
import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			@property
			def       lowerCAmelCase__	(       self :       int      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Any	=						UNetaDModel(
							    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Union[str, Any]	=						VQModel(
							    block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=3						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Any      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							List[str]	=						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						,	)
							return CLIPTextModel(snake_case_      )




			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Union[str, Any]	=						self.dummy_uncond_unet
							UpperCamelCase_:							Optional[Any]	=						DDIMScheduler()
							UpperCamelCase_:							List[str]	=						self.dummy_vq_model

							UpperCamelCase_:							List[Any]	=						LDMPipeline(unet=snake_case_						,	vqvae=snake_case_						,	scheduler=snake_case_      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							str	=						torch.manual_seed(0      )
							UpperCamelCase_:							int	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							Dict	=						torch.manual_seed(0      )
							UpperCamelCase_:							str	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""						,	return_dict=snake_case_      )[0]

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Optional[int]	=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase_:							str	=						np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]      )
							UpperCamelCase_:							Optional[Any]	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < tolerance




@slow
@require_torch
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Dict	=						LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							List[str]	=						torch.manual_seed(0      )
							UpperCamelCase_:							Optional[int]	=						ldm(generator=snake_case_						,	num_inference_steps=5						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							UpperCamelCase_:							List[str]	=						np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]      )
							UpperCamelCase_:							Dict	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance








import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_						: Union[str, Any]         =  logging.getLogger()

lowerCamelCase_						: List[str]         =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Dict      ):
							os.makedirs(snake_case_						,	exist_ok=snake_case_      )
							UpperCamelCase_:							int	=						{"""source""": """What is love ?""", """target""": """life"""}
							UpperCamelCase_:							Tuple	=						{"""train""": 12, """val""": 2, """test""": 2}
							for split in ["train", "test", "val"]:
											for field in ["source", "target"]:
															UpperCamelCase_:							Tuple	=						"""\n""".join([contents[field]] * n_lines[split]      )
															with open(os.path.join(snake_case_						,	f'''{split}.{field}'''      )						,	"""w"""      ) as f:
																			f.write(snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int						,	snake_case_ :       str = "pytorch"      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_auto_remove_tmp_dir()
							UpperCamelCase_:							Dict	=						os.path.join(snake_case_						,	"""output"""      )
							UpperCamelCase_:							Any	=						os.path.join(snake_case_						,	"""data"""      )
							self._create_dummy_data(data_dir=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

							if gpus > 0:
											testargs.append(f'''--gpus={gpus}'''      )
											if is_apex_available():
															testargs.append("""--fp16"""      )
							else:
											testargs.append("""--gpus=0"""      )
											testargs.append("""--distributed_backend=ddp_cpu"""      )
											testargs.append("""--num_processes=2"""      )

							UpperCamelCase_:							Optional[Any]	=						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
							execute_subprocess_async(snake_case_						,	env=self.get_env()      )

							UpperCamelCase_:							Optional[int]	=						os.path.join(snake_case_						,	"""metrics.json"""      )
							with open(snake_case_      ) as f:
											UpperCamelCase_:							Any	=						json.load(snake_case_      )
							return result

			@require_torch_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						self._run_finetune(gpus=1      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						self._run_finetune(gpus=2      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )




			@require_torch_multi_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )






