





'''simple docstring'''


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       lowerCAmelCase_						(						unittest.TestCase						):




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

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




 def 							_snake_case      (		self						)			->							List[str]:
     _lowerCAmelCase			=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

     _lowerCAmelCase			=					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=lowercase_	,   initializer_range=self.initializer_range	,   )

     return config, pixel_values, labels




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							List[str]:
     _lowerCAmelCase			=					FlaxBeitModel(config=lowercase_						)
     _lowerCAmelCase			=					model(lowercase_						)
     self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Union[str, Any]:
     _lowerCAmelCase			=					FlaxBeitForMaskedImageModeling(config=lowercase_						)
     _lowerCAmelCase			=					model(lowercase_						)
     self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length - 1, self.vocab_size)						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Optional[int]:
     _lowerCAmelCase			=					self.type_sequence_label_size
     _lowerCAmelCase			=					FlaxBeitForImageClassification(config=lowercase_						)
     _lowerCAmelCase			=					model(lowercase_						)
     self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)						)

     # test greyscale images
     _lowerCAmelCase			=					1
     _lowerCAmelCase			=					FlaxBeitForImageClassification(lowercase_						)

     _lowerCAmelCase			=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
     _lowerCAmelCase			=					model(lowercase_						)





 def 							_snake_case      (		self						)			->							Union[str, Any]:
     _lowerCAmelCase			=					self.prepare_config_and_inputs()
     (
         _lowerCAmelCase
     )			=					config_and_inputs
     _lowerCAmelCase			=					{'''pixel_values''': pixel_values}
     return config, inputs_dict







@require_flax
class       lowerCAmelCase_						(						a_  ,unittest.TestCase						):
 __lowerCamelCase  :							str				     =    (
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )




 def 							_snake_case      (		self						)			->							Optional[int]:
     _lowerCAmelCase			=					FlaxBeitModelTester(self						)
     _lowerCAmelCase			=					ConfigTester(self	,   config_class=lowercase_	,   has_text_modality=lowercase_	,   hidden_size=37						)




 def 							_snake_case      (		self						)			->							str:
     self.config_tester.run_common_tests()




 def 							_snake_case      (		self						)			->							List[str]:
     _lowerCAmelCase			=					self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase			=					['''pixel_values''']
         self.assertListEqual(arg_names[:1]	,   lowercase_						)




 def 							_snake_case      (		self						)			->							List[Any]:
     _lowerCAmelCase			=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
             _lowerCAmelCase			=					self._prepare_for_class(lowercase_	,   lowercase_						)
             _lowerCAmelCase			=					model_class(lowercase_						)

             @jax.jit
             def model_jitted(_lowerCAmelCase	,   **_lowerCAmelCase						):
                 return model(pixel_values=lowercase_	,   **lowercase_						)

             with self.subTest("JIT Enabled"						):
                 _lowerCAmelCase			=					model_jitted(**lowercase_						).to_tuple()

             with self.subTest("JIT Disabled"						):
                 with jax.disable_jit():
                     _lowerCAmelCase			=					model_jitted(**lowercase_						).to_tuple()

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




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




 def 							_snake_case      (		self						)			->							int:
     _lowerCAmelCase			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_						)




 def 							_snake_case      (		self						)			->							Any:
     _lowerCAmelCase			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_						)





 @slow
 def 							_snake_case      (		self						)			->							Union[str, Any]:
     for model_class_name in self.all_model_classes:
         _lowerCAmelCase			=					model_class_name.from_pretrained("microsoft/beit-base-patch16-224"						)
         _lowerCAmelCase			=					model(np.ones((1, 3, 224, 224)						)						)
         self.assertIsNotNone(lowercase_						)







def 		__a():



    '''simple docstring'''





    _lowerCAmelCase			=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
    return image





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




 @cached_property
 def 							_snake_case      (		self						)			->							Optional[Any]:
     return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"						) if is_vision_available() else None




 @slow
 def 							_snake_case      (		self						)			->							Optional[int]:
     _lowerCAmelCase			=					FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"						)

     _lowerCAmelCase			=					self.default_image_processor
     _lowerCAmelCase			=					prepare_img()
     _lowerCAmelCase			=					image_processor(images=lowercase_	,   return_tensors="np"						).pixel_values

     # prepare bool_masked_pos
     _lowerCAmelCase			=					np.ones((1, 196)	,   dtype=lowercase_						)

     # forward pass
     _lowerCAmelCase			=					model(pixel_values=lowercase_	,   bool_masked_pos=lowercase_						)
     _lowerCAmelCase			=					outputs.logits

     # verify the logits
     _lowerCAmelCase			=					(1, 196, 8192)
     self.assertEqual(logits.shape	,   lowercase_						)

     _lowerCAmelCase			=					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]	,   lowercase_	,   atol=1E-2						)						)




 @slow
 def 							_snake_case      (		self						)			->							Union[str, Any]:
     _lowerCAmelCase			=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"						)

     _lowerCAmelCase			=					self.default_image_processor
     _lowerCAmelCase			=					prepare_img()
     _lowerCAmelCase			=					image_processor(images=lowercase_	,   return_tensors="np"						)

     # forward pass
     _lowerCAmelCase			=					model(**lowercase_						)
     _lowerCAmelCase			=					outputs.logits

     # verify the logits
     _lowerCAmelCase			=					(1, 1000)
     self.assertEqual(logits.shape	,   lowercase_						)

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

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

     _lowerCAmelCase			=					281
     self.assertEqual(logits.argmax(-1						).item()	,   lowercase_						)





 @slow
 def 							_snake_case      (		self						)			->							Optional[int]:
     _lowerCAmelCase			=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"						)

     _lowerCAmelCase			=					self.default_image_processor
     _lowerCAmelCase			=					prepare_img()
     _lowerCAmelCase			=					image_processor(images=lowercase_	,   return_tensors="np"						)

     # forward pass
     _lowerCAmelCase			=					model(**lowercase_						)
     _lowerCAmelCase			=					outputs.logits

     # verify the logits
     _lowerCAmelCase			=					(1, 21841)
     self.assertEqual(logits.shape	,   lowercase_						)

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

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

     _lowerCAmelCase			=					2396
     self.assertEqual(logits.argmax(-1						).item()	,   lowercase_						)





"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







					def __len__(       self			:				Any		):
						return len(self.dataset		)







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Tuple=None		):
						lowerCAmelCase__	:						List[Any]  				=	loader
						lowerCAmelCase__	:						int  				=	infer
						lowerCAmelCase__	:						List[str]  				=	params
						if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							lowerCAmelCase__	:						int  				=	None
						lowerCAmelCase__	:						Dict  				=	loader_batch_size

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				Tuple		):
						if isinstance(self._loader_batch_data   ,torch.Tensor		):
							# Batch data is simple tensor, just fetch the slice
							lowerCAmelCase__	:						Tuple  				=	self._loader_batch_data[self._loader_batch_index]
						else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							lowerCAmelCase__	:						int  				=	{}
							for k, element in self._loader_batch_data.items():
								if isinstance(lowercase_   ,lowercase_		):
									# Convert ModelOutput to tuple first
									lowerCAmelCase__	:						List[Any]  				=	element.to_tuple()
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						List[Any]  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						str  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase_   ,lowercase_		):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						Dict  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						Optional[int]  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if element is None:
									# This can happen for optional data that get passed around
									lowerCAmelCase__	:						Dict  				=	None
								elif isinstance(element[self._loader_batch_index]   ,torch.Tensor		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									lowerCAmelCase__	:						str  				=	element[self._loader_batch_index].unsqueeze(0		)
								elif isinstance(element[self._loader_batch_index]   ,np.ndarray		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									lowerCAmelCase__	:						Tuple  				=	np.expand_dims(element[self._loader_batch_index]   ,0		)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase__	:						int  				=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							lowerCAmelCase__	:						int  				=	self._loader_batch_data.__class__(lowercase_		)
						self._loader_batch_index += 1
						return result







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

						# We're out of items within a batch
						lowerCAmelCase__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**self.params		)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							else:
								lowerCAmelCase__	:						List[str]  				=	first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								lowerCAmelCase__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	next(self.subiterator		)
						except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							lowerCAmelCase__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# Extremely similar to PipelineIterator in its unpacking mechanism
						# BUT, we have an extra required item which is the presence of `is_last`
						# That is because everything is flattened by `PipelineChunkIterator` we
						# need to keep track of how to regroup here in the original `process`
						# boundaries so that `process` and `postprocess` see the same data.

						# This iterator accumulates items (possibly while unbatching) until it
						# its a `is_last` and then just passes it on to the caller.
						lowerCAmelCase__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
								lowerCAmelCase__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

						while not is_last:
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							if self.loader_batch_size is not None:
								if isinstance(lowercase_   ,torch.Tensor		):
									lowerCAmelCase__	:						Tuple  				=	processed
								else:
									lowerCAmelCase__	:						List[Any]  				=	list(processed.keys()		)[0]
									lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Tuple  				=	len(lowercase_		)
								else:
									lowerCAmelCase__	:						str  				=	first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									lowerCAmelCase__	:						Optional[int]  				=	observed_batch_size
								lowerCAmelCase__	:						Optional[int]  				=	processed
								lowerCAmelCase__	:						Optional[int]  				=	0
								while self._loader_batch_index < self.loader_batch_size:
									lowerCAmelCase__	:						Any  				=	self.loader_batch_item()
									lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
									accumulator.append(lowercase_		)
									if is_last:
										return accumulator
							else:
								lowerCAmelCase__	:						Dict  				=	processed
								lowerCAmelCase__	:						Tuple  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
						return accumulator







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







					def __len__(       self			:				List[Any]		):
						return len(self.dataset		)







					def __getitem__(       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return self.dataset[i][self.key]







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







					def __len__(       self			:				str		):
						return len(self.dataset		)







					def __getitem__(       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]		):
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




