


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       lowercase_					(unittest.TestCase   ):





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

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





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											a__      =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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> List[Any]:
											a__      =FlaxBeitModel(config=lowercase_)
											a__      =model(lowercase_)
											self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size))





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Any:
											a__      =FlaxBeitForMaskedImageModeling(config=lowercase_)
											a__      =model(lowercase_)
											self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length - 1, self.vocab_size))





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> List[str]:
											a__      =self.type_sequence_label_size
											a__      =FlaxBeitForImageClassification(config=lowercase_)
											a__      =model(lowercase_)
											self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size))

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

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





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.prepare_config_and_inputs()
											(
											    (
											    a__
											)    ,     (
											    a__
											)    ,     (
											    a__
											)    ,     
											)      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(
				    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
				)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =FlaxBeitModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_       ,  hidden_size=37)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											self.config_tester.run_common_tests()





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

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

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

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

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

																									with self.subTest('JIT Enabled'):
																																a__      =model_jitted(**lowercase_).to_tuple()

																									with self.subTest('JIT Disabled'):
																																with jax.disable_jit():
																																							a__      =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 						__UpperCamelCase    (				self)  -> int:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowercase_)





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







def   _lowercase(					):
							a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
							return image







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





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





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

											a__      =self.default_image_processor
											a__      =prepare_img()
											a__      =image_processor(images=lowercase_       ,  return_tensors='np').pixel_values

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

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

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

											a__      =np.array(
											    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]])

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





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

											a__      =self.default_image_processor
											a__      =prepare_img()
											a__      =image_processor(images=lowercase_       ,  return_tensors='np')

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

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

											a__      =np.array([-1.23_85, -1.09_87, -1.01_08])

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

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





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

											a__      =self.default_image_processor
											a__      =prepare_img()
											a__      =image_processor(images=lowercase_       ,  return_tensors='np')

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

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

											a__      =np.array([1.68_81, -0.27_87, 0.59_01])

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

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





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

