






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       __UpperCAmelCase		(unittest.TestCase    ):







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  0







			def 							UpperCamelCase      (	self:  Union[str, Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
									self.assertIsInstance(UpperCAmelCase_      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """preprocessor_config.json"""
															_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """config.json"""
															json.dump(
															    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )      ,	)
															json.dump({"""model_type""": """clip"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )     )

															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_     )
															self.assertIsInstance(UpperCAmelCase_      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """preprocessor_config.json"""
															_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """config.json"""
															json.dump(
															    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )      ,	)
															json.dump({"""model_type""": """clip"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )     )

															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_     )
															self.assertIsInstance(UpperCAmelCase_      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE	      =  CLIPConfig()

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

															# remove image_processor_type to make sure config.json alone is enough to load image processor locally
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_     ).to_dict()

															config_dict.pop("""image_processor_type"""     )
															_SCREAMING_SNAKE_CASE	      =  CLIPImageProcessor(**UpperCAmelCase_     )

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

															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_     )

															# make sure private variable is not incorrectly saved
															_SCREAMING_SNAKE_CASE	      =  json.loads(config.to_json_string()     )
															self.assertTrue("""_processor_class""" not in dict_as_saved     )

									self.assertIsInstance(UpperCAmelCase_      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """preprocessor_config.json"""
															json.dump(
															    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )      ,	)

															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_     )
															self.assertIsInstance(UpperCAmelCase_      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									with self.assertRaisesRegex(
									    UpperCAmelCase_      ,	"""clip-base is not a local folder and is not a valid model identifier"""     ):
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained("""clip-base"""     )







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									with self.assertRaisesRegex(
									    UpperCAmelCase_      ,	R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_      ,	revision="""aaaaaa"""     )







			def 							UpperCamelCase      (	self:  List[Any]     ):







									'''simple docstring'''




									with self.assertRaisesRegex(
									    UpperCAmelCase_      ,	"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""      ,	):
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									with self.assertRaises(UpperCAmelCase_     ):
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(UpperCAmelCase_     ):
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_image_processor"""      ,	trust_remote_code=UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""      ,	trust_remote_code=UpperCAmelCase_     )
									self.assertEqual(image_processor.__class__.__name__      ,	"""NewImageProcessor"""     )

									# Test image processor can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_      ,	trust_remote_code=UpperCAmelCase_     )
									self.assertEqual(reloaded_image_processor.__class__.__name__      ,	"""NewImageProcessor"""     )







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									try:
															AutoConfig.register("""custom"""      ,	UpperCAmelCase_     )
															AutoImageProcessor.register(UpperCAmelCase_      ,	UpperCAmelCase_     )
															# Trying to register something existing in the Transformers library will raise an error
															with self.assertRaises(UpperCAmelCase_     ):
																					AutoImageProcessor.register(UpperCAmelCase_      ,	UpperCAmelCase_     )

															with tempfile.TemporaryDirectory() as tmpdirname:
																					_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """preprocessor_config.json"""
																					_SCREAMING_SNAKE_CASE	      =  Path(UpperCAmelCase_     ) / """config.json"""
																					json.dump(
																					    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )      ,	)
																					json.dump({"""model_type""": """clip"""}      ,	open(UpperCAmelCase_      ,	"""w"""     )     )

																					_SCREAMING_SNAKE_CASE	      =  CustomImageProcessor.from_pretrained(UpperCAmelCase_     )

															# 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(UpperCAmelCase_     )
																					_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(UpperCAmelCase_     )
																					self.assertIsInstance(UpperCAmelCase_      ,	UpperCAmelCase_     )

									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 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									class       __UpperCAmelCase		(__lowerCamelCase    ):
												__snake_case				:      Tuple 						=							True

									try:
															AutoConfig.register("""custom"""      ,	UpperCAmelCase_     )
															AutoImageProcessor.register(UpperCAmelCase_      ,	UpperCAmelCase_     )
															# If remote code is not set, the default is to use local
															_SCREAMING_SNAKE_CASE	      =  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.
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_image_processor"""      ,	trust_remote_code=UpperCAmelCase_     )
															self.assertEqual(image_processor.__class__.__name__      ,	"""NewImageProcessor"""     )
															self.assertTrue(image_processor.is_local     )

															# If remote is enabled, we load from the Hub
															_SCREAMING_SNAKE_CASE	      =  AutoImageProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_image_processor"""      ,	trust_remote_code=UpperCAmelCase_     )
															self.assertEqual(image_processor.__class__.__name__      ,	"""NewImageProcessor"""     )
															self.assertTrue(not hasattr(UpperCAmelCase_      ,	"""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 gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp







@slow
@require_flax
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Dict							,				A :    Any		):
         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A		) for s in shape]		)}.npy'''




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[int]=0							,				A :    Tuple=(4, 4, 64, 64)							,				A :    Tuple=False		):
         __snake_case:						Dict   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						str   =						jnp.array(load_hf_numpy(self.get_file_format(A							,				A		)		)							,				dtype=A		)
         return image




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Any=False							,				A :    Optional[Any]="CompVis/stable-diffusion-v1-4"		):
         __snake_case:						List[Any]   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Union[str, Any]   =						"""bf16""" if fpaa else None

         __snake_case  ,   __snake_case:						Optional[int]   =						FlaxUNetaDConditionModel.from_pretrained(
             A							,				subfolder="""unet"""							,				dtype=A							,				revision=A		)
         return model, params




       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    Tuple=0							,				A :    str=(4, 77, 768)							,				A :    List[str]=False		):
         __snake_case:						Any   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Dict   =						jnp.array(load_hf_numpy(self.get_file_format(A							,				A		)		)							,				dtype=A		)
         return hidden_states




       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
               [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
               [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
               [3, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
               # fmt: on
           ]		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[Any]							,				A :    str							,				A :    Any		):
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=A		)
         __snake_case:						Tuple   =						self.get_latents(A							,				fpaa=A		)
         __snake_case:						int   =						self.get_encoder_hidden_states(A							,				fpaa=A		)

         __snake_case:						List[Any]   =						model.apply(
             {"""params""": params}							,				A							,				jnp.array(A							,				dtype=jnp.intaa		)							,				encoder_hidden_states=A							,				).sample

         assert sample.shape == latents.shape

         __snake_case:						str   =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,				dtype=jnp.floataa		)
         __snake_case:						Optional[int]   =						jnp.array(A							,				dtype=jnp.floataa		)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(A							,				A							,				atol=1E-2		)






       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
               [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
               [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
               [3, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
               # fmt: on
           ]		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    int							,				A :    Tuple							,				A :    List[str]		):
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=A		)
         __snake_case:						Optional[int]   =						self.get_latents(A							,				shape=(4, 4, 96, 96)							,				fpaa=A		)
         __snake_case:						str   =						self.get_encoder_hidden_states(A							,				shape=(4, 77, 1_024)							,				fpaa=A		)

         __snake_case:						str   =						model.apply(
             {"""params""": params}							,				A							,				jnp.array(A							,				dtype=jnp.intaa		)							,				encoder_hidden_states=A							,				).sample

         assert sample.shape == latents.shape

         __snake_case:						Dict   =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,				dtype=jnp.floataa		)
         __snake_case:						Any   =						jnp.array(A							,				dtype=jnp.floataa		)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(A							,				A							,				atol=1E-2		)






