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

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCAmelCase_           =				get_tests_dir('''fixtures/dummy-config.json''')




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			0



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    self.assertIsNotNone(transformers.models.auto.__spec__				)
    self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto'''				)				)



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



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			AutoConfig.from_pretrained('''bert-base-uncased'''				)
    self.assertIsInstance(_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			AutoConfig.from_pretrained(_lowercase				)
    self.assertIsInstance(_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			AutoConfig.from_pretrained(_lowercase				)
    self.assertIsInstance(_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			AutoConfig.for_model('''roberta'''				)
    self.assertIsInstance(_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    with tempfile.TemporaryDirectory() as tmp_dir:
     # This model name contains bert and roberta, but roberta ends up being picked.
     _UpperCamelCase:  Union[str, Any]    						=			os.path.join(_lowercase		,	'''fake-roberta'''				)
     os.makedirs(_lowercase		,	exist_ok=_lowercase				)
     with open(os.path.join(_lowercase		,	'''config.json'''				)		,	'''w'''				) as f:
      f.write(json.dumps({}				)				)
     _UpperCamelCase:  Union[str, Any]    						=			AutoConfig.from_pretrained(_lowercase				)
     self.assertEqual(type(_lowercase				)		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    try:
     AutoConfig.register('''custom'''		,	_lowercase				)
     # Wrong model type will raise an error
     with self.assertRaises(_lowercase				):
      AutoConfig.register('''model'''		,	_lowercase				)
     # Trying to register something existing in the Transformers library will raise an error
     with self.assertRaises(_lowercase				):
      AutoConfig.register('''bert'''		,	_lowercase				)

     # Now that the config is registered, it can be used as any other config with the auto-API
     _UpperCamelCase:  Union[str, Any]    						=			CustomConfig()
     with tempfile.TemporaryDirectory() as tmp_dir:
      config.save_pretrained(_lowercase				)
      _UpperCamelCase:  Any    						=			AutoConfig.from_pretrained(_lowercase				)
      self.assertIsInstance(_lowercase		,	_lowercase				)

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



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    with self.assertRaisesRegex(
        _lowercase		,	'''bert-base is not a local folder and is not a valid model identifier'''				):
     _UpperCamelCase:  Optional[int]    						=			AutoConfig.from_pretrained('''bert-base'''				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    with self.assertRaisesRegex(
        _lowercase		,	R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''				):
     _UpperCamelCase:  str    						=			AutoConfig.from_pretrained(_lowercase		,	revision='''aaaaaa'''				)



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



    """simple docstring"""



    with self.assertRaisesRegex(
        _lowercase		,	'''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.'''		,	):
     _UpperCamelCase:  int    						=			AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo'''				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    with self.assertRaises(_lowercase				):
     _UpperCamelCase:  List[str]    						=			AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''				)
    # If remote code is disabled, we can't load this config.
    with self.assertRaises(_lowercase				):
     _UpperCamelCase:  Optional[int]    						=			AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''		,	trust_remote_code=_lowercase				)

    _UpperCamelCase:  Union[str, Any]    						=			AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''		,	trust_remote_code=_lowercase				)
    self.assertEqual(config.__class__.__name__		,	'''NewModelConfig'''				)

    # Test config can be reloaded.
    with tempfile.TemporaryDirectory() as tmp_dir:
     config.save_pretrained(_lowercase				)
     _UpperCamelCase:  str    						=			AutoConfig.from_pretrained(_lowercase		,	trust_remote_code=_lowercase				)
    self.assertEqual(reloaded_config.__class__.__name__		,	'''NewModelConfig'''				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    class 			__magic_name__	(		__a     ):





       """simple docstring"""




       lowerCAmelCase   :     List[Any]	      = '''new-model'''

    try:
     AutoConfig.register('''new-model'''		,	_lowercase				)
     # If remote code is not set, the default is to use local
     _UpperCamelCase:  List[Any]    						=			AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''				)
     self.assertEqual(config.__class__.__name__		,	'''NewModelConfigLocal'''				)

     # If remote code is disabled, we load the local one.
     _UpperCamelCase:  Union[str, Any]    						=			AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''		,	trust_remote_code=_lowercase				)
     self.assertEqual(config.__class__.__name__		,	'''NewModelConfigLocal'''				)

     # If remote is enabled, we load from the Hub
     _UpperCamelCase:  Any    						=			AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''		,	trust_remote_code=_lowercase				)
     self.assertEqual(config.__class__.__name__		,	'''NewModelConfig'''				)

    finally:
     if "new-model" in CONFIG_MAPPING._extra_content:
      del CONFIG_MAPPING._extra_content["new-model"]
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = StableDiffusionSAGPipeline
   lowerCAmelCase   :     List[Any]	      = TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase   :     List[str]	      = TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase   :     str	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = False



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			UNetaDConditionModel(
        block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=4		,	out_channels=4		,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,	cross_attention_dim=32		,	)
    _UpperCamelCase:  List[Any]    						=			DDIMScheduler(
        beta_start=0.00085		,	beta_end=0.012		,	beta_schedule='''scaled_linear'''		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[int]    						=			AutoencoderKL(
        block_out_channels=[32, 64]		,	in_channels=3		,	out_channels=3		,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,	latent_channels=4		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  int    						=			CLIPTextConfig(
        bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1E-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1_000		,	)
    _UpperCamelCase:  Any    						=			CLIPTextModel(_lowercase				)
    _UpperCamelCase:  List[str]    						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    _UpperCamelCase:  int    						=			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[Any]		,	_lowercase       :	int=0				):



    """simple docstring"""



    if str(_lowercase				).startswith('''mps'''				):
     _UpperCamelCase:  Tuple    						=			torch.manual_seed(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
    _UpperCamelCase:  Dict    						=			{
        '''prompt''': '''.''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 1.0,
        '''sag_scale''': 1.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




@slow
@require_torch_gpu
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




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



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
    _UpperCamelCase:  Any    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Tuple    						=			'''.'''
    _UpperCamelCase:  Tuple    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Union[str, Any]    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Optional[Any]    						=			output.images

    _UpperCamelCase:  Tuple    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  Any    						=			np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  List[Any]    						=			'''.'''
    _UpperCamelCase:  str    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Union[str, Any]    						=			output.images

    _UpperCamelCase:  List[str]    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  int    						=			np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Any    						=			'''.'''
    _UpperCamelCase:  List[Any]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			sag_pipe(
        [prompt]		,	width=768		,	height=512		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''		,	)

    _UpperCamelCase:  str    						=			output.images

    assert image.shape == (1, 512, 768, 3)