


import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()


class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      LDMTextToImagePipeline
     _lowerCamelCase   =      TEXT_TO_IMAGE_PARAMS - {
         """negative_prompt""",
         """negative_prompt_embeds""",
         """cross_attention_kwargs""",
         """prompt_embeds""",
     }
     _lowerCamelCase   =      PipelineTesterMixin.required_optional_params - {
         """num_images_per_prompt""",
         """callback""",
         """callback_steps""",
     }
     _lowerCamelCase   =      TEXT_TO_IMAGE_BATCH_PARAMS
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        __a					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        torch.manual_seed(0    )
        __a					=  AutoencoderKL(
            block_out_channels=(32, 64)  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")  ,       up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")  ,       latent_channels=4  ,       )
        torch.manual_seed(0    )
        __a					=  CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1E-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=1000  ,       )
        __a					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  {
            """unet""": unet,
            """scheduler""": scheduler,
            """vqvae""": vae,
            """bert""": text_encoder,
            """tokenizer""": tokenizer,
        }
        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )
        __a					=  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator

        __a					=  self.get_dummy_components()
        __a					=  LDMTextToImagePipeline(**__A    )
        pipe.to(__A    )
        pipe.set_progress_bar_config(disable=__A    )

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

        assert image.shape == (1, 16, 16, 3)
        __a					=  np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3







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


     """simple docstring"""


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


     def 	snake_case_  (	self  ,       __A  ,       __A=torch.floataa  ,       __A=0    ):
        __a					=  torch.manual_seed(__A    )
        __a					=  np.random.RandomState(__A    ).standard_normal((1, 4, 32, 32)    )
        __a					=  torch.from_numpy(__A    ).to(device=__A  ,       dtype=__A    )
        __a					=  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs


     def 	snake_case_  (	self    ):
        __a					=  LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""    ).to(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_inputs(__A    )
        __a					=  pipe(**__A    ).images
        __a					=  image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 256, 256, 3)
        __a					=  np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]    )
        __a					=  np.abs(expected_slice - image_slice    ).max()
        assert max_diff < 1E-3







@nightly
@require_torch_gpu
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


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


     def 	snake_case_  (	self  ,       __A  ,       __A=torch.floataa  ,       __A=0    ):
        __a					=  torch.manual_seed(__A    )
        __a					=  np.random.RandomState(__A    ).standard_normal((1, 4, 32, 32)    )
        __a					=  torch.from_numpy(__A    ).to(device=__A  ,       dtype=__A    )
        __a					=  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 50,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs


     def 	snake_case_  (	self    ):
        __a					=  LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""    ).to(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_inputs(__A    )
        __a					=  pipe(**__A    ).images[0]

        __a					=  load_numpy(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""    )
        __a					=  np.abs(expected_image - image    ).max()
        assert max_diff < 1E-3









"""simple docstring"""

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

