






'''simple docstring'''







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






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

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							a_		:  Tuple											=      (image_size // patch_size) ** 2
							a_		:  Optional[Any]											=      num_patches + 1






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Union[str, Any]:
							a_		:  List[str]											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

							a_		:  Any											=      ViTConfig(
							    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=__SCREAMING_SNAKE_CASE  ,						initializer_range=self.initializer_range  ,						)

							return config, pixel_values






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict					)			->			Optional[Any]:
							a_		:  Tuple											=      FlaxViTModel(config=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      model(__SCREAMING_SNAKE_CASE					)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							a_		:  str											=      (self.image_size, self.image_size)
							a_		:  Union[str, Any]											=      (self.patch_size, self.patch_size)
							a_		:  int											=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, num_patches + 1, self.hidden_size)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Union[str, Any]:
							a_		:  Dict											=      self.type_sequence_label_size
							a_		:  Tuple											=      FlaxViTForImageClassification(config=__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      model(__SCREAMING_SNAKE_CASE					)
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)					)

							# test greyscale images
							a_		:  int											=      1
							a_		:  Optional[Any]											=      FlaxViTForImageClassification(__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
							a_		:  Optional[int]											=      model(__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			int:
							a_		:  str											=      self.prepare_config_and_inputs()
							(
							    a_			
							)		:  Tuple											=      config_and_inputs
							a_		:  Dict											=      {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_flax
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			None:
							a_		:  List[Any]											=      FlaxViTModelTester(self					)
							a_		:  List[str]											=      ConfigTester(self  ,						config_class=__SCREAMING_SNAKE_CASE  ,						has_text_modality=__SCREAMING_SNAKE_CASE  ,						hidden_size=37					)






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Any:
							a_		:  int											=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			List[Any]:
							a_		:  Any											=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							a_		:  List[Any]											=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:  Dict											=      model_class(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      inspect.signature(model.__call__					)
											# 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]  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Tuple:
							a_		:  Tuple											=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
															a_		:  str											=      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
															a_		:  int											=      model_class(__SCREAMING_SNAKE_CASE					)

															@jax.jit
															def model_jitted(__SCREAMING_SNAKE_CASE      :			Tuple  ,						**__SCREAMING_SNAKE_CASE      :			int					):
																			return model(pixel_values=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

															with self.subTest('''JIT Enabled'''					):
																			a_		:  int											=      model_jitted(**__SCREAMING_SNAKE_CASE					).to_tuple()

															with self.subTest('''JIT Disabled'''					):
																			with jax.disable_jit():
																							a_		:  Optional[int]											=      model_jitted(**__SCREAMING_SNAKE_CASE					).to_tuple()

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







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			List[str]:
							for model_class_name in self.all_model_classes:
											a_		:  Union[str, Any]											=      model_class_name.from_pretrained('''google/vit-base-patch16-224'''					)
											a_		:  Optional[int]											=      model(np.ones((1, 3, 224, 224)					)					)
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




