


'''simple docstring'''



import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 		_snake_case					:




					def 				snake_case__						(      self):
								torch.manual_seed(0)
								UpperCAmelCase__  :					Dict        =     TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""")

								torch.manual_seed(0)
								UpperCAmelCase__  :					Any        =     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""")

								torch.manual_seed(0)
								UpperCAmelCase__  :					List[Any]        =     UNetaDConditionModel(
								    sample_size=32							,							layers_per_block=1							,							block_out_channels=[32, 64]							,							down_block_types=[
								        """ResnetDownsampleBlock2D""",
								        """SimpleCrossAttnDownBlock2D""",
								    ]							,							mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""							,							up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]							,							in_channels=3							,							out_channels=6							,							cross_attention_dim=32							,							encoder_hid_dim=32							,							attention_head_dim=8							,							addition_embed_type="""text"""							,							addition_embed_type_num_heads=2							,							cross_attention_norm="""group_norm"""							,							resnet_time_scale_shift="""scale_shift"""							,							act_fn="""gelu"""							,							)
								unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

								torch.manual_seed(0)
								UpperCAmelCase__  :					Optional[int]        =     DDPMScheduler(
								    num_train_timesteps=1000							,							beta_schedule="""squaredcos_cap_v2"""							,							beta_start=0.0001							,							beta_end=0.02							,							thresholding=snake_case__							,							dynamic_thresholding_ratio=0.95							,							sample_max_value=1.0							,							prediction_type="""epsilon"""							,							variance_type="""learned_range"""							,							)

								torch.manual_seed(0)
								UpperCAmelCase__  :					Union[str, Any]        =     IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




					def 				snake_case__						(      self):
								torch.manual_seed(0)
								UpperCAmelCase__  :					Dict        =     TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""")

								torch.manual_seed(0)
								UpperCAmelCase__  :					int        =     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""")

								torch.manual_seed(0)
								UpperCAmelCase__  :					int        =     UNetaDConditionModel(
								    sample_size=32							,							layers_per_block=[1, 2]							,							block_out_channels=[32, 64]							,							down_block_types=[
								        """ResnetDownsampleBlock2D""",
								        """SimpleCrossAttnDownBlock2D""",
								    ]							,							mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""							,							up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]							,							in_channels=6							,							out_channels=6							,							cross_attention_dim=32							,							encoder_hid_dim=32							,							attention_head_dim=8							,							addition_embed_type="""text"""							,							addition_embed_type_num_heads=2							,							cross_attention_norm="""group_norm"""							,							resnet_time_scale_shift="""scale_shift"""							,							act_fn="""gelu"""							,							class_embed_type="""timestep"""							,							mid_block_scale_factor=1.414							,							time_embedding_act_fn="""gelu"""							,							time_embedding_dim=32							,							)
								unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

								torch.manual_seed(0)
								UpperCAmelCase__  :					List[Any]        =     DDPMScheduler(
								    num_train_timesteps=1000							,							beta_schedule="""squaredcos_cap_v2"""							,							beta_start=0.0001							,							beta_end=0.02							,							thresholding=snake_case__							,							dynamic_thresholding_ratio=0.95							,							sample_max_value=1.0							,							prediction_type="""epsilon"""							,							variance_type="""learned_range"""							,							)

								torch.manual_seed(0)
								UpperCAmelCase__  :					int        =     DDPMScheduler(
								    num_train_timesteps=1000							,							beta_schedule="""squaredcos_cap_v2"""							,							beta_start=0.0001							,							beta_end=0.02							,							)

								torch.manual_seed(0)
								UpperCAmelCase__  :					Tuple        =     IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     self.get_dummy_components()
								UpperCAmelCase__  :					str        =     self.pipeline_class(**snake_case__)
								pipe.to(snake_case__)
								pipe.set_progress_bar_config(disable=snake_case__)

								UpperCAmelCase__  :					Any        =     self.get_dummy_inputs(snake_case__)

								UpperCAmelCase__  :					Union[str, Any]        =     inputs["prompt"]
								UpperCAmelCase__  :					str        =     inputs["generator"]
								UpperCAmelCase__  :					List[Any]        =     inputs["num_inference_steps"]
								UpperCAmelCase__  :					Optional[Any]        =     inputs["output_type"]

								if "image" in inputs:
											UpperCAmelCase__  :					str        =     inputs["image"]
								else:
											UpperCAmelCase__  :					int        =     None

								if "mask_image" in inputs:
											UpperCAmelCase__  :					str        =     inputs["mask_image"]
								else:
											UpperCAmelCase__  :					Optional[int]        =     None

								if "original_image" in inputs:
											UpperCAmelCase__  :					Optional[int]        =     inputs["original_image"]
								else:
											UpperCAmelCase__  :					Any        =     None

								UpperCAmelCase__  :					Tuple        =     pipe.encode_prompt(snake_case__)

								# inputs with prompt converted to embeddings
								UpperCAmelCase__  :					str        =     {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
											UpperCAmelCase__  :					Dict        =     image

								if mask_image is not None:
											UpperCAmelCase__  :					Tuple        =     mask_image

								if original_image is not None:
											UpperCAmelCase__  :					Dict        =     original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
											setattr(snake_case__							,							snake_case__							,							snake_case__)

								UpperCAmelCase__  :					Optional[Any]        =     pipe(**snake_case__)[0]

								with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(snake_case__)
											UpperCAmelCase__  :					Union[str, Any]        =     self.pipeline_class.from_pretrained(snake_case__)
											pipe_loaded.to(snake_case__)
											pipe_loaded.set_progress_bar_config(disable=snake_case__)

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

								for optional_component in pipe._optional_components:
											self.assertTrue(
											    getattr(snake_case__							,							snake_case__) is None							,							f'''`{optional_component}` did not stay set to None after loading.'''							,							)

								UpperCAmelCase__  :					Tuple        =     self.get_dummy_inputs(snake_case__)

								UpperCAmelCase__  :					Tuple        =     inputs["generator"]
								UpperCAmelCase__  :					Union[str, Any]        =     inputs["num_inference_steps"]
								UpperCAmelCase__  :					Any        =     inputs["output_type"]

								# inputs with prompt converted to embeddings
								UpperCAmelCase__  :					Optional[Any]        =     {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
											UpperCAmelCase__  :					int        =     image

								if mask_image is not None:
											UpperCAmelCase__  :					int        =     mask_image

								if original_image is not None:
											UpperCAmelCase__  :					List[str]        =     original_image

								UpperCAmelCase__  :					Union[str, Any]        =     pipe_loaded(**snake_case__)[0]

								UpperCAmelCase__  :					Optional[Any]        =     np.abs(to_np(snake_case__) - to_np(snake_case__)).max()
								self.assertLess(snake_case__							,							1e-4)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     self.get_dummy_components()
								UpperCAmelCase__  :					Tuple        =     self.pipeline_class(**snake_case__)
								pipe.to(snake_case__)
								pipe.set_progress_bar_config(disable=snake_case__)

								UpperCAmelCase__  :					Dict        =     self.get_dummy_inputs(snake_case__)
								UpperCAmelCase__  :					Dict        =     pipe(**snake_case__)[0]

								with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(snake_case__)
											UpperCAmelCase__  :					List[str]        =     self.pipeline_class.from_pretrained(snake_case__)
											pipe_loaded.to(snake_case__)
											pipe_loaded.set_progress_bar_config(disable=snake_case__)

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

								UpperCAmelCase__  :					Tuple        =     self.get_dummy_inputs(snake_case__)
								UpperCAmelCase__  :					List[str]        =     pipe_loaded(**snake_case__)[0]

								UpperCAmelCase__  :					Union[str, Any]        =     np.abs(to_np(snake_case__) - to_np(snake_case__)).max()
								self.assertLess(snake_case__							,							1e-4)



'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__A	=logging.get_logger(__name__)

__A	={
    '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 		_snake_case					(     a__	):
					lowerCAmelCase      :Any   =					'''xlm'''
					lowerCAmelCase      :Any   =					{
					    '''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-1_2							,							_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							,							):
								UpperCAmelCase__  :					Tuple        =     vocab_size
								UpperCAmelCase__  :					Tuple        =     emb_dim
								UpperCAmelCase__  :					Optional[Any]        =     n_layers
								UpperCAmelCase__  :					List[str]        =     n_heads
								UpperCAmelCase__  :					List[str]        =     dropout
								UpperCAmelCase__  :					Optional[int]        =     attention_dropout
								UpperCAmelCase__  :					Tuple        =     gelu_activation
								UpperCAmelCase__  :					Optional[Any]        =     sinusoidal_embeddings
								UpperCAmelCase__  :					int        =     causal
								UpperCAmelCase__  :					Union[str, Any]        =     asm
								UpperCAmelCase__  :					Optional[Any]        =     n_langs
								UpperCAmelCase__  :					List[Any]        =     use_lang_emb
								UpperCAmelCase__  :					Union[str, Any]        =     layer_norm_eps
								UpperCAmelCase__  :					List[str]        =     bos_index
								UpperCAmelCase__  :					List[Any]        =     eos_index
								UpperCAmelCase__  :					int        =     pad_index
								UpperCAmelCase__  :					str        =     unk_index
								UpperCAmelCase__  :					Dict        =     mask_index
								UpperCAmelCase__  :					str        =     is_encoder
								UpperCAmelCase__  :					Dict        =     max_position_embeddings
								UpperCAmelCase__  :					Any        =     embed_init_std
								UpperCAmelCase__  :					List[Any]        =     init_std
								UpperCAmelCase__  :					List[str]        =     summary_type
								UpperCAmelCase__  :					Union[str, Any]        =     summary_use_proj
								UpperCAmelCase__  :					Any        =     summary_activation
								UpperCAmelCase__  :					List[str]        =     summary_proj_to_labels
								UpperCAmelCase__  :					Union[str, Any]        =     summary_first_dropout
								UpperCAmelCase__  :					str        =     start_n_top
								UpperCAmelCase__  :					str        =     end_n_top
								UpperCAmelCase__  :					Tuple        =     mask_token_id
								UpperCAmelCase__  :					Union[str, Any]        =     lang_id

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

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


class 		_snake_case					(     a__	):




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