





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		__a				(						__UpperCamelCase      ,__UpperCamelCase      ,unittest.TestCase   ):
       __snake_case    : Union[str, Any]   =		StableDiffusionDiffEditPipeline
       __snake_case    : Any   =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
       __snake_case    : List[str]   =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
       __snake_case    : Any   =		frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       __snake_case    : List[Any]   =		frozenset([]   )



       def 			A				(  self						: Any      ):
           torch.manual_seed(0      )
           lowerCAmelCase_       :						Optional[Any]							= 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       ,      attention_head_dim=(2, 4)       ,      use_linear_projection=UpperCamelCase_       ,      )
           lowerCAmelCase_       :						List[Any]							= DDIMScheduler(
               beta_start=0.0_0085       ,      beta_end=0.012       ,      beta_schedule="""scaled_linear"""       ,      clip_sample=UpperCamelCase_       ,      set_alpha_to_one=UpperCamelCase_       ,      )
           lowerCAmelCase_       :						List[Any]							= DDIMInverseScheduler(
               beta_start=0.0_0085       ,      beta_end=0.012       ,      beta_schedule="""scaled_linear"""       ,      clip_sample=UpperCamelCase_       ,      set_alpha_to_zero=UpperCamelCase_       ,      )
           torch.manual_seed(0      )
           lowerCAmelCase_       :						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       ,      sample_size=1_28       ,      )
           torch.manual_seed(0      )
           lowerCAmelCase_       :						Optional[Any]							= CLIPTextConfig(
               bos_token_id=0       ,      eos_token_id=2       ,      hidden_size=32       ,      intermediate_size=37       ,      layer_norm_eps=1e-0_5       ,      num_attention_heads=4       ,      num_hidden_layers=5       ,      pad_token_id=1       ,      vocab_size=10_00       ,      hidden_act="""gelu"""       ,      projection_dim=5_12       ,      )
           lowerCAmelCase_       :						str							= CLIPTextModel(UpperCamelCase_      )
           lowerCAmelCase_       :						Union[str, Any]							= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

           lowerCAmelCase_       :						Optional[int]							= {
               'unet': unet,
               'scheduler': scheduler,
               'inverse_scheduler': inverse_scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }

           return components



       def 			A				(  self						: Optional[int]       ,      UpperCAmelCase						: Optional[Any]       ,      UpperCAmelCase						: str=0      ):
           lowerCAmelCase_       :						Tuple							= floats_tensor((1, 16, 16)       ,      rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
           lowerCAmelCase_       :						Union[str, Any]							= floats_tensor((1, 2, 4, 16, 16)       ,      rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
           if str(UpperCamelCase_      ).startswith("""mps"""      ):
               lowerCAmelCase_       :						Optional[int]							= torch.manual_seed(UpperCamelCase_      )
           else:
               lowerCAmelCase_       :						Optional[int]							= torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
           lowerCAmelCase_       :						Optional[int]							= {
               'prompt': 'a dog and a newt',
               'mask_image': mask,
               'image_latents': latents,
               'generator': generator,
               'num_inference_steps': 2,
               'inpaint_strength': 1.0,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }

           return inputs



       def 			A				(  self						: str       ,      UpperCAmelCase						: Optional[int]       ,      UpperCAmelCase						: int=0      ):
           lowerCAmelCase_       :						str							= floats_tensor((1, 3, 32, 32)       ,      rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
           lowerCAmelCase_       :						str							= image.cpu().permute(0       ,      2       ,      3       ,      1      )[0]
           lowerCAmelCase_       :						List[Any]							= Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert("""RGB"""      )
           if str(UpperCamelCase_      ).startswith("""mps"""      ):
               lowerCAmelCase_       :						Optional[int]							= torch.manual_seed(UpperCamelCase_      )
           else:
               lowerCAmelCase_       :						Optional[Any]							= torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
           lowerCAmelCase_       :						Dict							= {
               'image': image,
               'source_prompt': 'a cat and a frog',
               'target_prompt': 'a dog and a newt',
               'generator': generator,
               'num_inference_steps': 2,
               'num_maps_per_mask': 2,
               'mask_encode_strength': 1.0,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }

           return inputs



       def 			A				(  self						: List[Any]       ,      UpperCAmelCase						: Union[str, Any]       ,      UpperCAmelCase						: Optional[int]=0      ):
           lowerCAmelCase_       :						int							= floats_tensor((1, 3, 32, 32)       ,      rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
           lowerCAmelCase_       :						Optional[Any]							= image.cpu().permute(0       ,      2       ,      3       ,      1      )[0]
           lowerCAmelCase_       :						Optional[int]							= Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert("""RGB"""      )
           if str(UpperCamelCase_      ).startswith("""mps"""      ):
               lowerCAmelCase_       :						Tuple							= torch.manual_seed(UpperCamelCase_      )
           else:
               lowerCAmelCase_       :						Tuple							= torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
           lowerCAmelCase_       :						str							= {
               'image': image,
               'prompt': 'a cat and a frog',
               'generator': generator,
               'num_inference_steps': 2,
               'inpaint_strength': 1.0,
               'guidance_scale': 6.0,
               'decode_latents': True,
               'output_type': 'numpy',
           }
           return inputs



       def 			A				(  self						: List[str]      ):
           if not hasattr(self.pipeline_class       ,      """_optional_components"""      ):
               return

           lowerCAmelCase_       :						int							= self.get_dummy_components()
           lowerCAmelCase_       :						List[Any]							= self.pipeline_class(**UpperCamelCase_      )
           pipe.to(UpperCamelCase_      )
           pipe.set_progress_bar_config(disable=UpperCamelCase_      )

           # set all optional components to None and update pipeline config accordingly
           for optional_component in pipe._optional_components:
               setattr(UpperCamelCase_       ,      UpperCamelCase_       ,      UpperCamelCase_      )
           pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}      )

           lowerCAmelCase_       :						Dict							= self.get_dummy_inputs(UpperCamelCase_      )
           lowerCAmelCase_       :						Optional[Any]							= pipe(**UpperCamelCase_      )[0]

           with tempfile.TemporaryDirectory() as tmpdir:
               pipe.save_pretrained(UpperCamelCase_      )
               lowerCAmelCase_       :						str							= self.pipeline_class.from_pretrained(UpperCamelCase_      )
               pipe_loaded.to(UpperCamelCase_      )
               pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_      )

           for optional_component in pipe._optional_components:
               self.assertTrue(
                   getattr(UpperCamelCase_       ,      UpperCamelCase_      ) is None       ,      F'`{optional_component}` did not stay set to None after loading.'       ,      )

           lowerCAmelCase_       :						Optional[int]							= self.get_dummy_inputs(UpperCamelCase_      )
           lowerCAmelCase_       :						Optional[Any]							= pipe_loaded(**UpperCamelCase_      )[0]

           lowerCAmelCase_       :						Tuple							= np.abs(output - output_loaded      ).max()
           self.assertLess(UpperCamelCase_       ,      1e-4      )



       def 			A				(  self						: List[Any]      ):
           lowerCAmelCase_       :						List[Any]							= 'cpu'

           lowerCAmelCase_       :						Optional[Any]							= self.get_dummy_components()
           lowerCAmelCase_       :						List[str]							= self.pipeline_class(**UpperCamelCase_      )
           pipe.to(UpperCamelCase_      )
           pipe.set_progress_bar_config(disable=UpperCamelCase_      )

           lowerCAmelCase_       :						List[Any]							= self.get_dummy_mask_inputs(UpperCamelCase_      )
           lowerCAmelCase_       :						Union[str, Any]							= pipe.generate_mask(**UpperCamelCase_      )
           lowerCAmelCase_       :						Any							= mask[0, -3:, -3:]

           self.assertEqual(mask.shape       ,      (1, 16, 16)      )
           lowerCAmelCase_       :						str							= np.array([0] * 9      )
           lowerCAmelCase_       :						Dict							= np.abs(mask_slice.flatten() - expected_slice      ).max()
           self.assertLessEqual(UpperCamelCase_       ,      1e-3      )
           self.assertEqual(mask[0, -3, -4]       ,      0      )



       def 			A				(  self						: Optional[int]      ):
           lowerCAmelCase_       :						Tuple							= 'cpu'

           lowerCAmelCase_       :						List[str]							= self.get_dummy_components()
           lowerCAmelCase_       :						Optional[int]							= self.pipeline_class(**UpperCamelCase_      )
           pipe.to(UpperCamelCase_      )
           pipe.set_progress_bar_config(disable=UpperCamelCase_      )

           lowerCAmelCase_       :						List[Any]							= self.get_dummy_inversion_inputs(UpperCamelCase_      )
           lowerCAmelCase_       :						Union[str, Any]							= pipe.invert(**UpperCamelCase_      ).images
           lowerCAmelCase_       :						List[str]							= image[0, -1, -3:, -3:]

           self.assertEqual(image.shape       ,      (2, 32, 32, 3)      )
           lowerCAmelCase_       :						Optional[int]							= np.array(
               [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,      )
           lowerCAmelCase_       :						Any							= np.abs(image_slice.flatten() - expected_slice      ).max()
           self.assertLessEqual(UpperCamelCase_       ,      1e-3      )



       def 			A				(  self						: Any      ):
           super().test_inference_batch_single_identical(expected_max_diff=5e-3      )



       def 			A				(  self						: List[Any]      ):
           lowerCAmelCase_       :						Optional[Any]							= 'cpu'

           lowerCAmelCase_       :						int							= self.get_dummy_components()

           lowerCAmelCase_       :						List[Any]							= {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
           lowerCAmelCase_       :						List[str]							= DPMSolverMultistepScheduler(**UpperCamelCase_      )
           lowerCAmelCase_       :						Tuple							= DPMSolverMultistepInverseScheduler(**UpperCamelCase_      )

           lowerCAmelCase_       :						List[Any]							= self.pipeline_class(**UpperCamelCase_      )
           pipe.to(UpperCamelCase_      )
           pipe.set_progress_bar_config(disable=UpperCamelCase_      )

           lowerCAmelCase_       :						Optional[Any]							= self.get_dummy_inversion_inputs(UpperCamelCase_      )
           lowerCAmelCase_       :						Tuple							= pipe.invert(**UpperCamelCase_      ).images
           lowerCAmelCase_       :						Optional[Any]							= image[0, -1, -3:, -3:]

           self.assertEqual(image.shape       ,      (2, 32, 32, 3)      )
           lowerCAmelCase_       :						Dict							= np.array(
               [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,      )
           lowerCAmelCase_       :						str							= np.abs(image_slice.flatten() - expected_slice      ).max()
           self.assertLessEqual(UpperCamelCase_       ,      1e-3      )






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



       def 			A				(  self						: Tuple      ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       @classmethod
       def 			A				(  cls						: Optional[int]      ):
           lowerCAmelCase_       :						Dict							= load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""      )

           lowerCAmelCase_       :						List[Any]							= raw_image.convert("""RGB"""      ).resize((7_68, 7_68)      )

           lowerCAmelCase_       :						List[Any]							= raw_image



       def 			A				(  self						: str      ):
           lowerCAmelCase_       :						int							= torch.manual_seed(0      )

           lowerCAmelCase_       :						List[str]							= StableDiffusionDiffEditPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2-1"""       ,      safety_checker=UpperCamelCase_       ,      torch_dtype=torch.floataa      )
           lowerCAmelCase_       :						Tuple							= DDIMScheduler.from_config(pipe.scheduler.config      )
           lowerCAmelCase_       :						Tuple							= DDIMInverseScheduler.from_config(pipe.scheduler.config      )
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCamelCase_      )

           lowerCAmelCase_       :						List[str]							= 'a bowl of fruit'
           lowerCAmelCase_       :						Optional[Any]							= 'a bowl of pears'

           lowerCAmelCase_       :						Tuple							= pipe.generate_mask(
               image=self.raw_image       ,      source_prompt=UpperCamelCase_       ,      target_prompt=UpperCamelCase_       ,      generator=UpperCamelCase_       ,      )

           lowerCAmelCase_       :						str							= pipe.invert(
               prompt=UpperCamelCase_       ,      image=self.raw_image       ,      inpaint_strength=0.7       ,      generator=UpperCamelCase_      ).latents

           lowerCAmelCase_       :						Any							= pipe(
               prompt=UpperCamelCase_       ,      mask_image=UpperCamelCase_       ,      image_latents=UpperCamelCase_       ,      generator=UpperCamelCase_       ,      negative_prompt=UpperCamelCase_       ,      inpaint_strength=0.7       ,      output_type="""numpy"""       ,      ).images[0]

           lowerCAmelCase_       :						List[str]							= (
               np.array(
                   load_image(
                       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                       """/diffedit/pears.png"""      ).resize((7_68, 7_68)      )      )
               / 2_55
           )
           assert np.abs((expected_image - image).max()      ) < 5e-1



       def 			A				(  self						: int      ):
           lowerCAmelCase_       :						str							= torch.manual_seed(0      )

           lowerCAmelCase_       :						Any							= StableDiffusionDiffEditPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2-1"""       ,      safety_checker=UpperCamelCase_       ,      torch_dtype=torch.floataa      )
           lowerCAmelCase_       :						List[Any]							= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
           lowerCAmelCase_       :						List[Any]							= DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config      )
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=UpperCamelCase_      )

           lowerCAmelCase_       :						List[str]							= 'a bowl of fruit'
           lowerCAmelCase_       :						Tuple							= 'a bowl of pears'

           lowerCAmelCase_       :						List[Any]							= pipe.generate_mask(
               image=self.raw_image       ,      source_prompt=UpperCamelCase_       ,      target_prompt=UpperCamelCase_       ,      generator=UpperCamelCase_       ,      )

           lowerCAmelCase_       :						Tuple							= pipe.invert(
               prompt=UpperCamelCase_       ,      image=self.raw_image       ,      inpaint_strength=0.7       ,      generator=UpperCamelCase_       ,      num_inference_steps=25       ,      ).latents

           lowerCAmelCase_       :						Any							= pipe(
               prompt=UpperCamelCase_       ,      mask_image=UpperCamelCase_       ,      image_latents=UpperCamelCase_       ,      generator=UpperCamelCase_       ,      negative_prompt=UpperCamelCase_       ,      inpaint_strength=0.7       ,      num_inference_steps=25       ,      output_type="""numpy"""       ,      ).images[0]

           lowerCAmelCase_       :						Union[str, Any]							= (
               np.array(
                   load_image(
                       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                       """/diffedit/pears.png"""      ).resize((7_68, 7_68)      )      )
               / 2_55
           )
           assert np.abs((expected_image - image).max()      ) < 5e-1




"""simple docstring"""



import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


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

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCamelCase__          =	{
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 1_28,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class 				a			(					unittest.TestCase      ):



      @classmethod
      def  __snake_case							(   cls      ):
          UpperCAmelCase__	:      List[str]     							=					TOKEN
          HfFolder.save_token(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls      ):
          try:
              delete_repo(token=cls._token     ,	repo_id='test-config'      )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token     ,	repo_id='valid_org/test-config-org'      )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token     ,	repo_id='test-dynamic-config'      )
          except HTTPError:
              pass



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('test-config'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[int]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

        # Reset repo
          delete_repo(token=self._token     ,	repo_id='test-config'      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(UpperCamelCase_     ,	repo_id='test-config'     ,	push_to_hub=UpperCamelCase_     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('valid_org/test-config-org'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

        # Reset repo
          delete_repo(token=self._token     ,	repo_id='valid_org/test-config-org'      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  UpperCamelCase_     ,	repo_id='valid_org/test-config-org'     ,	push_to_hub=UpperCamelCase_     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          CustomConfig.register_for_auto_class()
          UpperCAmelCase__	:      Dict     							=					CustomConfig(attribute=42      )

          config.push_to_hub('test-dynamic-config'     ,	use_auth_token=self._token      )

          # This has added the proper auto_map field to the config
          self.assertDictEqual(config.auto_map     ,	{'AutoConfig': 'custom_configuration.CustomConfig'}      )

          UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''     ,	trust_remote_code=UpperCamelCase_      )
          # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
          self.assertEqual(new_config.__class__.__name__     ,	'CustomConfig'      )
          self.assertEqual(new_config.attribute     ,	42      )





class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					GPTaConfig()

          # attempt to modify each of int/float/bool/str config records and verify they were updated
          UpperCAmelCase__	:      List[str]     							=					c.n_embd + 1  # int
          UpperCAmelCase__	:      Optional[int]     							=					c.resid_pdrop + 1.0  # float
          UpperCAmelCase__	:      Optional[int]     							=					not c.scale_attn_weights  # bool
          UpperCAmelCase__	:      str     							=					c.summary_type + 'foo'  # str
          c.update_from_string(
              F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''      )
          self.assertEqual(UpperCamelCase_     ,	c.n_embd     ,	'mismatch for key: n_embd'      )
          self.assertEqual(UpperCamelCase_     ,	c.resid_pdrop     ,	'mismatch for key: resid_pdrop'      )
          self.assertEqual(UpperCamelCase_     ,	c.scale_attn_weights     ,	'mismatch for key: scale_attn_weights'      )
          self.assertEqual(UpperCamelCase_     ,	c.summary_type     ,	'mismatch for key: summary_type'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					PretrainedConfig()
          UpperCAmelCase__	:      str     							=					[key for key in base_config.__dict__ if key not in config_common_kwargs]
          # If this part of the test fails, you have arguments to addin config_common_kwargs above.
          self.assertListEqual(
              UpperCamelCase_     ,	['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']      )
          UpperCAmelCase__	:      int     							=					[key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase_     ,	UpperCamelCase_      )]
          if len(UpperCamelCase_      ) > 0:
              raise ValueError(
                  'The following keys are set with the default values in'
                  ' `test_configuration_common.config_common_kwargs` pick another value for them:'
                  F''' {', '.join(UpperCamelCase_      )}.'''      )



      def  __snake_case							(   self      ):
          with self.assertRaises(UpperCamelCase_      ):
              # config is in subfolder, the following should not work without specifying the subfolder
              UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'      )

          UpperCAmelCase__	:      str     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'     ,	subfolder='bert'      )

          self.assertIsNotNone(UpperCamelCase_      )



      def  __snake_case							(   self      ):
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase__	:      str     							=					mock.Mock()
          UpperCAmelCase__	:      List[Any]     							=					500
          UpperCAmelCase__	:      Optional[int]     							=					{}
          UpperCAmelCase__	:      Dict     							=					HTTPError
          UpperCAmelCase__	:      Optional[int]     							=					{}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )

          # Under the mock environment we get a 500 error when trying to reach the model.
          with mock.patch('requests.Session.request'     ,	return_value=UpperCamelCase_      ) as mock_head:
              UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )
              # This check we did call the fake head request
              mock_head.assert_called()



      def  __snake_case							(   self      ):
          # This test is for deprecated behavior and can be removed in v5
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained(
              'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					AutoConfig.from_pretrained('bert-base-cased'      )
          UpperCAmelCase__	:      str     							=					['config.4.0.0.json']

          with tempfile.TemporaryDirectory() as tmp_dir:
              configuration.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					2
              json.dump(configuration.to_dict()     ,	open(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	'w'      )      )

              # This should pick the new configuration file as the version of Transformers is > 4.0.0
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	2      )

              # Will need to be adjusted if we reach v42 and this test is still here.
              # Should pick the old configuration file as the version of Transformers is < 4.42.0
              UpperCAmelCase__	:      Tuple     							=					['config.42.0.0.json']
              UpperCAmelCase__	:      Tuple     							=					768
              configuration.save_pretrained(UpperCamelCase_      )
              shutil.move(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	os.path.join(UpperCamelCase_     ,	'config.42.0.0.json'      )      )
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	768      )



      def  __snake_case							(   self      ):
          # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
          UpperCAmelCase__	:      Tuple     							=					'hf-internal-testing/test-two-configs'

          import transformers as new_transformers

          UpperCAmelCase__	:      Optional[int]     							=					'v4.0.0'
          UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					new_transformers.models.auto.AutoConfig.from_pretrained(
              UpperCamelCase_     ,	return_unused_kwargs=UpperCamelCase_      )
          self.assertEqual(new_configuration.hidden_size     ,	2      )
          # This checks `_configuration_file` ia not kept in the kwargs by mistake.
          self.assertDictEqual(UpperCamelCase_     ,	{}      )

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          UpperCAmelCase__	:      List[Any]     							=					'v3.0.0'
          UpperCAmelCase__	:      Union[str, Any]     							=					old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase_      )
          self.assertEqual(old_configuration.hidden_size     ,	768      )

