




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			int =				CycleDiffusionPipeline
    __lowerCamelCase      :			Union[str, Any] =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        "negative_prompt",
        "height",
        "width",
        "negative_prompt_embeds",
    }
    __lowerCamelCase      :			Optional[int] =				PipelineTesterMixin.required_optional_params - {"latents"}
    __lowerCamelCase      :			List[Any] =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
    __lowerCamelCase      :			Union[str, Any] =				IMAGE_TO_IMAGE_IMAGE_PARAMS
    __lowerCamelCase      :			str =				IMAGE_TO_IMAGE_IMAGE_PARAMS






    def       _lowerCAmelCase (	self		):
        torch.manual_seed(0		)
        A			:	Tuple										=       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			:	Optional[Any]										=       DDIMScheduler(
            beta_start=0.0_0085,		beta_end=0.012,		beta_schedule="""scaled_linear""",		num_train_timesteps=1000,		clip_sample=lowerCamelCase__,		set_alpha_to_one=lowerCamelCase__,		)
        torch.manual_seed(0		)
        A			:	int										=       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			:	List[Any]										=       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			:	int										=       CLIPTextModel(lowerCamelCase__		)
        A			:	Union[str, Any]										=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

        A			:	Optional[int]										=       {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A			:	Dict										=       floats_tensor((1, 3, 32, 32),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)
        A			:	List[str]										=       image / 2 + 0.5
        if str(lowerCamelCase__		).startswith("""mps"""		):
            A			:	Tuple										=       torch.manual_seed(lowerCamelCase__		)
        else:
            A			:	Union[str, Any]										=       torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
        A			:	Union[str, Any]										=       {
            """prompt""": """An astronaut riding an elephant""",
            """source_prompt""": """An astronaut riding a horse""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """eta""": 0.1,
            """strength""": 0.8,
            """guidance_scale""": 3,
            """source_guidance_scale""": 1,
            """output_type""": """numpy""",
        }
        return inputs






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       """cpu"""  # ensure determinism for the device-dependent torch.Generator

        A			:	Any										=       self.get_dummy_components()
        A			:	Optional[int]										=       CycleDiffusionPipeline(**lowerCamelCase__		)
        A			:	List[Any]										=       pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A			:	List[str]										=       self.get_dummy_inputs(lowerCamelCase__		)
        A			:	str										=       pipe(**lowerCamelCase__		)
        A			:	Tuple										=       output.images

        A			:	str										=       images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        A			:	List[Any]										=       np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]		)

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






    @unittest.skipIf(torch_device != """cuda""",		"""This test requires a GPU"""		)
    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.get_dummy_components()
        for name, module in components.items():
            if hasattr(lowerCamelCase__,		"""half"""		):
                A			:	Any										=       module.half()
        A			:	Tuple										=       CycleDiffusionPipeline(**lowerCamelCase__		)
        A			:	Optional[Any]										=       pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A			:	Union[str, Any]										=       self.get_dummy_inputs(lowerCamelCase__		)
        A			:	Union[str, Any]										=       pipe(**lowerCamelCase__		)
        A			:	Optional[Any]										=       output.images

        A			:	Tuple										=       images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        A			:	int										=       np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]		)

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






    @skip_mps
    def       _lowerCAmelCase (	self		):
        return super().test_save_load_local()






    @unittest.skip("""non-deterministic pipeline"""		)
    def       _lowerCAmelCase (	self		):
        return super().test_inference_batch_single_identical()






    @skip_mps
    def       _lowerCAmelCase (	self		):
        return super().test_dict_tuple_outputs_equivalent()






    @skip_mps
    def       _lowerCAmelCase (	self		):
        return super().test_save_load_optional_components()






    @skip_mps
    def       _lowerCAmelCase (	self		):
        return super().test_attention_slicing_forward_pass()






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




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






    def       _lowerCAmelCase (	self		):
        A			:	str										=       load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""		)
        A			:	str										=       load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""		)
        A			:	Any										=       init_image.resize((512, 512)		)

        A			:	Any										=       """CompVis/stable-diffusion-v1-4"""
        A			:	str										=       DDIMScheduler.from_pretrained(lowerCamelCase__,		subfolder="""scheduler"""		)
        A			:	Any										=       CycleDiffusionPipeline.from_pretrained(
            lowerCamelCase__,		scheduler=lowerCamelCase__,		safety_checker=lowerCamelCase__,		torch_dtype=torch.floataa,		revision="""fp16"""		)

        pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)
        pipe.enable_attention_slicing()

        A			:	Any										=       """A black colored car"""
        A			:	Optional[Any]										=       """A blue colored car"""

        A			:	List[str]										=       torch.manual_seed(0		)
        A			:	Dict										=       pipe(
            prompt=lowerCamelCase__,		source_prompt=lowerCamelCase__,		image=lowerCamelCase__,		num_inference_steps=100,		eta=0.1,		strength=0.85,		guidance_scale=3,		source_guidance_scale=1,		generator=lowerCamelCase__,		output_type="""np""",		)
        A			:	Dict										=       output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image		).max() < 5e-1






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""		)
        A			:	Any										=       load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""		)
        A			:	List[str]										=       init_image.resize((512, 512)		)

        A			:	Tuple										=       """CompVis/stable-diffusion-v1-4"""
        A			:	Union[str, Any]										=       DDIMScheduler.from_pretrained(lowerCamelCase__,		subfolder="""scheduler"""		)
        A			:	Union[str, Any]										=       CycleDiffusionPipeline.from_pretrained(lowerCamelCase__,		scheduler=lowerCamelCase__,		safety_checker=lowerCamelCase__		)

        pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)
        pipe.enable_attention_slicing()

        A			:	Dict										=       """A black colored car"""
        A			:	int										=       """A blue colored car"""

        A			:	Dict										=       torch.manual_seed(0		)
        A			:	Dict										=       pipe(
            prompt=lowerCamelCase__,		source_prompt=lowerCamelCase__,		image=lowerCamelCase__,		num_inference_steps=100,		eta=0.1,		strength=0.85,		guidance_scale=3,		source_guidance_scale=1,		generator=lowerCamelCase__,		output_type="""np""",		)
        A			:	List[Any]										=       output.images

        assert np.abs(image - expected_image		).max() < 2e-2







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				"xlm"
    __lowerCamelCase      :			Tuple =				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(	self,		lowerCamelCase__=3_0145,		lowerCamelCase__=2048,		lowerCamelCase__=12,		lowerCamelCase__=16,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=1,		lowerCamelCase__=True,		lowerCamelCase__=512,		lowerCamelCase__=2048**-0.5,		lowerCamelCase__=1e-12,		lowerCamelCase__=0.02,		lowerCamelCase__=0,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=5,		lowerCamelCase__=True,		lowerCamelCase__="first",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=5,		lowerCamelCase__=5,		lowerCamelCase__=0,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A			:	Dict										=       vocab_size
        A			:	int										=       emb_dim
        A			:	str										=       n_layers
        A			:	Union[str, Any]										=       n_heads
        A			:	Optional[int]										=       dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	Optional[Any]										=       gelu_activation
        A			:	Dict										=       sinusoidal_embeddings
        A			:	int										=       causal
        A			:	Optional[Any]										=       asm
        A			:	Any										=       n_langs
        A			:	List[str]										=       use_lang_emb
        A			:	Union[str, Any]										=       layer_norm_eps
        A			:	str										=       bos_index
        A			:	int										=       eos_index
        A			:	Tuple										=       pad_index
        A			:	str										=       unk_index
        A			:	Optional[Any]										=       mask_index
        A			:	Union[str, Any]										=       is_encoder
        A			:	Tuple										=       max_position_embeddings
        A			:	List[str]										=       embed_init_std
        A			:	Tuple										=       init_std
        A			:	Tuple										=       summary_type
        A			:	int										=       summary_use_proj
        A			:	List[Any]										=       summary_activation
        A			:	Optional[Any]										=       summary_proj_to_labels
        A			:	Optional[Any]										=       summary_first_dropout
        A			:	Optional[int]										=       start_n_top
        A			:	Optional[Any]										=       end_n_top
        A			:	List[str]										=       mask_token_id
        A			:	Tuple										=       lang_id

        if "n_words" in kwargs:
            A			:	List[str]										=       kwargs["""n_words"""]

        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		**lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Any										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	Union[str, Any]										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]		)


