

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor






class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any=100			,     __lowerCamelCase		:	Optional[Any]=13			,     __lowerCamelCase		:	Tuple=30			,     __lowerCamelCase		:	Optional[Any]=2			,     __lowerCamelCase		:	Dict=3			,     __lowerCamelCase		:	Optional[int]=True			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	int=32			,     __lowerCamelCase		:	List[Any]=5			,     __lowerCamelCase		:	Any=4			,     __lowerCamelCase		:	int=37			,     __lowerCamelCase		:	Dict="gelu"			,     __lowerCamelCase		:	str=0.1			,     __lowerCamelCase		:	Dict=0.1			,     __lowerCamelCase		:	Tuple=10			,     __lowerCamelCase		:	Optional[Any]=0.02			,     __lowerCamelCase		:	List[str]=3			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					patch_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					initializer_range

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






  def    lowercase_	(							self		:	int      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

       SCREAMING_SNAKE_CASE__					=					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=__lowerCamelCase			,     initializer_range=self.initializer_range			,     )

       return config, pixel_values, labels






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	List[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitModel(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[Any]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForMaskedImageModeling(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length - 1, self.vocab_size)      )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       SCREAMING_SNAKE_CASE__					=					1
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )







  def    lowercase_	(							self		:	Dict      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),
       )					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_flax
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )






  def    lowercase_	(							self		:	Dict      )	->							None:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase			,     hidden_size=37      )






  def    lowercase_	(							self		:	Any      )	->							List[str]:
       self.config_tester.run_common_tests()






  def    lowercase_	(							self		:	Optional[int]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					inspect.signature(model.__call__      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            SCREAMING_SNAKE_CASE__					=					[*signature.parameters.keys()]

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__      ):
                 SCREAMING_SNAKE_CASE__					=					self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )
                 SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )

                 @jax.jit
                 def model_jitted(__lowerCamelCase		:	str			,     **__lowerCamelCase		:	Dict      ):
                      return model(pixel_values=__lowerCamelCase			,     **__lowerCamelCase      )

                 with self.subTest('''JIT Enabled'''      ):
                      SCREAMING_SNAKE_CASE__					=					model_jitted(**__lowerCamelCase      ).to_tuple()

                 with self.subTest('''JIT Disabled'''      ):
                      with jax.disable_jit():
                           SCREAMING_SNAKE_CASE__					=					model_jitted(**__lowerCamelCase      ).to_tuple()

                 self.assertEqual(len(__lowerCamelCase      )			,     len(__lowerCamelCase      )      )
                 for jitted_output, output in zip(__lowerCamelCase			,     __lowerCamelCase      ):
                      self.assertEqual(jitted_output.shape			,     output.shape      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[int]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	List[Any]      )	->							Any:
       for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''      )
            SCREAMING_SNAKE_CASE__					=					model(np.ones((1, 3, 224, 224)      )      )
            self.assertIsNotNone(__lowerCamelCase      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image







@require_vision
@require_flax
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @cached_property
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None






  @slow
  def    lowercase_	(							self		:	Dict      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''np'''      ).pixel_values

       # prepare bool_masked_pos
       SCREAMING_SNAKE_CASE__					=					np.ones((1, 196)			,     dtype=__lowerCamelCase      )

       # forward pass
       SCREAMING_SNAKE_CASE__					=					model(pixel_values=__lowerCamelCase			,     bool_masked_pos=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					(1, 196, 8192)
       self.assertEqual(logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					np.array(
           [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]      )

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]			,     __lowerCamelCase			,     atol=1e-2      )      )






  @slow
  def    lowercase_	(							self		:	List[str]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''np'''      )

       # forward pass
       SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					(1, 1000)
       self.assertEqual(logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					np.array([-1.2385, -1.0987, -1.0108]      )

       self.assertTrue(np.allclose(logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )

       SCREAMING_SNAKE_CASE__					=					281
       self.assertEqual(logits.argmax(-1      ).item()			,     __lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''np'''      )

       # forward pass
       SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					(1, 2_1841)
       self.assertEqual(logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					np.array([1.6881, -0.2787, 0.5901]      )

       self.assertTrue(np.allclose(logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )

       SCREAMING_SNAKE_CASE__					=					2396
       self.assertEqual(logits.argmax(-1      ).item()			,     __lowerCamelCase      )





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       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__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]      )






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset			,     14      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					super().generate_dummy_inputs(
           preprocessor=__lowerCamelCase			,     batch_size=__lowerCamelCase			,     seq_length=__lowerCamelCase			,     is_pair=__lowerCamelCase			,     framework=__lowerCamelCase      )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


