
import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
   from transformers.models.rag.configuration_rag import RagConfig
   from transformers.models.rag.tokenization_rag import RagTokenizer






@require_faiss
@require_torch
class   UpperCAmelCase		(						_lowerCamelCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   str							=      tempfile.mkdtemp()
          snake_case       :   Any							=      8

          # DPR tok
          snake_case       :   int							=      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          snake_case       :   Optional[Any]							=      os.path.join(self.tmpdirname    ,						"dpr_tokenizer" )
          os.makedirs(lowercase_    ,						exist_ok=lowercase_ )
          snake_case       :   str							=      os.path.join(lowercase_    ,						DPR_VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file    ,						"w"    ,						encoding="utf-8" ) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

          # BART tok
          snake_case       :   Optional[int]							=      [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          snake_case       :   int							=      dict(zip(lowercase_    ,						range(len(lowercase_ ) ) ) )
          snake_case       :   Optional[Any]							=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          snake_case       :   Tuple							=      {"unk_token": "<unk>"}

          snake_case       :   List[Any]							=      os.path.join(self.tmpdirname    ,						"bart_tokenizer" )
          os.makedirs(lowercase_    ,						exist_ok=lowercase_ )
          snake_case       :   Any							=      os.path.join(lowercase_    ,						BART_VOCAB_FILES_NAMES["vocab_file"] )
          snake_case       :   int							=      os.path.join(lowercase_    ,						BART_VOCAB_FILES_NAMES["merges_file"] )
          with open(self.vocab_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 fp.write(json.dumps(lowercase_ ) + "\n" )
          with open(self.merges_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 fp.write("\n".join(lowercase_ ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Dict:


          '''simple docstring'''


          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,						"dpr_tokenizer" ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,						"bart_tokenizer" ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Any:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )



   @require_tokenizers
   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      os.path.join(self.tmpdirname    ,						"rag_tokenizer" )
          snake_case       :   Any							=      RagConfig(question_encoder=DPRConfig().to_dict()    ,						generator=BartConfig().to_dict() )
          snake_case       :   Tuple							=      RagTokenizer(question_encoder=self.get_dpr_tokenizer()    ,						generator=self.get_bart_tokenizer() )
          rag_config.save_pretrained(lowercase_ )
          rag_tokenizer.save_pretrained(lowercase_ )
          snake_case       :   Union[str, Any]							=      RagTokenizer.from_pretrained(lowercase_    ,						config=lowercase_ )
          self.assertIsInstance(new_rag_tokenizer.question_encoder    ,						lowercase_ )
          self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()    ,						rag_tokenizer.question_encoder.get_vocab() )
          self.assertIsInstance(new_rag_tokenizer.generator    ,						lowercase_ )
          self.assertEqual(new_rag_tokenizer.generator.get_vocab()    ,						rag_tokenizer.generator.get_vocab() )



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      RagTokenizer.from_pretrained("facebook/rag-token-nq" )
          snake_case       :   List[str]							=      [
              "who got the first nobel prize in physics",
              "when is the next deadpool movie being released",
              "which mode is used for short wave broadcast service",
              "who is the owner of reading football club",
              "when is the next scandal episode coming out",
              "when is the last time the philadelphia won the superbowl",
              "what is the most current adobe flash player version",
              "how many episodes are there in dragon ball z",
              "what is the first step in the evolution of the eye",
              "where is gall bladder situated in human body",
              "what is the main mineral in lithium batteries",
              "who is the president of usa right now",
              "where do the greasers live in the outsiders",
              "panda is a national animal of which country",
              "what is the name of manchester united stadium",
          ]
          snake_case       :   str							=      tokenizer(lowercase_ )
          self.assertIsNotNone(lowercase_ )



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Dict:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      RagTokenizer.from_pretrained("facebook/rag-sequence-nq" )
          snake_case       :   int							=      [
              "who got the first nobel prize in physics",
              "when is the next deadpool movie being released",
              "which mode is used for short wave broadcast service",
              "who is the owner of reading football club",
              "when is the next scandal episode coming out",
              "when is the last time the philadelphia won the superbowl",
              "what is the most current adobe flash player version",
              "how many episodes are there in dragon ball z",
              "what is the first step in the evolution of the eye",
              "where is gall bladder situated in human body",
              "what is the main mineral in lithium batteries",
              "who is the president of usa right now",
              "where do the greasers live in the outsiders",
              "panda is a national animal of which country",
              "what is the name of manchester united stadium",
          ]
          snake_case       :   Optional[int]							=      tokenizer(lowercase_ )
          self.assertIsNotNone(lowercase_ )











'''simple docstring'''

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 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		CycleDiffusionPipeline
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				snake_case_      =		PipelineTesterMixin.required_optional_params - {"latents"}
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}     )
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS


				def   A_ (		self    :					Tuple					):
									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_0085       ,				beta_end=0.012       ,				beta_schedule='''scaled_linear'''       ,				num_train_timesteps=1000       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				)
									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(lowercase_					)
									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    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]=0					):
									snake_case_									=				floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				image / 2 + 0.5
									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									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    :					Union[str, Any]					):
									snake_case_									=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_									=				self.get_dummy_components()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

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

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

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


				@unittest.skipIf(torch_device != '''cuda'''       ,				'''This test requires a GPU'''					)
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.get_dummy_components()
									for name, module in components.items():
														if hasattr(lowercase_       ,				'''half'''					):
																			snake_case_									=				module.half()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

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

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

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


				@skip_mps
				def   A_ (		self    :					Optional[int]					):
									return super().test_save_load_local()


				@unittest.skip('''non-deterministic pipeline'''					)
				def   A_ (		self    :					List[Any]					):
									return super().test_inference_batch_single_identical()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_dict_tuple_outputs_equivalent()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_save_load_optional_components()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_attention_slicing_forward_pass()







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


				def   A_ (		self    :					List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					Union[str, Any]					):
									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(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(
									    lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_       ,				torch_dtype=torch.floataa       ,				revision='''fp16'''					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									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=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				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    :					List[str]					):
									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(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									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=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

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

