






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A_		:		int			= [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

A_		:		Optional[Any]			= get_logger(__name__)


A_		:		Dict			= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}

def   snake_case					(UpperCAmelCase__  )       ->				Any:
  UpperCamelCase_:					Union[str, Any]          =				task_template_dict.get('task'  )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_lowercase  )}'''  )
    return None
  UpperCamelCase_:					str          =				NAMEaTEMPLATE.get(_lowercase  )
  return template.from_dict(_lowercase  )







import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[Any]						,   __A:      bool = False      )			-> Union[str, Any]:
												_A									=       10

												if only_cross_attention:
																	_A									=       12
												else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_A									=       query_dim

												return {
												    "query_dim": query_dim,
												    "cross_attention_dim": cross_attention_dim,
												    "heads": 2,
												    "dim_head": 4,
												    "added_kv_proj_dim": 6,
												    "norm_num_groups": 1,
												    "only_cross_attention": only_cross_attention,
												    "processor": AttnAddedKVProcessor(),
												}

							def 					__A       (     self:      List[Any]						,   __A:      str						,   __A:      Union[str, Any]      )			-> Union[str, Any]:
												_A									=       2

												_A									=       torch.rand(__A						,   __A						,   3						,   2      )
												_A									=       torch.rand(__A						,   4						,   __A      )
												_A									=       None

												return {
												    "hidden_states": hidden_states,
												    "encoder_hidden_states": encoder_hidden_states,
												    "attention_mask": attention_mask,
												}



							def 					__A       (     self:      Optional[int]      )			-> Any:
												# self and cross attention

												torch.manual_seed(0      )

												_A									=       self.get_constructor_arguments(only_cross_attention=__A      )
												_A									=       Attention(**__A      )

												self.assertTrue(attn.to_k is not None      )
												self.assertTrue(attn.to_v is not None      )

												_A									=       self.get_forward_arguments(
												    query_dim=constructor_args['''query_dim''']						,   added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']      )

												_A									=       attn(**__A      )

												# only self attention

												torch.manual_seed(0      )

												_A									=       self.get_constructor_arguments(only_cross_attention=__A      )
												_A									=       Attention(**__A      )

												self.assertTrue(attn.to_k is None      )
												self.assertTrue(attn.to_v is None      )

												_A									=       self.get_forward_arguments(
												    query_dim=constructor_args['''query_dim''']						,   added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']      )

												_A									=       attn(**__A      )

												self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Any      )			-> Dict:
												_A									=       DiffusionPipeline.from_pretrained('''hf-internal-testing/tiny-stable-diffusion-pipe'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												# the initial conversion succeeds
												_A									=       DiffusionPipeline.from_pretrained(
												    '''hf-internal-testing/tiny-stable-diffusion-pipe'''						,   device_map='''sequential'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__A      )

																	# can also load the converted weights
																	_A									=       DiffusionPipeline.from_pretrained(__A						,   device_map='''sequential'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												self.assertTrue(np.allclose(__A						,   __A						,   atol=1e-5      )      )
												self.assertTrue(np.allclose(__A						,   __A						,   atol=1e-5      )      )



