(a_	)
   model.to(a_	)
   model.eval()

   _snake_case							:	Dict			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
   _snake_case							:	Optional[Any]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size)	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Optional[int],   a_:	List[Any],   a_:	str,   a_:	int	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.num_labels
   _snake_case							:	List[Any]			=						BeitForSemanticSegmentation(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[str]			=						model(a_	)
   self.parent.assertEqual(
       result.logits.shape,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
   _snake_case							:	str			=						model(a_,   labels=a_	)
   self.parent.assertEqual(
       result.logits.shape,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case	,      _snake_case							:	Any			=						config_and_inputs
   _snake_case							:	Optional[int]			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {
         "feature-extraction": BeitModel,
         "image-classification": BeitForImageClassification,
         "image-segmentation": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						BeitModelTester(self	)
   _snake_case							:	int			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 @unittest.skip(reason="""BEiT does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   pass


 @require_torch_multi_gpu
 @unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Any			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	List[str]			=						model_class(a_	)
     self.assertIsInstance(model.get_input_embeddings(),   (nn.Module)	)
     _snake_case							:	Tuple			=						model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(a_,   nn.Linear	)	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	Any			=						model_class(a_	)
     _snake_case							:	List[str]			=						inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:	List[Any]			=						[*signature.parameters.keys()]

     _snake_case							:	List[Any]			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   _snake_case	,      _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs_for_common()
   _snake_case							:	Any			=						True

   for model_class in self.all_model_classes:
     # we don't test BeitForMaskedImageModeling
     if model_class in [*get_values(a_	), BeitForMaskedImageModeling]:
       continue

     _snake_case							:	List[Any]			=						model_class(a_	)
     model.to(a_	)
     model.train()
     _snake_case							:	Dict			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	List[Any]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
   if not self.model_tester.is_training:
     return

   _snake_case							:	Dict			=						False
   _snake_case							:	Optional[Any]			=						True

   for model_class in self.all_model_classes:
     # we don't test BeitForMaskedImageModeling
     if (
         model_class in [*get_values(a_	), BeitForMaskedImageModeling]
         or not model_class.supports_gradient_checkpointing
     ):
       continue

     _snake_case							:	Any			=						model_class(a_	)
     model.gradient_checkpointing_enable()
     model.to(a_	)
     model.train()
     _snake_case							:	Any			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	int			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs_for_common()

   _snake_case							:	int			=						_config_zero_init(a_	)
   for model_class in self.all_model_classes:
     _snake_case							:	Tuple			=						model_class(config=a_	)
     for name, param in model.named_parameters():
       # we skip lambda parameters as these require special initial values
       # determined by config.layer_scale_init_value
       if "lambda" in name:
         continue
       if 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",   )


 @slow
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	Optional[int]			=						BeitModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image






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







 '''simple docstring'''





 @cached_property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""	) if is_vision_available() else None


 @slow
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	str			=						BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""	).to(a_	)

   _snake_case							:	Dict			=						self.default_image_processor
   _snake_case							:	Dict			=						prepare_img()
   _snake_case							:	List[str]			=						image_processor(images=a_,   return_tensors="""pt"""	).pixel_values.to(a_	)

   # prepare bool_masked_pos
   _snake_case							:	Optional[int]			=						torch.ones((1, 196),   dtype=torch.bool	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	int			=						model(pixel_values=a_,   bool_masked_pos=a_	)
   _snake_case							:	Dict			=						outputs.logits

   # verify the logits
   _snake_case							:	Optional[int]			=						torch.Size((1, 196, 8_192)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Optional[Any]			=						torch.tensor(
       [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	).to(a_	)

   self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3],   a_,   atol=1E-2	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""	).to(a_	)

   _snake_case							:	List[Any]			=						self.default_image_processor
   _snake_case							:	Any			=						prepare_img()
   _snake_case							:	Any			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	int			=						model(**a_	)
   _snake_case							:	Optional[int]			=						outputs.logits

   # verify the logits
   _snake_case							:	Tuple			=						torch.Size((1, 1_000)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Any			=						torch.tensor([-1.2_385, -1.0_987, -1.0_108]	).to(a_	)

   self.assertTrue(torch.allclose(logits[0, :3],   a_,   atol=1E-4	)	)

   _snake_case							:	str			=						281
   self.assertEqual(logits.argmax(-1	).item(),   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""	).to(
       a_	)

   _snake_case							:	int			=						self.default_image_processor
   _snake_case							:	Optional[Any]			=						prepare_img()
   _snake_case							:	Union[str, Any]			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Union[str, Any]			=						model(**a_	)
   _snake_case							:	Dict			=						outputs.logits

   # verify the logits
   _snake_case							:	Tuple			=						torch.Size((1, 21_841)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						torch.tensor([1.6_881, -0.2_787, 0.5_901]	).to(a_	)

   self.assertTrue(torch.allclose(logits[0, :3],   a_,   atol=1E-4	)	)

   _snake_case							:	List[str]			=						2_396
   self.assertEqual(logits.argmax(-1	).item(),   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""	)
   _snake_case							:	int			=						model.to(a_	)

   _snake_case							:	List[str]			=						BeitImageProcessor(do_resize=a_,   size=640,   do_center_crop=a_	)

   _snake_case							:	Optional[int]			=						load_dataset("""hf-internal-testing/fixtures_ade20k""",   split="""test"""	)
   _snake_case							:	Union[str, Any]			=						Image.open(ds[0]["""file"""]	)
   _snake_case							:	List[Any]			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)
   _snake_case							:	Union[str, Any]			=						outputs.logits

   # verify the logits
   _snake_case							:	List[str]			=						torch.Size((1, 150, 160, 160)	)
   self.assertEqual(logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						version.parse(PIL.__version__	) < version.parse("""9.0.0"""	)

   if is_pillow_less_than_a:
     _snake_case							:	Any			=						torch.tensor(
         [
             [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
             [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
             [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
         ],   device=a_,   )
   else:
     _snake_case							:	Optional[Any]			=						torch.tensor(
         [
             [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
             [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
             [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
         ],   device=a_,   )

   self.assertTrue(torch.allclose(logits[0, :3, :3, :3],   a_,   atol=1E-4	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""	)
   _snake_case							:	List[Any]			=						model.to(a_	)

   _snake_case							:	Tuple			=						BeitImageProcessor(do_resize=a_,   size=640,   do_center_crop=a_	)

   _snake_case							:	Union[str, Any]			=						load_dataset("""hf-internal-testing/fixtures_ade20k""",   split="""test"""	)
   _snake_case							:	str			=						Image.open(ds[0]["""file"""]	)
   _snake_case							:	Tuple			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)

   _snake_case							:	Union[str, Any]			=						outputs.logits.detach().cpu()

   _snake_case							:	Union[str, Any]			=						image_processor.post_process_semantic_segmentation(outputs=a_,   target_sizes=[(500, 300)]	)
   _snake_case							:	Optional[int]			=						torch.Size((500, 300)	)
   self.assertEqual(segmentation[0].shape,   a_	)

   _snake_case							:	List[str]			=						image_processor.post_process_semantic_segmentation(outputs=a_	)
   _snake_case							:	List[str]			=						torch.Size((160, 160)	)
   self.assertEqual(segmentation[0].shape,   a_	)


