





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







	'''simple docstring'''

	def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Optional[int]:



			'''simple docstring'''



			lowerCamelCase__:   List[Any]    						="ylacombe/bark-small"
			lowerCamelCase__:   int    						=tempfile.mkdtemp()
			lowerCamelCase__:   Optional[int]    						="en_speaker_1"
			lowerCamelCase__:   Optional[int]    						="This is a test string"
			lowerCamelCase__:   int    						="speaker_embeddings_path.json"
			lowerCamelCase__:   List[str]    						="speaker_embeddings"

	def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				**UpperCAmelCase_       :     Dict) ->Union[str, Any]:



			'''simple docstring'''



			return AutoTokenizer.from_pretrained(self.checkpoint ,				**__lowerCAmelCase)

	def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Dict:



			'''simple docstring'''



			shutil.rmtree(self.tmpdirname)

	def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->List[Any]:



			'''simple docstring'''



			lowerCamelCase__:   List[str]    						=self.get_tokenizer()

			lowerCamelCase__:   Any    						=BarkProcessor(tokenizer=__lowerCAmelCase)

			processor.save_pretrained(self.tmpdirname)
			lowerCamelCase__:   List[str]    						=BarkProcessor.from_pretrained(self.tmpdirname)

			self.assertEqual(processor.tokenizer.get_vocab() ,				tokenizer.get_vocab())

	@slow
	def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Dict:



			'''simple docstring'''



			lowerCamelCase__:   Any    						=BarkProcessor.from_pretrained(
			    pretrained_processor_name_or_path=self.checkpoint ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,				)
			processor.save_pretrained(
			    self.tmpdirname ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,				speaker_embeddings_directory=self.speaker_embeddings_directory ,				)

			lowerCamelCase__:   List[Any]    						=self.get_tokenizer(bos_token="(BOS)" ,				eos_token="(EOS)")

			lowerCamelCase__:   Union[str, Any]    						=BarkProcessor.from_pretrained(
			    self.tmpdirname ,				self.speaker_embeddings_dict_path ,				bos_token="(BOS)" ,				eos_token="(EOS)" ,				)

			self.assertEqual(processor.tokenizer.get_vocab() ,				tokenizer_add_kwargs.get_vocab())

	def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->Dict:



			'''simple docstring'''



			lowerCamelCase__:   Optional[Any]    						=BarkProcessor.from_pretrained(
			    pretrained_processor_name_or_path=self.checkpoint ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,				)

			lowerCamelCase__:   Optional[int]    						=35
			lowerCamelCase__:   Any    						=2
			lowerCamelCase__:   Optional[Any]    						=8

			lowerCamelCase__:   Optional[Any]    						={
			    "semantic_prompt": np.ones(__lowerCAmelCase),
			    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
			    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
			}

			# test providing already loaded voice_preset
			lowerCamelCase__:   int    						=processor(text=self.input_string ,				voice_preset=__lowerCAmelCase)

			lowerCamelCase__:   Dict    						=inputs["history_prompt"]
			for key in voice_preset:
					self.assertListEqual(voice_preset[key].tolist() ,				processed_voice_preset.get(__lowerCAmelCase ,				np.array([])).tolist())

			# test loading voice preset from npz file
			lowerCamelCase__:   int    						=os.path.join(self.tmpdirname ,				"file.npz")
			np.savez(__lowerCAmelCase ,				**__lowerCAmelCase)
			lowerCamelCase__:   Optional[Any]    						=processor(text=self.input_string ,				voice_preset=__lowerCAmelCase)
			lowerCamelCase__:   Optional[Any]    						=inputs["history_prompt"]

			for key in voice_preset:
					self.assertListEqual(voice_preset[key].tolist() ,				processed_voice_preset.get(__lowerCAmelCase ,				np.array([])).tolist())

			# test loading voice preset from the hub
			lowerCamelCase__:   Optional[Any]    						=processor(text=self.input_string ,				voice_preset=self.voice_preset)







	def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->List[Any]:



			'''simple docstring'''



			lowerCamelCase__:   int    						=self.get_tokenizer()

			lowerCamelCase__:   Tuple    						=BarkProcessor(tokenizer=__lowerCAmelCase)

			lowerCamelCase__:   Optional[Any]    						=processor(text=self.input_string)

			lowerCamelCase__:   List[str]    						=tokenizer(
			    self.input_string ,				padding="max_length" ,				max_length=256 ,				add_special_tokens=__lowerCAmelCase ,				return_attention_mask=__lowerCAmelCase ,				return_token_type_ids=__lowerCAmelCase ,				)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key] ,				encoded_processor[key].squeeze().tolist())








import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    DiTPipeline
 lowercase_	=    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 lowercase_	=    PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_	=    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 lowercase_	=    False

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



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=TransformeraDModel(
       sample_size=16 ,				num_layers=2 ,				patch_size=4 ,				attention_head_dim=8 ,				num_attention_heads=2 ,				in_channels=4 ,				out_channels=8 ,				attention_bias=UpperCAmelCase_ ,				activation_fn="gelu-approximate" ,				num_embeds_ada_norm=1_000 ,				norm_type="ada_norm_zero" ,				norm_elementwise_affine=UpperCAmelCase_ ,				)
   lowerCamelCase__:   List[str]    						=AutoencoderKL()
   lowerCamelCase__:   Tuple    						=DDIMScheduler()
   lowerCamelCase__:   Tuple    						={"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
   return components

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     List[Any]=0) ->List[Any]:



   '''simple docstring'''



   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:   Any    						=torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:   Union[str, Any]    						=torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:   List[Any]    						={
       "class_labels": [1],
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs

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



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						="cpu"

   lowerCamelCase__:   Union[str, Any]    						=self.get_dummy_components()
   lowerCamelCase__:   List[str]    						=self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:   List[str]    						=self.get_dummy_inputs(UpperCAmelCase_)
   lowerCamelCase__:   Any    						=pipe(**UpperCAmelCase_).images
   lowerCamelCase__:   List[str]    						=image[0, -3:, -3:, -1]

   self.assertEqual(image.shape ,				(1, 16, 16, 3))
   lowerCamelCase__:   Optional[Any]    						=np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])
   lowerCamelCase__:   List[Any]    						=np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(UpperCAmelCase_ ,				1E-3)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->str:



   '''simple docstring'''



   self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase_ ,				expected_max_diff=1E-3)







 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available() ,				reason="XFormers attention is only available with CUDA and `xformers` installed" ,				)
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->int:



   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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







 '''simple docstring'''

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=torch.manual_seed(0)

   lowerCamelCase__:   Tuple    						=DiTPipeline.from_pretrained("facebook/DiT-XL-2-256")
   pipe.to("cuda")

   lowerCamelCase__:   Any    						=["vase", "umbrella", "white shark", "white wolf"]
   lowerCamelCase__:   Any    						=pipe.get_label_ids(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=pipe(UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				num_inference_steps=40 ,				output_type="np").images

   for word, image in zip(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   Optional[int]    						=load_numpy(
         F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""")
     assert np.abs((expected_image - image).max()) < 1E-2







 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
   lowerCamelCase__:   List[str]    						=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
   pipe.to("cuda")

   lowerCamelCase__:   List[str]    						=["vase", "umbrella"]
   lowerCamelCase__:   List[Any]    						=pipe.get_label_ids(UpperCAmelCase_)

   lowerCamelCase__:   str    						=torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=pipe(UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				num_inference_steps=25 ,				output_type="np").images

   for word, image in zip(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   str    						=load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         F"""/dit/{word}_512.npy""")

     assert np.abs((expected_image - image).max()) < 1E-1

