


'''simple docstring'''





import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
        RwkvForCausalLM,
        RwkvModel,
    )
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
    __lowerCamelCase       				=	False




class 							UpperCAmelCase :


 def __init__(			self       :  int ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  int=1_4 ,					__lowerCamelCase       :  Tuple=7 ,					__lowerCamelCase       :  int=True ,					__lowerCamelCase       :  Tuple=False ,					__lowerCamelCase       :  Any=True ,					__lowerCamelCase       :  List[Any]=True ,					__lowerCamelCase       :  Dict=True ,					__lowerCamelCase       :  List[Any]=9_9 ,					__lowerCamelCase       :  Dict=3_2 ,					__lowerCamelCase       :  Optional[int]=5 ,					__lowerCamelCase       :  Union[str, Any]=3_7 ,					__lowerCamelCase       :  Dict="gelu" ,					__lowerCamelCase       :  Any=0.1 ,					__lowerCamelCase       :  Any=0.1 ,					__lowerCamelCase       :  Optional[int]=5_1_2 ,					__lowerCamelCase       :  Tuple=1_6 ,					__lowerCamelCase       :  str=2 ,					__lowerCamelCase       :  str=3 ,					__lowerCamelCase       :  Any=4 ,					__lowerCamelCase       :  Tuple=None ,					):
    UpperCAmelCase__  :Any            =			parent
    UpperCAmelCase__  :Optional[Any]            =			batch_size
    UpperCAmelCase__  :str            =			seq_length
    UpperCAmelCase__  :Optional[int]            =			is_training
    UpperCAmelCase__  :Optional[Any]            =			use_token_type_ids
    UpperCAmelCase__  :Dict            =			use_input_mask
    UpperCAmelCase__  :List[Any]            =			use_labels
    UpperCAmelCase__  :List[str]            =			use_mc_token_ids
    UpperCAmelCase__  :List[Any]            =			vocab_size
    UpperCAmelCase__  :Dict            =			hidden_size
    UpperCAmelCase__  :Union[str, Any]            =			num_hidden_layers
    UpperCAmelCase__  :List[Any]            =			intermediate_size
    UpperCAmelCase__  :int            =			hidden_act
    UpperCAmelCase__  :Optional[Any]            =			hidden_dropout_prob
    UpperCAmelCase__  :Optional[int]            =			attention_probs_dropout_prob
    UpperCAmelCase__  :str            =			max_position_embeddings
    UpperCAmelCase__  :int            =			type_vocab_size
    UpperCAmelCase__  :List[Any]            =			type_sequence_label_size
    UpperCAmelCase__  :int            =			num_labels
    UpperCAmelCase__  :str            =			num_choices
    UpperCAmelCase__  :Optional[int]            =			scope
    UpperCAmelCase__  :Optional[Any]            =			vocab_size - 1
    UpperCAmelCase__  :str            =			vocab_size - 1
    UpperCAmelCase__  :Tuple            =			vocab_size - 1


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple ,					__lowerCamelCase       :  Dict=False ,					__lowerCamelCase       :  Dict=False ,					__lowerCamelCase       :  List[str]=False   ):
    UpperCAmelCase__  :Union[str, Any]            =			ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

    UpperCAmelCase__  :Dict            =			None
    if self.use_input_mask:
       UpperCAmelCase__  :Optional[int]            =			random_attention_mask([self.batch_size, self.seq_length]   )

    UpperCAmelCase__  :Tuple            =			None
    if self.use_token_type_ids:
       UpperCAmelCase__  :Any            =			ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size   )

    UpperCAmelCase__  :str            =			None
    if self.use_mc_token_ids:
       UpperCAmelCase__  :Optional[Any]            =			ids_tensor([self.batch_size, self.num_choices] ,					self.seq_length   )

    UpperCAmelCase__  :Optional[Any]            =			None
    UpperCAmelCase__  :List[Any]            =			None
    UpperCAmelCase__  :Any            =			None
    if self.use_labels:
       UpperCAmelCase__  :Optional[Any]            =			ids_tensor([self.batch_size] ,					self.type_sequence_label_size   )
       UpperCAmelCase__  :int            =			ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels   )
       UpperCAmelCase__  :Tuple            =			ids_tensor([self.batch_size] ,					self.num_choices   )

    UpperCAmelCase__  :Optional[int]            =			self.get_config(
        gradient_checkpointing=__lowerCamelCase ,					scale_attn_by_inverse_layer_idx=__lowerCamelCase ,					reorder_and_upcast_attn=__lowerCamelCase ,					)

    return (
        config,
        input_ids,
        input_mask,
        None,
        token_type_ids,
        mc_token_ids,
        sequence_labels,
        token_labels,
        choice_labels,
    )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  Union[str, Any]=False ,					__lowerCamelCase       :  int=False ,					__lowerCamelCase       :  int=False   ):
    return RwkvConfig(
        vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					intermediate_size=self.intermediate_size ,					activation_function=self.hidden_act ,					resid_pdrop=self.hidden_dropout_prob ,					attn_pdrop=self.attention_probs_dropout_prob ,					n_positions=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					use_cache=__lowerCamelCase ,					bos_token_id=self.bos_token_id ,					eos_token_id=self.eos_token_id ,					pad_token_id=self.pad_token_id ,					gradient_checkpointing=__lowerCamelCase ,					scale_attn_by_inverse_layer_idx=__lowerCamelCase ,					reorder_and_upcast_attn=__lowerCamelCase ,					)


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :int            =			self.get_config()
    UpperCAmelCase__  :Any            =			3_0_0
    return config


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    (
        (
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	
    )  :Dict            =			self.prepare_config_and_inputs()

    UpperCAmelCase__  :List[str]            =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
    UpperCAmelCase__  :Dict            =			ids_tensor([self.batch_size, self.seq_length] ,					vocab_size=2   )

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
        token_type_ids,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Optional[int] ,					*__lowerCamelCase       :  Optional[Any]   ):
    UpperCAmelCase__  :str            =			True
    UpperCAmelCase__  :Optional[Any]            =			RwkvModel(config=__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    model.eval()

    UpperCAmelCase__  :Optional[int]            =			model(__lowerCamelCase   )

    self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)   )
    self.parent.assertEqual(len(result.hidden_states   ) ,					config.num_hidden_layers + 1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Union[str, Any] ,					*__lowerCamelCase       :  List[Any]   ):
    UpperCAmelCase__  :int            =			RwkvForCausalLM(__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    model.eval()

    UpperCAmelCase__  :int            =			model(__lowerCamelCase ,					labels=__lowerCamelCase   )
    self.parent.assertEqual(result.loss.shape ,					()   )
    self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int] ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Tuple ,					*__lowerCamelCase       :  Optional[Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			RwkvModel(config=__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    model.eval()

    UpperCAmelCase__  :Tuple            =			model(__lowerCamelCase   )
    UpperCAmelCase__  :int            =			outputs.last_hidden_state

    UpperCAmelCase__  :Tuple            =			model(input_ids[:, :2]   )
    UpperCAmelCase__  :List[Any]            =			outputs.last_hidden_state

    # Using the state computed on the first inputs, we will get the same output
    UpperCAmelCase__  :Any            =			model(input_ids[:, 2:] ,					state=outputs.state   )
    UpperCAmelCase__  :Any            =			outputs.last_hidden_state

    self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two] ,					dim=1   ) ,					__lowerCamelCase ,					atol=1e-5   )   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  List[str] ,					__lowerCamelCase       :  Any ,					*__lowerCamelCase       :  Any ,					__lowerCamelCase       :  Tuple=False   ):
    UpperCAmelCase__  :Any            =			RwkvForCausalLM(__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    if gradient_checkpointing:
       model.gradient_checkpointing_enable()

    UpperCAmelCase__  :Union[str, Any]            =			model(__lowerCamelCase ,					labels=__lowerCamelCase   )
    self.parent.assertEqual(result.loss.shape ,					()   )
    self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)   )
    result.loss.backward()





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			self.prepare_config_and_inputs()

    (
        (
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	
    )  :Any            =			config_and_inputs

    UpperCAmelCase__  :int            =			{'''input_ids''': input_ids}

    return config, inputs_dict




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,   reason="See https://github.com/huggingface/transformers/pull/24204"	)
@require_torch
class 							UpperCAmelCase ( _snake_case	,   _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
 UpperCAmelCase				=				(
     {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
 )
 # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False  # Rwkv does not support head masking


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Dict            =			RwkvModelTester(self   )
    UpperCAmelCase__  :Union[str, Any]            =			ConfigTester(
        self ,					config_class=__lowerCamelCase ,					n_embd=3_7 ,					common_properties=['''hidden_size''', '''num_hidden_layers''']   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Optional[int]=None   ):
    if isinstance(__lowerCamelCase ,					torch.Tensor   ):
       UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			member.max().item(), member.min().item()
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase   ) or isinstance(__lowerCamelCase ,					__lowerCamelCase   ):
       UpperCAmelCase__      ,	UpperCAmelCase__  :List[Any]            =			max(__lowerCamelCase   ), min(__lowerCamelCase   )

    if not isinstance(__lowerCamelCase ,					__lowerCamelCase   ):
       raise TypeError('''container should be a list or tuple'''   )
    elif len(__lowerCamelCase   ) != 2:
       raise ValueError('''container should have 2 elements'''   )

    UpperCAmelCase__      ,	UpperCAmelCase__  :Any            =			container

    UpperCAmelCase__  :List[str]            =			(min_value >= expected_min) and (max_value <= expected_max)

    if not is_inside_interval:
       UpperCAmelCase__  :int            =			'''%s not found in %s''' % (safe_repr(__lowerCamelCase   ), safe_repr(__lowerCamelCase   ))
       self.fail(self._formatMessage(__lowerCamelCase ,					__lowerCamelCase   )   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    self.config_tester.run_common_tests()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :Tuple            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_rwkv_model(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :str            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_causl_lm(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :Union[str, Any]            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_state_equivalency(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCAmelCase__  :Optional[int]            =			model_class(config=__lowerCamelCase   )
       for name, param in model.named_parameters():
          if "time_decay" in name:
             if param.requires_grad:
                self.assertTrue(param.data.max().item() == 3.0   )
                self.assertTrue(param.data.min().item() == -5.0   )
          elif "time_first" in name:
             if param.requires_grad:
                # check if it's a ones like
                self.assertTrue(torch.allclose(param.data ,					torch.ones_like(param.data   ) ,					atol=1e-5 ,					rtol=1e-5   )   )
          elif any(x in name for x in ['''time_mix_key''', '''time_mix_receptance''']   ):
             if param.requires_grad:
                self.assertInterval(
                    param.data ,					[0.0, 1.0] ,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,					)
          elif "time_mix_value" in name:
             if param.requires_grad:
                self.assertInterval(
                    param.data ,					[0.0, 1.3] ,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,					)


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :int            =			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCAmelCase__  :Dict            =			True

    UpperCAmelCase__  :Dict            =			getattr(self.model_tester ,					'''seq_length''' ,					__lowerCamelCase   )

    for model_class in self.all_model_classes:
       UpperCAmelCase__  :Tuple            =			True
       UpperCAmelCase__  :Optional[int]            =			False
       UpperCAmelCase__  :List[Any]            =			True
       UpperCAmelCase__  :Optional[int]            =			model_class(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()

       UpperCAmelCase__  :List[Any]            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Tuple            =			inputs['''input_ids'''].shape[0]
       with torch.no_grad():
          UpperCAmelCase__  :List[str]            =			model(**__lowerCamelCase   )
       UpperCAmelCase__  :int            =			outputs.attentions
       self.assertEqual(len(__lowerCamelCase   ) ,					self.model_tester.num_hidden_layers   )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       UpperCAmelCase__  :Optional[Any]            =			True
       UpperCAmelCase__  :Union[str, Any]            =			model_class(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()

       UpperCAmelCase__  :List[str]            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Optional[int]            =			inputs['''input_ids'''].shape[0]
       with torch.no_grad():
          UpperCAmelCase__  :Tuple            =			model(**__lowerCamelCase   )
       UpperCAmelCase__  :Union[str, Any]            =			outputs.attentions
       self.assertEqual(len(__lowerCamelCase   ) ,					self.model_tester.num_hidden_layers   )

       self.assertListEqual(
           list(attentions[0].shape[-3:]   ) ,					[batch_size, seq_len, config.hidden_size] ,					)
       UpperCAmelCase__  :Dict            =			len(__lowerCamelCase   )

       # Check attention is always last and order is fine
       UpperCAmelCase__  :int            =			True
       UpperCAmelCase__  :Tuple            =			True
       UpperCAmelCase__  :List[Any]            =			model_class(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()

       UpperCAmelCase__  :Optional[int]            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :int            =			inputs['''input_ids'''].shape[0]
       with torch.no_grad():
          UpperCAmelCase__  :Optional[int]            =			model(**__lowerCamelCase   )

       UpperCAmelCase__  :List[Any]            =			1
       self.assertEqual(out_len + added_hidden_states ,					len(__lowerCamelCase   )   )

       UpperCAmelCase__  :str            =			outputs.attentions

       self.assertEqual(len(__lowerCamelCase   ) ,					self.model_tester.num_hidden_layers   )
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]   ) ,					[batch_size, seq_len, config.hidden_size] ,					)





 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase__  :List[Any]            =			RwkvModel.from_pretrained(__lowerCamelCase   )
       self.assertIsNotNone(__lowerCamelCase   )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,   reason="See https://github.com/huggingface/transformers/pull/24204"	)
@slow
class 							UpperCAmelCase ( unittest.TestCase	):


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :List[Any]            =			'''RWKV/rwkv-4-169m-pile'''
    UpperCAmelCase__  :str            =			AutoTokenizer.from_pretrained(self.model_id   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :Optional[int]            =			'''Hello my name is Jasmine and I am a newbie to the'''
    UpperCAmelCase__  :Union[str, Any]            =			RwkvForCausalLM.from_pretrained(self.model_id   ).to(__lowerCamelCase   )

    UpperCAmelCase__  :List[str]            =			self.tokenizer('''Hello my name is''' ,					return_tensors='''pt'''   ).input_ids.to(__lowerCamelCase   )
    UpperCAmelCase__  :str            =			model.generate(__lowerCamelCase ,					max_new_tokens=1_0   )
    UpperCAmelCase__  :str            =			self.tokenizer.decode(output[0].tolist()   )

    self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :str            =			'''Hello my name is Jasmine and I am a newbie to the'''

    UpperCAmelCase__  :int            =			self.tokenizer('''Hello my name is''' ,					return_tensors='''pt'''   ).input_ids.to(__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			RwkvForCausalLM.from_pretrained(self.model_id ,					torch_dtype=torch.bfloataa   ).to(__lowerCamelCase   )

    UpperCAmelCase__  :str            =			model.generate(__lowerCamelCase ,					max_new_tokens=1_0   )
    UpperCAmelCase__  :int            =			self.tokenizer.decode(output[0].tolist()   )

    self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							UpperCAmelCase ( _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				StableDiffusionParadigmsPipeline
 UpperCAmelCase				=				TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    torch.manual_seed(0   )
    UpperCAmelCase__  :Optional[int]            =			UNetaDConditionModel(
        block_out_channels=(3_2, 6_4) ,					layers_per_block=2 ,					sample_size=3_2 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,					cross_attention_dim=3_2 ,					attention_head_dim=(2, 4) ,					use_linear_projection=__lowerCamelCase ,					)
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler(
        beta_start=0.0_00_85 ,					beta_end=0.0_12 ,					beta_schedule='''scaled_linear''' ,					clip_sample=__lowerCamelCase ,					set_alpha_to_one=__lowerCamelCase ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			AutoencoderKL(
        block_out_channels=[3_2, 6_4] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,					latent_channels=4 ,					sample_size=1_2_8 ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			CLIPTextConfig(
        bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=3_2 ,					intermediate_size=3_7 ,					layer_norm_eps=1e-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_0_0_0 ,					hidden_act='''gelu''' ,					projection_dim=5_1_2 ,					)
    UpperCAmelCase__  :List[Any]            =			CLIPTextModel(__lowerCamelCase   )
    UpperCAmelCase__  :Tuple            =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

    UpperCAmelCase__  :int            =			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  Any=0   ):
    if str(__lowerCamelCase   ).startswith('''mps'''   ):
       UpperCAmelCase__  :str            =			torch.manual_seed(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :Union[str, Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Dict            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    UpperCAmelCase__  :str            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Union[str, Any]            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[Any]            =			np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]   )

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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Any            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			DDPMParallelScheduler()
    torch.manual_seed(0   )
    UpperCAmelCase__  :List[str]            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Dict            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[str]            =			np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]   )

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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().test_inference_batch_consistent(batch_sizes=[1, 2]   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    super().test_inference_batch_single_identical(batch_size=2 ,					expected_max_diff=3e-3   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :int            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :str            =			self.get_dummy_components()
    UpperCAmelCase__  :Any            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Tuple            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			'''french fries'''
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase ,					negative_prompt=__lowerCamelCase   )
    UpperCAmelCase__  :int            =			output.images
    UpperCAmelCase__  :Tuple            =			image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :Tuple            =			np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]   )

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




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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Tuple=0   ):
    UpperCAmelCase__  :List[Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 7.5,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs





 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :Dict            =			'''stabilityai/stable-diffusion-2-base'''
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler.from_pretrained(__lowerCamelCase ,					subfolder='''scheduler'''   )
    UpperCAmelCase__  :Dict            =			StableDiffusionParadigmsPipeline.from_pretrained(__lowerCamelCase ,					scheduler=__lowerCamelCase ,					safety_checker=__lowerCamelCase   )
    pipe.to(__lowerCamelCase   )
    pipe.set_progress_bar_config(disable=__lowerCamelCase   )
    pipe.enable_attention_slicing()

    UpperCAmelCase__  :Dict            =			self.get_inputs()
    UpperCAmelCase__  :int            =			pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :List[str]            =			image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase__  :Optional[int]            =			np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]   )

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



