





"""simple docstring"""




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    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    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					List[str]             =  StableDiffusionXLPipeline
      lowercase_ :					Any             =  TEXT_TO_IMAGE_PARAMS
      lowercase_ :					Optional[int]             =  TEXT_TO_IMAGE_BATCH_PARAMS
      lowercase_ :					Optional[Any]             =  TEXT_TO_IMAGE_IMAGE_PARAMS
      lowercase_ :					Any             =  TEXT_TO_IMAGE_IMAGE_PARAMS

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )
        A_    :			Union[str, Any]									=    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')   , attention_head_dim=(2, 4)   , use_linear_projection=snake_case_   , addition_embed_type='text_time'   , addition_time_embed_dim=8   , transformer_layers_per_block=(1, 2)   , projection_class_embeddings_input_dim=8_0   , cross_attention_dim=6_4   , )
        A_    :			str									=    EulerDiscreteScheduler(
            beta_start=0.0_00_85   , beta_end=0.0_12   , steps_offset=1   , beta_schedule='scaled_linear'   , timestep_spacing='leading'   , )
        torch.manual_seed(0    )
        A_    :			Optional[int]									=    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    )
        A_    :			List[str]									=    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=3_2   , )
        A_    :			List[str]									=    CLIPTextModel(snake_case_    )
        A_    :			List[Any]									=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   , local_files_only=snake_case_    )

        A_    :			Union[str, Any]									=    CLIPTextModelWithProjection(snake_case_    )
        A_    :			int									=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   , local_files_only=snake_case_    )

        A_    :			List[Any]									=    {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'text_encoder_2': text_encoder_a,
            'tokenizer_2': tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=0    ):

        """simple docstring"""

        if str(snake_case_    ).startswith('mps'    ):
          A_    :			List[Any]									=    torch.manual_seed(snake_case_    )
        else:
          A_    :			Tuple									=    torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
        A_    :			Dict									=    {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 5.0,
            'output_type': 'numpy',
        }
        return inputs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    'cpu'  # ensure determinism for the device-dependent torch.Generator
        A_    :			List[Any]									=    self.get_dummy_components()
        A_    :			str									=    StableDiffusionXLPipeline(**snake_case_    )
        A_    :			Dict									=    sd_pipe.to(snake_case_    )
        sd_pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			str									=    self.get_dummy_inputs(snake_case_    )
        A_    :			List[str]									=    sd_pipe(**snake_case_    ).images
        A_    :			int									=    image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)
        A_    :			List[str]									=    np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]    )

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    self.get_dummy_components()
        A_    :			Tuple									=    StableDiffusionXLPipeline(**snake_case_    )
        A_    :			List[str]									=    sd_pipe.to(snake_case_    )
        A_    :			List[Any]									=    sd_pipe.to(snake_case_    )
        sd_pipe.set_progress_bar_config(disable=snake_case_    )

        # forward without prompt embeds
        A_    :			List[Any]									=    self.get_dummy_inputs(snake_case_    )
        A_    :			Dict									=    3 * ['this is a negative prompt']
        A_    :			str									=    negative_prompt
        A_    :			Optional[int]									=    3 * [inputs['prompt']]

        A_    :			List[str]									=    sd_pipe(**snake_case_    )
        A_    :			List[str]									=    output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        A_    :			int									=    self.get_dummy_inputs(snake_case_    )
        A_    :			Union[str, Any]									=    3 * ['this is a negative prompt']
        A_    :			List[str]									=    3 * [inputs.pop('prompt'    )]

        (
            (
            A_
        )		,			(
            A_
        )		,			(
            A_
        )		,			(
            A_
        )		,			
        )    :			Optional[int]									=    sd_pipe.encode_prompt(snake_case_   , negative_prompt=snake_case_    )

        A_    :			List[str]									=    sd_pipe(
            **snake_case_   , prompt_embeds=snake_case_   , negative_prompt_embeds=snake_case_   , pooled_prompt_embeds=snake_case_   , negative_pooled_prompt_embeds=snake_case_   , )
        A_    :			Any									=    output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        super().test_attention_slicing_forward_pass(expected_max_diff=3E-3    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        super().test_inference_batch_single_identical(expected_max_diff=3E-3    )


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



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_="cpu"   , snake_case_=torch.floataa   , snake_case_=0    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
        A_    :			Union[str, Any]									=    np.random.RandomState(snake_case_    ).standard_normal((1, 4, 6_4, 6_4)    )
        A_    :			Union[str, Any]									=    torch.from_numpy(snake_case_    ).to(device=snake_case_   , dtype=snake_case_    )
        A_    :			Union[str, Any]									=    {
            'prompt': 'a photograph of an astronaut riding a horse',
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'    )
        pipe.to(snake_case_    )
        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			int									=    self.get_inputs(snake_case_    )
        A_    :			Union[str, Any]									=    pipe(**snake_case_    ).images
        A_    :			int									=    image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        A_    :			Optional[int]									=    np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]    )
        assert np.abs(image_slice - expected_slice    ).max() < 7E-3






"""simple docstring"""




import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       CpmAntConfig,
       CpmAntForCausalLM,
       CpmAntModel,
       CpmAntTokenizer,
   )

@require_torch
class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=2   , snake_case_=8   , snake_case_=True   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=9_9   , snake_case_=3_2   , snake_case_=3   , snake_case_=4   , snake_case_=3_7   , snake_case_=3_2   , snake_case_=1_2_8   , snake_case_=8   , snake_case_=8   , snake_case_=8   , snake_case_=1.0   , snake_case_=True   , ):

        """simple docstring"""

        A_    :			List[Any]									=    parent
        A_    :			Union[str, Any]									=    batch_size
        A_    :			Dict									=    seq_length
        A_    :			Tuple									=    is_training
        A_    :			Union[str, Any]									=    use_token_type_ids
        A_    :			Optional[Any]									=    use_input_mask
        A_    :			str									=    use_labels
        A_    :			Optional[int]									=    use_mc_token_ids
        A_    :			str									=    vocab_size
        A_    :			Any									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			str									=    num_attention_heads
        A_    :			Optional[Any]									=    intermediate_size
        A_    :			List[str]									=    num_buckets
        A_    :			Tuple									=    max_distance
        A_    :			Optional[int]									=    prompt_length
        A_    :			List[str]									=    prompt_types
        A_    :			Dict									=    segment_types
        A_    :			List[str]									=    init_std
        A_    :			Union[str, Any]									=    return_dict

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    {}
        A_    :			Any									=    ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size    ).type(torch.intaa    )
        A_    :			Union[str, Any]									=    False

        A_    :			Optional[int]									=    self.get_config()

        return (config, input_ids)

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return CpmAntConfig(
            vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , num_hidden_layers=self.num_hidden_layers   , num_attention_heads=self.num_attention_heads   , dim_ff=self.intermediate_size   , position_bias_num_buckets=self.num_buckets   , position_bias_max_distance=self.max_distance   , prompt_types=self.prompt_types   , prompt_length=self.prompt_length   , segment_types=self.segment_types   , use_cache=snake_case_   , init_std=self.init_std   , return_dict=self.return_dict   , )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Tuple									=    CpmAntModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()

        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        self.parent.assertEqual(hidden_states.shape   , (self.batch_size, self.seq_length, config.hidden_size)    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntForCausalLM(snake_case_    )
        model.to(snake_case_    )
        A_    :			str									=    input_ids['input_ids'].to(snake_case_    )
        model.eval()

        A_    :			int									=    model(**snake_case_    )
        self.parent.assertEqual(
            model_output.logits.shape   , (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Any             =  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
      lowercase_ :					int             =  (
          {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
      )

      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Dict             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Union[str, Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntModelTester(self    )
        A_    :			Optional[int]									=    ConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        unittest.skip('CPMAnt doesn\'t support input_embeds.'    )(self.test_inputs_embeds    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        unittest.skip(
            'CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'    )(self.test_retain_grad_hidden_states_attentions    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			List[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(snake_case_   , snake_case_    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Tuple									=    'openbmb/cpm-ant-10b'
        A_    :			Tuple									=    CpmAntModel.from_pretrained(snake_case_    )
        A_    :			Dict									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        A_    :			Optional[int]									=    torch.tensor(
            [[[6.17_08, 5.92_44, 1.08_35], [6.52_07, 6.28_93, -11.33_24], [-1.01_07, -0.05_76, -5.95_77]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Union[str, Any]									=    'openbmb/cpm-ant-10b'
        A_    :			List[str]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Union[str, Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Tuple									=    model(**snake_case_    ).logits

        A_    :			Union[str, Any]									=    torch.tensor(
            [[[-6.42_67, -6.40_83, -6.39_58], [-5.88_02, -5.94_47, -5.78_11], [-5.38_96, -5.48_20, -5.42_95]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			Optional[int]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			List[str]									=    '今天天气不错，'
        A_    :			Dict									=    '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Dict									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			List[Any]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    ['今天天气不错，', '新年快乐，万事如意！']
        A_    :			List[Any]									=    [
            '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的',
            '新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的',
        ]
        A_    :			Tuple									=    tokenizer(snake_case_   , return_tensors='pt'   , padding=snake_case_    )
        A_    :			int									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )