




from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase			:	Optional[Any]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Dict							 =     {
    """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        UpperCamelCase_					(				lowercase_  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''xlm'''
    UpperCAmelCase__													=   {
        '''hidden_size''': '''emb_dim''',
        '''num_attention_heads''': '''n_heads''',
        '''num_hidden_layers''': '''n_layers''',
        '''n_words''': '''vocab_size''',  # For backward compatibility
    }


    def __init__(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Any=30_145      ,				UpperCAmelCase__   :			Optional[Any]=2_048      ,				UpperCAmelCase__   :			Dict=12      ,				UpperCAmelCase__   :			int=16      ,				UpperCAmelCase__   :			List[Any]=0.1      ,				UpperCAmelCase__   :			Optional[int]=0.1      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			List[Any]=False      ,				UpperCAmelCase__   :			str=False      ,				UpperCAmelCase__   :			Dict=False      ,				UpperCAmelCase__   :			str=1      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[int]=512      ,				UpperCAmelCase__   :			Tuple=2_048**-0.5      ,				UpperCAmelCase__   :			str=1e-12      ,				UpperCAmelCase__   :			Dict=0.02      ,				UpperCAmelCase__   :			Optional[int]=0      ,				UpperCAmelCase__   :			Any=1      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			List[str]=3      ,				UpperCAmelCase__   :			Tuple=5      ,				UpperCAmelCase__   :			int=True      ,				UpperCAmelCase__   :			Any="first"      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			Optional[int]=0.1      ,				UpperCAmelCase__   :			Optional[Any]=5      ,				UpperCAmelCase__   :			List[str]=5      ,				UpperCAmelCase__   :			int=0      ,				UpperCAmelCase__   :			Optional[Any]=0      ,				UpperCAmelCase__   :			Tuple=2      ,				UpperCAmelCase__   :			Optional[Any]=0      ,				**UpperCAmelCase__   :			Tuple      ,				)		->List[Any]:





           '''simple docstring'''

           A__   				=					vocab_size
           A__   				=					emb_dim
           A__   				=					n_layers
           A__   				=					n_heads
           A__   				=					dropout
           A__   				=					attention_dropout
           A__   				=					gelu_activation
           A__   				=					sinusoidal_embeddings
           A__   				=					causal
           A__   				=					asm
           A__   				=					n_langs
           A__   				=					use_lang_emb
           A__   				=					layer_norm_eps
           A__   				=					bos_index
           A__   				=					eos_index
           A__   				=					pad_index
           A__   				=					unk_index
           A__   				=					mask_index
           A__   				=					is_encoder
           A__   				=					max_position_embeddings
           A__   				=					embed_init_std
           A__   				=					init_std
           A__   				=					summary_type
           A__   				=					summary_use_proj
           A__   				=					summary_activation
           A__   				=					summary_proj_to_labels
           A__   				=					summary_first_dropout
           A__   				=					start_n_top
           A__   				=					end_n_top
           A__   				=					mask_token_id
           A__   				=					lang_id

           if "n_words" in kwargs:
                  A__   				=					kwargs['''n_words''']

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


class        UpperCamelCase_					(				lowercase_  ):

    '''simple docstring'''






    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->str:





           '''simple docstring'''

           if self.task == "multiple-choice":
                  A__   				=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                  A__   				=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ])








import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()


class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

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


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Dict:





           '''simple docstring'''

           A__   				=					1
           A__   				=					3
           A__   				=					(32, 32)

           A__   				=					floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0)).to(UpperCAmelCase__)
           return image


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					UNetaDConditionModel(
               block_out_channels=(32, 32, 64)      ,				layers_per_block=2      ,				sample_size=32      ,				in_channels=7      ,				out_channels=4      ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')      ,				up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,				cross_attention_dim=32      ,				attention_head_dim=8      ,				use_linear_projection=UpperCAmelCase__      ,				only_cross_attention=(True, True, False)      ,				num_class_embeds=100      ,				)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					AutoencoderKL(
               block_out_channels=[32, 32, 64]      ,				in_channels=3      ,				out_channels=3      ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,				latent_channels=4      ,				)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           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=1_000      ,				hidden_act='''gelu'''      ,				projection_dim=512      ,				)
           return CLIPTextModel(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Tuple:





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)

           A__   				=					output.images

           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				return_dict=UpperCAmelCase__      ,				)[0]

           A__   				=					image[0, -3:, -3:, -1]
           A__   				=					image_from_tuple[0, -3:, -3:, -1]

           A__   				=					low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           A__   				=					np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->int:





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					sd_pipe(
               2 * [prompt]      ,				image=2 * [low_res_image]      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)
           A__   				=					output.images
           assert image.shape[0] == 2

           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_images_per_prompt=2      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)
           A__   				=					output.images
           assert image.shape[0] == 2





    @unittest.skipIf(torch_device != '''cuda'''      ,				'''This test requires a GPU''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # put models in fp16, except vae as it overflows in fp16
           A__   				=					unet.half()
           A__   				=					text_encoder.half()

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					torch.manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=2      ,				output_type='''np'''      ,				).images

           A__   				=					low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)


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

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->str:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
               '''/upsampled_cat.npy''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				output_type='''np'''      ,				)
           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 1e-3


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
               '''/upsampled_cat_fp16.npy''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(
               UpperCAmelCase__      ,				torch_dtype=torch.floataa      ,				)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				output_type='''np'''      ,				)
           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 5e-1





    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->List[Any]:





           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(
               UpperCAmelCase__      ,				torch_dtype=torch.floataa      ,				)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=5      ,				output_type='''np'''      ,				)

           A__   				=					torch.cuda.max_memory_allocated()
           # make sure that less than 2.9 GB is allocated
           assert mem_bytes < 2.9 * 10**9


