


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
		from ..models.auto import AutoTokenizer




class 				UpperCamelCase_			:

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       List[Any]     ) ->			int:
						raise NotImplementedError()




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Optional[Any]:
						raise NotImplementedError()




class 				UpperCamelCase_			(				A_):

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[str] = False    ,    **UpperCAmelCase__    :       Any     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       tokenizer
						__SCREAMING_SNAKE_CASE		     =       skip_prompt
						__SCREAMING_SNAKE_CASE		     =       decode_kwargs

						# variables used in the streaming process
						__SCREAMING_SNAKE_CASE		     =       []
						__SCREAMING_SNAKE_CASE		     =       0
						__SCREAMING_SNAKE_CASE		     =       True




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       List[Any]     ) ->			str:
						if len(value.shape     ) > 1 and value.shape[0] > 1:
											raise ValueError("TextStreamer only supports batch size 1"     )
						elif len(value.shape     ) > 1:
											__SCREAMING_SNAKE_CASE		     =       value[0]

						if self.skip_prompt and self.next_tokens_are_prompt:
											__SCREAMING_SNAKE_CASE		     =       False
											return

						# Add the new token to the cache and decodes the entire thing.
						self.token_cache.extend(value.tolist()     )
						__SCREAMING_SNAKE_CASE		     =       self.tokenizer.decode(self.token_cache    ,    **self.decode_kwargs     )

						# After the symbol for a new line, we flush the cache.
						if text.endswith("\n"     ):
											__SCREAMING_SNAKE_CASE		     =       text[self.print_len :]
											__SCREAMING_SNAKE_CASE		     =       []
											__SCREAMING_SNAKE_CASE		     =       0
						# If the last token is a CJK character, we print the characters.
						elif len(UpperCAmelCase__     ) > 0 and self._is_chinese_char(ord(text[-1]     )     ):
											__SCREAMING_SNAKE_CASE		     =       text[self.print_len :]
											self.print_len += len(UpperCAmelCase__     )
						# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
						# which may change with the subsequent token -- there are probably smarter ways to do this!)
						else:
											__SCREAMING_SNAKE_CASE		     =       text[self.print_len : text.rfind(" "     ) + 1]
											self.print_len += len(UpperCAmelCase__     )

						self.on_finalized_text(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						# Flush the cache, if it exists
						if len(self.token_cache     ) > 0:
											__SCREAMING_SNAKE_CASE		     =       self.tokenizer.decode(self.token_cache    ,    **self.decode_kwargs     )
											__SCREAMING_SNAKE_CASE		     =       text[self.print_len :]
											__SCREAMING_SNAKE_CASE		     =       []
											__SCREAMING_SNAKE_CASE		     =       0
						else:
											__SCREAMING_SNAKE_CASE		     =       """"""

						__SCREAMING_SNAKE_CASE		     =       True
						self.on_finalized_text(UpperCAmelCase__    ,    stream_end=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Tuple = False     ) ->			List[Any]:
						print(UpperCAmelCase__    ,    flush=UpperCAmelCase__    ,    end="" if not stream_end else None     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Tuple     ) ->			Any:
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
						    or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
						    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
						    or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
						    or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
						    or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
						    or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
						    or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
						):  #
											return True

						return False




class 				UpperCamelCase_			(				A_):

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       List[Any] = False    ,    UpperCAmelCase__    :       Dict = None    ,    **UpperCAmelCase__    :       int     ) ->			Tuple:
						super().__init__(UpperCAmelCase__    ,    UpperCAmelCase__    ,    **UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       Queue()
						__SCREAMING_SNAKE_CASE		     =       None
						__SCREAMING_SNAKE_CASE		     =       timeout




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       str = False     ) ->			List[Any]:
						self.text_queue.put(UpperCAmelCase__    ,    timeout=self.timeout     )
						if stream_end:
											self.text_queue.put(self.stop_signal    ,    timeout=self.timeout     )




	def __iter__(				self    :       str     ) ->			Dict:
						return self




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       self.text_queue.get(timeout=self.timeout     )
						if value == self.stop_signal:
											raise StopIteration()
						else:
											return value




"""simple docstring"""







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 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Union[str, Any]					      =    DiTPipeline
	snake_case__       :						Union[str, Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	snake_case__       :						Any					      =    PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case__       :						Optional[Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	snake_case__       :						Optional[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       TransformeraDModel(
						    sample_size=1_6    ,    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_0_0_0    ,    norm_type="ada_norm_zero"    ,    norm_elementwise_affine=UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       AutoencoderKL()
						__SCREAMING_SNAKE_CASE		     =       DDIMScheduler()
						__SCREAMING_SNAKE_CASE		     =       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
						return components




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       str=0     ) ->			Optional[int]:
						if str(UpperCAmelCase__     ).startswith("mps"     ):
											__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       {
						    "class_labels": [1],
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       "cpu"

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()
						__SCREAMING_SNAKE_CASE		     =       self.pipeline_class(**UpperCAmelCase__     )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_inputs(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       pipe(**UpperCAmelCase__     ).images
						__SCREAMING_SNAKE_CASE		     =       image[0, -3:, -3:, -1]

						self.assertEqual(image.shape    ,    (1, 1_6, 1_6, 3)     )
						__SCREAMING_SNAKE_CASE		     =       np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]     )
						__SCREAMING_SNAKE_CASE		     =       np.abs(image_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(UpperCAmelCase__    ,    1E-3     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						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 						UpperCAmelCase_   (				self    :       int     ) ->			Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )




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

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

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

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella", "white shark", "white wolf"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=4_0    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"     )
						__SCREAMING_SNAKE_CASE		     =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=2_5    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       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
