





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


snake_case_           =      {
    '''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''': 128,
    '''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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      @classmethod
      def        a			(cls	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       TOKEN
         HfFolder.save_token(SCREAMING_SNAKE_CASE_				)


      @classmethod
      def        a			(cls	:				Union[str, Any]				):





         """simple docstring"""



         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        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       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				)

         __snake_case								=       BertConfig.from_pretrained(f"""{USER}/test-config"""				)
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(SCREAMING_SNAKE_CASE_    ,      getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_				)				)

        # 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(SCREAMING_SNAKE_CASE_    ,      repo_id='''test-config'''    ,      push_to_hub=SCREAMING_SNAKE_CASE_    ,      use_auth_token=self._token				)

         __snake_case								=       BertConfig.from_pretrained(f"""{USER}/test-config"""				)
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(SCREAMING_SNAKE_CASE_    ,      getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_				)				)


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       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				)

         __snake_case								=       BertConfig.from_pretrained('''valid_org/test-config-org'''				)
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(SCREAMING_SNAKE_CASE_    ,      getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_				)				)

        # 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(
                SCREAMING_SNAKE_CASE_    ,      repo_id='''valid_org/test-config-org'''    ,      push_to_hub=SCREAMING_SNAKE_CASE_    ,      use_auth_token=self._token				)

         __snake_case								=       BertConfig.from_pretrained('''valid_org/test-config-org'''				)
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(SCREAMING_SNAKE_CASE_    ,      getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_				)				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         CustomConfig.register_for_auto_class()
         __snake_case								=       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'''}				)

         __snake_case								=       AutoConfig.from_pretrained(f"""{USER}/test-dynamic-config"""    ,      trust_remote_code=SCREAMING_SNAKE_CASE_				)
         # 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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


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





         """simple docstring"""



         __snake_case								=       GPTaConfig()

         # attempt to modify each of int/float/bool/str config records and verify they were updated
         __snake_case								=       c.n_embd + 1  # int
         __snake_case								=       c.resid_pdrop + 1.0  # float
         __snake_case								=       not c.scale_attn_weights  # bool
         __snake_case								=       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(SCREAMING_SNAKE_CASE_    ,      c.n_embd    ,      '''mismatch for key: n_embd'''				)
         self.assertEqual(SCREAMING_SNAKE_CASE_    ,      c.resid_pdrop    ,      '''mismatch for key: resid_pdrop'''				)
         self.assertEqual(SCREAMING_SNAKE_CASE_    ,      c.scale_attn_weights    ,      '''mismatch for key: scale_attn_weights'''				)
         self.assertEqual(SCREAMING_SNAKE_CASE_    ,      c.summary_type    ,      '''mismatch for key: summary_type'''				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       PretrainedConfig()
         __snake_case								=       [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(
             SCREAMING_SNAKE_CASE_    ,      ['''is_encoder_decoder''', '''_name_or_path''', '''_commit_hash''', '''transformers_version''']				)
         __snake_case								=       [key for key, value in config_common_kwargs.items() if value == getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_				)]
         if len(SCREAMING_SNAKE_CASE_				) > 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(SCREAMING_SNAKE_CASE_				)}."""				)


      def        a			(self	:				Any				):





         """simple docstring"""



         with self.assertRaises(SCREAMING_SNAKE_CASE_				):
            # config is in subfolder, the following should not work without specifying the subfolder
            __snake_case								=       BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder'''				)

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

         self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       mock.Mock()
         __snake_case								=       500
         __snake_case								=       {}
         __snake_case								=       HTTPError
         __snake_case								=       {}

         # Download this model to make sure it's in the cache.
         __snake_case								=       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=SCREAMING_SNAKE_CASE_				) as mock_head:
            __snake_case								=       BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
            # This check we did call the fake head request
            mock_head.assert_called()


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       BertConfig.from_pretrained(
             '''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'''				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       AutoConfig.from_pretrained('''bert-base-cased'''				)
         __snake_case								=       ["""config.4.0.0.json"""]

         with tempfile.TemporaryDirectory() as tmp_dir:
            configuration.save_pretrained(SCREAMING_SNAKE_CASE_				)
            __snake_case								=       2
            json.dump(configuration.to_dict()    ,      open(os.path.join(SCREAMING_SNAKE_CASE_    ,      '''config.4.0.0.json'''				)    ,      '''w'''				)				)

            # This should pick the new configuration file as the version of Transformers is > 4.0.0
            __snake_case								=       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
            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
            __snake_case								=       ["""config.42.0.0.json"""]
            __snake_case								=       768
            configuration.save_pretrained(SCREAMING_SNAKE_CASE_				)
            shutil.move(os.path.join(SCREAMING_SNAKE_CASE_    ,      '''config.4.0.0.json'''				)    ,      os.path.join(SCREAMING_SNAKE_CASE_    ,      '''config.42.0.0.json'''				)				)
            __snake_case								=       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
            self.assertEqual(new_configuration.hidden_size    ,      768				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       """hf-internal-testing/test-two-configs"""

         import transformers as new_transformers

         __snake_case								=       """v4.0.0"""
         __snake_case								=       new_transformers.models.auto.AutoConfig.from_pretrained(
             SCREAMING_SNAKE_CASE_    ,      return_unused_kwargs=SCREAMING_SNAKE_CASE_				)
         self.assertEqual(new_configuration.hidden_size    ,      2				)
         # This checks `_configuration_file` ia not kept in the kwargs by mistake.
         self.assertDictEqual(SCREAMING_SNAKE_CASE_    ,      {}				)

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

         __snake_case								=       """v3.0.0"""
         __snake_case								=       old_transformers.models.auto.AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
         self.assertEqual(old_configuration.hidden_size    ,      768				)











import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Optional[int]							=   CycleDiffusionPipeline
      A_						:    List[Any]							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'negative_prompt',
          'height',
          'width',
          'negative_prompt_embeds',
      }
      A_						:    List[str]							=   PipelineTesterMixin.required_optional_params - {'latents'}
      A_						:    List[str]							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
      A_						:    List[str]							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
      A_						:    Any							=   IMAGE_TO_IMAGE_IMAGE_PARAMS


      def        a			(self	:				Dict				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       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    ,      )
         __snake_case								=       DDIMScheduler(
             beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      beta_schedule='''scaled_linear'''    ,      num_train_timesteps=1000    ,      clip_sample=a__    ,      set_alpha_to_one=a__    ,      )
         torch.manual_seed(0				)
         __snake_case								=       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				)
         __snake_case								=       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=1000    ,      )
         __snake_case								=       CLIPTextModel(a__				)
         __snake_case								=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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


      def        a			(self	:				List[str]    ,      a__	:				Tuple    ,      a__	:				Optional[Any]=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       image / 2 + 0.5
         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)
         __snake_case								=       {
             '''prompt''': '''An astronaut riding an elephant''',
             '''source_prompt''': '''An astronaut riding a horse''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''eta''': 0.1,
             '''strength''': 0.8,
             '''guidance_scale''': 3,
             '''source_guidance_scale''': 1,
             '''output_type''': '''numpy''',
         }
         return inputs


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __snake_case								=       self.get_dummy_components()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


      @unittest.skipIf(torch_device != '''cuda'''    ,      '''This test requires a GPU'''				)
      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       self.get_dummy_components()
         for name, module in components.items():
            if hasattr(a__    ,      '''half'''				):
               __snake_case								=       module.half()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_save_load_local()


      @unittest.skip('''non-deterministic pipeline'''				)
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_inference_batch_single_identical()


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_dict_tuple_outputs_equivalent()


      @skip_mps
      def        a			(self	:				str				):





         """simple docstring"""



         return super().test_save_load_optional_components()


      @skip_mps
      def        a			(self	:				Dict				):





         """simple docstring"""



         return super().test_attention_slicing_forward_pass()






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


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





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(
             a__    ,      scheduler=a__    ,      safety_checker=a__    ,      torch_dtype=torch.floataa    ,      revision='''fp16'''				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image				).max() < 5E-1


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(a__    ,      scheduler=a__    ,      safety_checker=a__				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         assert np.abs(image - expected_image				).max() < 2E-2

