

from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder



@dataclass
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							"DiagonalGaussianDistribution"







class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					True



			@register_to_config
			def __init__(			self  :					str       ,						lowerCAmelCase__  :					int = 3       ,						lowerCAmelCase__  :					int = 3       ,						lowerCAmelCase__  :					Tuple[str] = ("DownEncoderBlock2D",)       ,						lowerCAmelCase__  :					Tuple[str] = ("UpDecoderBlock2D",)       ,						lowerCAmelCase__  :					Tuple[int] = (64,)       ,						lowerCAmelCase__  :					int = 1       ,						lowerCAmelCase__  :					str = "silu"       ,						lowerCAmelCase__  :					int = 4       ,						lowerCAmelCase__  :					int = 32       ,						lowerCAmelCase__  :					int = 32       ,						lowerCAmelCase__  :					float = 0.1_8215       ,						):
									super().__init__()

									# pass init params to Encoder
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Encoder(
									    in_channels=lowerCAmelCase__       ,						out_channels=lowerCAmelCase__       ,						down_block_types=lowerCAmelCase__       ,						block_out_channels=lowerCAmelCase__       ,						layers_per_block=lowerCAmelCase__       ,						act_fn=lowerCAmelCase__       ,						norm_num_groups=lowerCAmelCase__       ,						double_z=lowerCAmelCase__       ,						)

									# pass init params to Decoder
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	Decoder(
									    in_channels=lowerCAmelCase__       ,						out_channels=lowerCAmelCase__       ,						up_block_types=lowerCAmelCase__       ,						block_out_channels=lowerCAmelCase__       ,						layers_per_block=lowerCAmelCase__       ,						norm_num_groups=lowerCAmelCase__       ,						act_fn=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Dict  		=	nn.Convad(2 * latent_channels       ,						2 * latent_channels       ,						1)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	nn.Convad(lowerCAmelCase__       ,						lowerCAmelCase__       ,						1)

									SCREAMING_SNAKE_CASE_:			str  		=	False
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	False

									# only relevant if vae tiling is enabled
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.config.sample_size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	(
									    self.config.sample_size[0]
									    if isinstance(self.config.sample_size       ,						(list, tuple))
									    else self.config.sample_size
									)
									SCREAMING_SNAKE_CASE_:			str  		=	int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
									SCREAMING_SNAKE_CASE_:			int  		=	0.25



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					List[str]=False):
									if isinstance(lowerCAmelCase__       ,						(Encoder, Decoder)):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	value



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					bool = True):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	use_tiling



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									self.enable_tiling(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	True



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	False



			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{}

									def fn_recursive_add_processors(lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					torch.nn.Module       ,						lowerCAmelCase__  :					Dict[str, AttentionProcessor]):
															if hasattr(lowerCAmelCase__       ,						"set_processor"):
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	module.processor

															for sub_name, child in module.named_children():
																					fn_recursive_add_processors(F"{name}.{sub_name}"       ,						lowerCAmelCase__       ,						lowerCAmelCase__)

															return processors

									for name, module in self.named_children():
															fn_recursive_add_processors(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)

									return processors



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(self.attn_processors.keys())

									if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) and len(lowerCAmelCase__) != count:
															raise ValueError(
															    F"A dict of processors was passed, but the number of processors {len(lowerCAmelCase__)} does not match the"
															    F" number of attention layers: {count}. Please make sure to pass {count} processor classes.")

									def fn_recursive_attn_processor(lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					torch.nn.Module       ,						lowerCAmelCase__  :					Optional[Any]):
															if hasattr(lowerCAmelCase__       ,						"set_processor"):
																					if not isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
																											module.set_processor(lowerCAmelCase__)
																					else:
																											module.set_processor(processor.pop(F"{name}.processor"))

															for sub_name, child in module.named_children():
																					fn_recursive_attn_processor(F"{name}.{sub_name}"       ,						lowerCAmelCase__       ,						lowerCAmelCase__)

									for name, module in self.named_children():
															fn_recursive_attn_processor(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									self.set_attn_processor(AttnProcessor())



			@apply_forward_hook
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					bool = True):
									if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
															return self.tiled_encode(lowerCAmelCase__       ,						return_dict=lowerCAmelCase__)

									if self.use_slicing and x.shape[0] > 1:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.encoder(lowerCAmelCase__) for x_slice in x.split(1)]
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cat(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			str  		=	self.encoder(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.quant_conv(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	DiagonalGaussianDistribution(lowerCAmelCase__)

									if not return_dict:
															return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					bool = True):
									if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
															return self.tiled_decode(lowerCAmelCase__       ,						return_dict=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.post_quant_conv(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	self.decoder(lowerCAmelCase__)

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=lowerCAmelCase__)



			@apply_forward_hook
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					bool = True):
									if self.use_slicing and z.shape[0] > 1:
															SCREAMING_SNAKE_CASE_:			int  		=	[self._decode(lowerCAmelCase__).sample for z_slice in z.split(1)]
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.cat(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self._decode(lowerCAmelCase__).sample

									if not return_dict:
															return (decoded,)

									return DecoderOutput(sample=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	min(a.shape[2]       ,						b.shape[2]       ,						lowerCAmelCase__)
									for y in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Any  		=	a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	min(a.shape[3]       ,						b.shape[3]       ,						lowerCAmelCase__)
									for x in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
									return b



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					bool = True):
									SCREAMING_SNAKE_CASE_:			int  		=	int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
									SCREAMING_SNAKE_CASE_:			str  		=	int(self.tile_latent_min_size * self.tile_overlap_factor)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.tile_latent_min_size - blend_extent

									# Split the image into 512x512 tiles and encode them separately.
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[]
									for i in range(0       ,						x.shape[2]       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
															for j in range(0       ,						x.shape[3]       ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_:			str  		=	x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																					SCREAMING_SNAKE_CASE_:			Dict  		=	self.encoder(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.quant_conv(lowerCAmelCase__)
																					row.append(lowerCAmelCase__)
															rows.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[]
									for i, row in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			str  		=	[]
															for j, tile in enumerate(lowerCAmelCase__):
																					# blend the above tile and the left tile
																					# to the current tile and add the current tile to the result row
																					if i > 0:
																											SCREAMING_SNAKE_CASE_:			List[str]  		=	self.blend_v(rows[i - 1][j]       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
																					if j > 0:
																											SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.blend_h(row[j - 1]       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
																					result_row.append(tile[:, :, :row_limit, :row_limit])
															result_rows.append(torch.cat(lowerCAmelCase__       ,						dim=3))

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.cat(lowerCAmelCase__       ,						dim=2)
									SCREAMING_SNAKE_CASE_:			str  		=	DiagonalGaussianDistribution(lowerCAmelCase__)

									if not return_dict:
															return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					bool = True):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
									SCREAMING_SNAKE_CASE_:			Tuple  		=	int(self.tile_sample_min_size * self.tile_overlap_factor)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.tile_sample_min_size - blend_extent

									# Split z into overlapping 64x64 tiles and decode them separately.
									# The tiles have an overlap to avoid seams between tiles.
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[]
									for i in range(0       ,						z.shape[2]       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[]
															for j in range(0       ,						z.shape[3]       ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.post_quant_conv(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.decoder(lowerCAmelCase__)
																					row.append(lowerCAmelCase__)
															rows.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
									for i, row in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			int  		=	[]
															for j, tile in enumerate(lowerCAmelCase__):
																					# blend the above tile and the left tile
																					# to the current tile and add the current tile to the result row
																					if i > 0:
																											SCREAMING_SNAKE_CASE_:			Tuple  		=	self.blend_v(rows[i - 1][j]       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
																					if j > 0:
																											SCREAMING_SNAKE_CASE_:			List[str]  		=	self.blend_h(row[j - 1]       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
																					result_row.append(tile[:, :, :row_limit, :row_limit])
															result_rows.append(torch.cat(lowerCAmelCase__       ,						dim=3))

									SCREAMING_SNAKE_CASE_:			int  		=	torch.cat(lowerCAmelCase__       ,						dim=2)
									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					bool = False       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[torch.Generator] = None       ,						):
									SCREAMING_SNAKE_CASE_:			int  		=	sample
									SCREAMING_SNAKE_CASE_:			int  		=	self.encode(lowerCAmelCase__).latent_dist
									if sample_posterior:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	posterior.sample(generator=lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			str  		=	posterior.mode()
									SCREAMING_SNAKE_CASE_:			int  		=	self.decode(lowerCAmelCase__).sample

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=lowerCAmelCase__)









import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM



@require_torch
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			str  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			int  		=	TextStreamer(lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			int  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(greedy_ids[0])

									SCREAMING_SNAKE_CASE_:			int  		=	TextIteratorStreamer(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	""
									for new_text in streamer:
															streamer_text += new_text

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	greedy_ids[:, input_ids.shape[1] :]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.decode(new_greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Dict  		=	TextStreamer(lowerCAmelCase__       ,						skip_prompt=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Any  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
									# with actual models -- the dummy models' tokenizers are not aligned with their models, and
									# `skip_special_tokens=True` has no effect on them
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained("distilgpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.ones((1, 5)       ,						device=lowerCAmelCase__).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TextStreamer(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=1       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									SCREAMING_SNAKE_CASE_:			str  		=	cs.out[:-1]  # Remove the final "\n"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__       ,						return_tensors="pt")
									self.assertEqual(streamer_text_tokenized.input_ids.shape       ,						(1, 1))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	TextIteratorStreamer(lowerCAmelCase__       ,						timeout=0.001)
									SCREAMING_SNAKE_CASE_:			Any  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	""
															for new_text in streamer:
																					streamer_text += new_text






