




"""simple docstring"""






import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402






class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    0
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'  )
							self.assertIsInstance(_a  ,      _a  )
			def 					A_    (  self  ):
							with tempfile.TemporaryDirectory() as tmpdirname:
											_lowerCamelCase     :			Tuple    =    Path(_a  ) / "preprocessor_config.json"
											_lowerCamelCase     :			str    =    Path(_a  ) / "config.json"
											json.dump(
											    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}  ,      open(_a  ,      'w'  )  ,      )
											json.dump({'model_type': 'clip'}  ,      open(_a  ,      'w'  )  )

											_lowerCamelCase     :			List[str]    =    AutoImageProcessor.from_pretrained(_a  )
											self.assertIsInstance(_a  ,      _a  )
			def 					A_    (  self  ):
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
											_lowerCamelCase     :			Dict    =    Path(_a  ) / "preprocessor_config.json"
											_lowerCamelCase     :			List[str]    =    Path(_a  ) / "config.json"
											json.dump(
											    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}  ,      open(_a  ,      'w'  )  ,      )
											json.dump({'model_type': 'clip'}  ,      open(_a  ,      'w'  )  )

											_lowerCamelCase     :			List[Any]    =    AutoImageProcessor.from_pretrained(_a  )
											self.assertIsInstance(_a  ,      _a  )
			def 					A_    (  self  ):
							with tempfile.TemporaryDirectory() as tmpdirname:
											_lowerCamelCase     :			List[str]    =    CLIPConfig()

											# Create a dummy config file with image_proceesor_type
											_lowerCamelCase     :			Tuple    =    Path(_a  ) / "preprocessor_config.json"
											_lowerCamelCase     :			int    =    Path(_a  ) / "config.json"
											json.dump(
											    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}  ,      open(_a  ,      'w'  )  ,      )
											json.dump({'model_type': 'clip'}  ,      open(_a  ,      'w'  )  )

											# remove image_processor_type to make sure config.json alone is enough to load image processor locally
											_lowerCamelCase     :			Tuple    =    AutoImageProcessor.from_pretrained(_a  ).to_dict()

											config_dict.pop('image_processor_type'  )
											_lowerCamelCase     :			List[Any]    =    CLIPImageProcessor(**_a  )

											# save in new folder
											model_config.save_pretrained(_a  )
											config.save_pretrained(_a  )

											_lowerCamelCase     :			Tuple    =    AutoImageProcessor.from_pretrained(_a  )

											# make sure private variable is not incorrectly saved
											_lowerCamelCase     :			int    =    json.loads(config.to_json_string()  )
											self.assertTrue('_processor_class' not in dict_as_saved  )

							self.assertIsInstance(_a  ,      _a  )
			def 					A_    (  self  ):
							with tempfile.TemporaryDirectory() as tmpdirname:
											_lowerCamelCase     :			Optional[int]    =    Path(_a  ) / "preprocessor_config.json"
											json.dump(
											    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}  ,      open(_a  ,      'w'  )  ,      )

											_lowerCamelCase     :			Optional[int]    =    AutoImageProcessor.from_pretrained(_a  )
											self.assertIsInstance(_a  ,      _a  )
			def 					A_    (  self  ):
							with self.assertRaisesRegex(
							    _a  ,      'clip-base is not a local folder and is not a valid model identifier'  ):
											_lowerCamelCase     :			Union[str, Any]    =    AutoImageProcessor.from_pretrained('clip-base'  )
			def 					A_    (  self  ):
							with self.assertRaisesRegex(
							    _a  ,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'  ):
											_lowerCamelCase     :			List[Any]    =    AutoImageProcessor.from_pretrained(_a  ,      revision='aaaaaa'  )
			def 					A_    (  self  ):
							with self.assertRaisesRegex(
							    _a  ,      'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'  ,      ):
											_lowerCamelCase     :			str    =    AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'  )
			def 					A_    (  self  ):
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(_a  ):
											_lowerCamelCase     :			List[str]    =    AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'  )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(_a  ):
											_lowerCamelCase     :			Union[str, Any]    =    AutoImageProcessor.from_pretrained(
											    'hf-internal-testing/test_dynamic_image_processor'  ,      trust_remote_code=_a  )

							_lowerCamelCase     :			Any    =    AutoImageProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_image_processor'  ,      trust_remote_code=_a  )
							self.assertEqual(image_processor.__class__.__name__  ,      'NewImageProcessor'  )

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(_a  )
											_lowerCamelCase     :			List[str]    =    AutoImageProcessor.from_pretrained(_a  ,      trust_remote_code=_a  )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,      'NewImageProcessor'  )
			def 					A_    (  self  ):
							try:
											AutoConfig.register('custom'  ,      _a  )
											AutoImageProcessor.register(_a  ,      _a  )
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(_a  ):
															AutoImageProcessor.register(_a  ,      _a  )

											with tempfile.TemporaryDirectory() as tmpdirname:
															_lowerCamelCase     :			Dict    =    Path(_a  ) / "preprocessor_config.json"
															_lowerCamelCase     :			Union[str, Any]    =    Path(_a  ) / "config.json"
															json.dump(
															    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}  ,      open(_a  ,      'w'  )  ,      )
															json.dump({'model_type': 'clip'}  ,      open(_a  ,      'w'  )  )

															_lowerCamelCase     :			Dict    =    CustomImageProcessor.from_pretrained(_a  )

											# Now that the config is registered, it can be used as any other config with the auto-API
											with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(_a  )
															_lowerCamelCase     :			Any    =    AutoImageProcessor.from_pretrained(_a  )
															self.assertIsInstance(_a  ,      _a  )

							finally:
											if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
															del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






			def 					A_    (  self  ):






							class lowerCAmelCase__	(     lowercase						):






										'''simple docstring'''



										lowerCamelCase__      =			True

							try:
											AutoConfig.register('custom'  ,      _a  )
											AutoImageProcessor.register(_a  ,      _a  )
											# If remote code is not set, the default is to use local
											_lowerCamelCase     :			Optional[int]    =    AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'  )
											self.assertEqual(image_processor.__class__.__name__  ,      'NewImageProcessor'  )
											self.assertTrue(image_processor.is_local  )

											# If remote code is disabled, we load the local one.
											_lowerCamelCase     :			Optional[int]    =    AutoImageProcessor.from_pretrained(
											    'hf-internal-testing/test_dynamic_image_processor'  ,      trust_remote_code=_a  )
											self.assertEqual(image_processor.__class__.__name__  ,      'NewImageProcessor'  )
											self.assertTrue(image_processor.is_local  )

											# If remote is enabled, we load from the Hub
											_lowerCamelCase     :			List[Any]    =    AutoImageProcessor.from_pretrained(
											    'hf-internal-testing/test_dynamic_image_processor'  ,      trust_remote_code=_a  )
											self.assertEqual(image_processor.__class__.__name__  ,      'NewImageProcessor'  )
											self.assertTrue(not hasattr(_a  ,      'is_local'  )  )

							finally:
											if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
															del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)





class       _snake_case					(     unittest.TestCase				):







			def __init__(				self						,   _a						,   _a=13						,   _a=7						,   _a=True						,   _a=True						,   _a=True						,   _a=True						,   _a=99						,   _a=32						,   _a=5						,   _a=4						,   _a=37						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=16						,   _a=2						,   _a=0.02						,   _a=4						,   ):
				__magic_name__		:		List[Any]	   =     parent
				__magic_name__		:		Optional[Any]	   =     batch_size
				__magic_name__		:		Dict	   =     seq_length
				__magic_name__		:		Union[str, Any]	   =     is_training
				__magic_name__		:		Optional[Any]	   =     use_attention_mask
				__magic_name__		:		Optional[Any]	   =     use_token_type_ids
				__magic_name__		:		int	   =     use_labels
				__magic_name__		:		List[Any]	   =     vocab_size
				__magic_name__		:		Union[str, Any]	   =     hidden_size
				__magic_name__		:		Optional[Any]	   =     num_hidden_layers
				__magic_name__		:		int	   =     num_attention_heads
				__magic_name__		:		Any	   =     intermediate_size
				__magic_name__		:		List[Any]	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		List[Any]	   =     max_position_embeddings
				__magic_name__		:		Tuple	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     type_sequence_label_size
				__magic_name__		:		Dict	   =     initializer_range
				__magic_name__		:		List[Any]	   =     num_choices







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

				__magic_name__		:		List[Any]	   =     None
				if self.use_attention_mask:
					__magic_name__		:		List[str]	   =     random_attention_mask([self.batch_size, self.seq_length]		)

				__magic_name__		:		str	   =     None
				if self.use_token_type_ids:
					__magic_name__		:		str	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size		)

				__magic_name__		:		List[str]	   =     RobertaPreLayerNormConfig(
				    vocab_size=self.vocab_size						,   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						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   is_decoder=_a						,   initializer_range=self.initializer_range						,   )

				return config, input_ids, token_type_ids, attention_mask







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[Any]	   =     config_and_inputs
				__magic_name__		:		List[str]	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     config_and_inputs

				__magic_name__		:		Tuple	   =     True
				__magic_name__		:		int	   =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				__magic_name__		:		Optional[Any]	   =     ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    True

			UpperCamelCase__          =    (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     FlaxRobertaPreLayerNormModelTester(self		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				for model_class_name in self.all_model_classes:
					__magic_name__		:		Optional[Any]	   =     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)
					__magic_name__		:		Dict	   =     model(np.ones((1, 1)		)		)
					self.assertIsNotNone(_a		)







@require_flax
class       _snake_case					(     unittest.TestCase				):







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Union[str, Any]	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		List[str]	   =     model(_a		)[0]
				__magic_name__		:		str	   =     [1, 11, 50_265]
				self.assertEqual(list(output.shape		)						,   _a		)
				# compare the actual values for a slice.
				__magic_name__		:		List[str]	   =     np.array(
				    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Tuple	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		Tuple	   =     model(_a		)[0]
				# compare the actual values for a slice.
				__magic_name__		:		Dict	   =     np.array(
				    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)
