


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[str]=True							,					UpperCamelCase__:     Union[str, Any]="pt"						) ->   int:





	"""simple docstring"""







	A								=				{"""add_prefix_space""": True} if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						) and not line.startswith(""" """						) else {}
	A								=				padding_side
	return tokenizer(
	    [line]							,					max_length=lowerCAmelCase__							,					padding="""max_length""" if pad_to_max_length else None							,					truncation=lowerCAmelCase__							,					return_tensors=lowerCAmelCase__							,					add_special_tokens=lowerCAmelCase__							,					**lowerCAmelCase__							,					)





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Any=None							,					) ->   int:





	"""simple docstring"""







	A								=				input_ids.ne(lowerCAmelCase__						).any(dim=0						)
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])






class       _UpperCamelCase    (					__SCREAMING_SNAKE_CASE				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__				,   a__				,   a__="train"				,   a__=None				,   a__=None				,   a__=None				,   a__=""				,   )	->				Optional[Any]:
					super().__init__()
					A								=				Path(_a   ).joinpath(type_path + """.source"""   )
					A								=				Path(_a   ).joinpath(type_path + """.target"""   )
					A								=				self.get_char_lens(self.src_file   )
					A								=				max_source_length
					A								=				max_target_length
					assert min(self.src_lens   ) > 0, f'found empty line in {self.src_file}'
					A								=				tokenizer
					A								=				prefix
					if n_obs is not None:
						A								=				self.src_lens[:n_obs]
					A								=				src_lang
					A								=				tgt_lang

				def __len__(						self   )	->				Any:
					return len(self.src_lens   )

				def __getitem__(						self				,   a__   )	->				Any:
					A								=				index + 1  # linecache starts at 1
					A								=				self.prefix + linecache.getline(str(self.src_file   )				,   _a   ).rstrip("""\n"""   )
					A								=				linecache.getline(str(self.tgt_file   )				,   _a   ).rstrip("""\n"""   )
					assert source_line, f'empty source line for index {index}'
					assert tgt_line, f'empty tgt line for index {index}'

					# Need to add eos token manually for T5
					if isinstance(self.tokenizer				,   _a   ):
						source_line += self.tokenizer.eos_token
						tgt_line += self.tokenizer.eos_token

					# Pad source and target to the right
					A								=				(
					    self.tokenizer.question_encoder if isinstance(self.tokenizer				,   _a   ) else self.tokenizer
					)
					A								=				self.tokenizer.generator if isinstance(self.tokenizer				,   _a   ) else self.tokenizer

					A								=				encode_line(_a				,   _a				,   self.max_source_length				,   """right"""   )
					A								=				encode_line(_a				,   _a				,   self.max_target_length				,   """right"""   )

					A								=				source_inputs["""input_ids"""].squeeze()
					A								=				target_inputs["""input_ids"""].squeeze()
					A								=				source_inputs["""attention_mask"""].squeeze()
					return {
					    "input_ids": source_ids,
					    "attention_mask": src_mask,
					    "decoder_input_ids": target_ids,
					}

				@staticmethod
				def      _UpperCAmelCase					(						a__   )	->				Union[str, Any]:
					return [len(_a   ) for x in Path(_a   ).open().readlines()]

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					A								=				torch.stack([x["""input_ids"""] for x in batch]   )
					A								=				torch.stack([x["""attention_mask"""] for x in batch]   )
					A								=				torch.stack([x["""decoder_input_ids"""] for x in batch]   )
					A								=				(
					    self.tokenizer.generator.pad_token_id
					    if isinstance(self.tokenizer				,   _a   )
					    else self.tokenizer.pad_token_id
					)
					A								=				(
					    self.tokenizer.question_encoder.pad_token_id
					    if isinstance(self.tokenizer				,   _a   )
					    else self.tokenizer.pad_token_id
					)
					A								=				trim_batch(_a				,   _a   )
					A					,			A								=				trim_batch(_a				,   _a				,   attention_mask=_a   )
					A								=				{
					    """input_ids""": source_ids,
					    """attention_mask""": source_mask,
					    """decoder_input_ids""": y,
					}
					return batch
_lowercase       :      List[str]	=	getLogger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     List[List]						) ->   int:





	"""simple docstring"""







	return list(itertools.chain.from_iterable(lowerCAmelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   None:





	"""simple docstring"""







	A								=				get_git_info()
	save_json(lowerCAmelCase__							,					os.path.join(lowerCAmelCase__							,					"""git_log.json"""						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     int=4							,					**UpperCamelCase__:     Optional[int]						) ->   str:





	"""simple docstring"""







	with open(lowerCAmelCase__							,					"""w"""						) as f:
		json.dump(lowerCAmelCase__							,					lowerCAmelCase__							,					indent=lowerCAmelCase__							,					**lowerCAmelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Dict:





	"""simple docstring"""







	with open(lowerCAmelCase__						) as f:
		return json.load(lowerCAmelCase__						)





def   _lowerCAmelCase				(   ) ->   Optional[Any]:





	"""simple docstring"""







	A								=				git.Repo(search_parent_directories=lowerCAmelCase__						)
	A								=				{
	    """repo_id""": str(lowerCAmelCase__						),
	    """repo_sha""": str(repo.head.object.hexsha						),
	    """repo_branch""": str(repo.active_branch						),
	    """hostname""": str(socket.gethostname()						),
	}
	return repo_infos





def   _lowerCAmelCase				(   UpperCamelCase__:     Callable							,					UpperCamelCase__:     Iterable						) ->   List:





	"""simple docstring"""







	return list(map(lowerCAmelCase__							,					lowerCAmelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Optional[Any]						) ->   Optional[int]:





	"""simple docstring"""







	with open(lowerCAmelCase__							,					"""wb"""						) as f:
		return pickle.dump(lowerCAmelCase__							,					lowerCAmelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   str:





	"""simple docstring"""







	def remove_articles(UpperCamelCase__:     Any						):
		return re.sub(r"""\b(a|an|the)\b"""							,					""" """							,					lowerCAmelCase__						)

	def white_space_fix(UpperCamelCase__:     List[Any]						):
		return " ".join(text.split()						)

	def remove_punc(UpperCamelCase__:     int						):
		A								=				set(string.punctuation						)
		return "".join(ch for ch in text if ch not in exclude						)

	def lower(UpperCamelCase__:     int						):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__						)						)						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     str						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				normalize_answer(lowerCAmelCase__						).split()
	A								=				normalize_answer(lowerCAmelCase__						).split()
	A								=				Counter(lowerCAmelCase__						) & Counter(lowerCAmelCase__						)
	A								=				sum(common.values()						)
	if num_same == 0:
		return 0
	A								=				1.0 * num_same / len(lowerCAmelCase__						)
	A								=				1.0 * num_same / len(lowerCAmelCase__						)
	A								=				(2 * precision * recall) / (precision + recall)
	return fa





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return normalize_answer(lowerCAmelCase__						) == normalize_answer(lowerCAmelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]							,					UpperCamelCase__:     List[str]						) ->   Dict:





	"""simple docstring"""







	assert len(lowerCAmelCase__						) == len(lowerCAmelCase__						)
	A								=				0
	for hypo, pred in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
		em += exact_match_score(lowerCAmelCase__							,					lowerCAmelCase__						)
	if len(lowerCAmelCase__						) > 0:
		em /= len(lowerCAmelCase__						)
	return {"em": em}





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   Tuple:





	"""simple docstring"""







	return model_prefix.startswith("""rag"""						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				{p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	A								=				"""dropout_rate"""
	for p in extra_params:
		if getattr(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						):
			if not hasattr(lowerCAmelCase__							,					lowerCAmelCase__						) and not hasattr(lowerCAmelCase__							,					equivalent_param[p]						):
				logger.info("""config doesn\'t have a `{}` attribute""".format(lowerCAmelCase__						)						)
				delattr(lowerCAmelCase__							,					lowerCAmelCase__						)
				continue
			A								=				p if hasattr(lowerCAmelCase__							,					lowerCAmelCase__						) else equivalent_param[p]
			setattr(lowerCAmelCase__							,					lowerCAmelCase__							,					getattr(lowerCAmelCase__							,					lowerCAmelCase__						)						)
			delattr(lowerCAmelCase__							,					lowerCAmelCase__						)
	return hparams, config










import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= ConsistencyModelPipeline
				lowerCAmelCase 		= UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				lowerCAmelCase 		= UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

				# Override required_optional_params to remove num_images_per_prompt
				lowerCAmelCase 		= frozenset(
				    [
				        'num_inference_steps',
				        'generator',
				        'latents',
				        'output_type',
				        'return_dict',
				        'callback',
				        'callback_steps',
				    ]				)

				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				UNetaDModel.from_pretrained(
					    """diffusers/consistency-models-test"""				,   subfolder="""test_unet"""				,   )
					return unet

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				UNetaDModel.from_pretrained(
					    """diffusers/consistency-models-test"""				,   subfolder="""test_unet_class_cond"""				,   )
					return unet

				def      _UpperCAmelCase					(						self				,   a__=False   )	->				Tuple:
					if class_cond:
						A								=				self.dummy_cond_unet
					else:
						A								=				self.dummy_uncond_unet

					# Default to CM multistep sampler
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )

					A								=				{
					    """unet""": unet,
					    """scheduler""": scheduler,
					}

					return components

				def      _UpperCAmelCase					(						self				,   a__				,   a__=0   )	->				Tuple:
					if str(a__   ).startswith("""mps"""   ):
						A								=				torch.manual_seed(a__   )
					else:
						A								=				torch.Generator(device=a__   ).manual_seed(a__   )

					A								=				{
					    """batch_size""": 1,
					    """num_inference_steps""": None,
					    """timesteps""": [22, 0],
					    """generator""": generator,
					    """output_type""": """np""",
					}

					return inputs

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components()
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04]   )

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

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components(class_cond=a__   )
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				0
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04]   )

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

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components()
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				1
					A								=				None
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87]   )

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

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components(class_cond=a__   )
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				1
					A								=				None
					A								=				0
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87]   )

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






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

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Tuple:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def      _UpperCAmelCase					(						self				,   a__=0				,   a__=False				,   a__="cpu"				,   a__=torch.floataa				,   a__=(1, 3, 64, 64)   )	->				str:
					A								=				torch.manual_seed(a__   )

					A								=				{
					    """num_inference_steps""": None,
					    """timesteps""": [22, 0],
					    """class_labels""": 0,
					    """generator""": generator,
					    """output_type""": """np""",
					}

					if get_fixed_latents:
						A								=				self.get_fixed_latents(seed=a__				,   device=a__				,   dtype=a__				,   shape=a__   )
						A								=				latents

					return inputs

				def      _UpperCAmelCase					(						self				,   a__=0				,   a__="cpu"				,   a__=torch.floataa				,   a__=(1, 3, 64, 64)   )	->				Optional[Any]:
					if type(a__   ) == str:
						A								=				torch.device(a__   )
					A								=				torch.Generator(device=a__   ).manual_seed(a__   )
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__				,   dtype=a__   )
					return latents

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs()
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				np.array([0.08_88, 0.08_81, 0.06_66, 0.04_79, 0.02_92, 0.01_95, 0.02_01, 0.01_63, 0.02_54]   )

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

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs()
					A								=				1
					A								=				None
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				np.array([0.03_40, 0.01_52, 0.00_63, 0.02_67, 0.02_21, 0.01_07, 0.04_16, 0.01_86, 0.02_17]   )

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

				@require_torch_a
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__				,   torch_dtype=torch.floataa   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs(get_fixed_latents=a__				,   device=a__   )
					# Ensure usage of flash attention in torch 2.0
					with sdp_kernel(enable_flash=a__				,   enable_math=a__				,   enable_mem_efficient=a__   ):
						A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				np.array([0.18_75, 0.14_28, 0.12_89, 0.21_51, 0.20_92, 0.14_77, 0.18_77, 0.16_41, 0.13_53]   )

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

				@require_torch_a
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__				,   torch_dtype=torch.floataa   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs(get_fixed_latents=a__				,   device=a__   )
					A								=				1
					A								=				None
					# Ensure usage of flash attention in torch 2.0
					with sdp_kernel(enable_flash=a__				,   enable_math=a__				,   enable_mem_efficient=a__   ):
						A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				np.array([0.16_63, 0.19_48, 0.22_75, 0.16_80, 0.12_04, 0.12_45, 0.18_58, 0.13_38, 0.20_95]   )

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



