
"""simple docstring"""
import os

import jsonlines
import numpy as np
from tqdm import tqdm


SCREAMING_SNAKE_CASE_					     =	2_048
SCREAMING_SNAKE_CASE_					     =	4_096
SCREAMING_SNAKE_CASE_					     =	42
SCREAMING_SNAKE_CASE_					     =	os.environ.pop('''PROCESS_TRAIN''', '''false''')
SCREAMING_SNAKE_CASE_					     =	{'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}

def  lowercase     (_lowerCAmelCase						):
						def choose_first(_lowerCAmelCase ,    _lowerCAmelCase=False						):
												assert isinstance(_lowerCAmelCase ,    _lowerCAmelCase						)
												if len(_lowerCAmelCase						) == 1:
																		__lowerCAmelCase     =  answer[0]
																		return {k: [answer[k]] for k in answer} if is_long_answer else answer
												for a in answer:
																		if is_long_answer:
																								__lowerCAmelCase     =  {k: [a[k]] for k in a}
																		if len(a["""start_token"""]						) > 0:
																								break
												return a

						__lowerCAmelCase     =  {"""id""": example["""id"""]}
						__lowerCAmelCase     =  example["""annotations"""]
						__lowerCAmelCase     =  annotation["""yes_no_answer"""]
						if 0 in yes_no_answer or 1 in yes_no_answer:
												__lowerCAmelCase     =  ["""yes"""] if 1 in yes_no_answer else ["""no"""]
												__lowerCAmelCase     =  __lowerCAmelCase     =  []
												__lowerCAmelCase     =  __lowerCAmelCase     =  []
												__lowerCAmelCase     =  ["""<cls>"""]
						else:
												__lowerCAmelCase     =  ["""short"""]
												__lowerCAmelCase     =  choose_first(annotation["""short_answers"""]						)
												if len(out["""start_token"""]						) == 0:
																		# answer will be long if short is not available
																		__lowerCAmelCase     =  ["""long"""]
																		__lowerCAmelCase     =  choose_first(annotation["""long_answer"""] ,    is_long_answer=_lowerCAmelCase						)
																		__lowerCAmelCase     =  []
												answer.update(_lowerCAmelCase						)

						# disregard some samples
						if len(answer["""start_token"""]						) > 1 or answer["start_token"] == answer["end_token"]:
												__lowerCAmelCase     =  True
						else:
												__lowerCAmelCase     =  False

						__lowerCAmelCase     =  ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
						if not all(isinstance(answer[k] ,    _lowerCAmelCase						) for k in cols						):
												raise ValueError("""Issue in ID""" ,    example["""id"""]						)

						return answer

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase=False						):
						__lowerCAmelCase     =  _get_single_answer(_lowerCAmelCase						)
						# bytes are of no use
						del answer["start_byte"]
						del answer["end_byte"]

						# handle yes_no answers explicitly
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												__lowerCAmelCase     =  example["""document"""]["""tokens"""]
												__lowerCAmelCase     =  []
												for i in range(len(doc["""token"""]						)						):
																		if not doc["is_html"][i]:
																								context.append(doc["""token"""][i]						)
												return {
												    "context": " ".join(_lowerCAmelCase						),
												    "answer": {
												        "start_token": -100,  # ignore index in cross-entropy
												        "end_token": -100,  # ignore index in cross-entropy
												        "category": answer["category"],
												        "span": answer["category"],  # extra
												    },
												}

						# later, help in removing all no answers
						if answer["start_token"] == [-1]:
												return {
												    "context": "None",
												    "answer": {
												        "start_token": -1,
												        "end_token": -1,
												        "category": "null",
												        "span": "None",  # extra
												    },
												}

						# handling normal samples

						__lowerCAmelCase     =  ["""start_token""", """end_token"""]
						answer.update({k: answer[k][0] if len(answer[k]						) > 0 else answer[k] for k in cols}						)  # e.g. [10] == 10

						__lowerCAmelCase     =  example["""document"""]["""tokens"""]
						__lowerCAmelCase     =  answer["""start_token"""]
						__lowerCAmelCase     =  answer["""end_token"""]

						__lowerCAmelCase     =  []
						for i in range(len(doc["""token"""]						)						):
												if not doc["is_html"][i]:
																		context.append(doc["""token"""][i]						)
												else:
																		if answer["start_token"] > i:
																								start_token -= 1
																		if answer["end_token"] > i:
																								end_token -= 1
						__lowerCAmelCase     =  """ """.join(context[start_token:end_token]						)

						# checking above code
						if assertion:
												__lowerCAmelCase     =  doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
												__lowerCAmelCase     =  doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
												__lowerCAmelCase     =  """ """.join([old[i] for i in range(len(_lowerCAmelCase						)						) if not is_html[i]]						)
												if new != old:
																		print("""ID:""" ,    example["""id"""]						)
																		print("""New:""" ,    _lowerCAmelCase ,    end="""\n"""						)
																		print("""Old:""" ,    _lowerCAmelCase ,    end="""\n\n"""						)

						return {
						    "context": " ".join(_lowerCAmelCase						),
						    "answer": {
						        "start_token": start_token,
						        "end_token": end_token - 1,  # this makes it inclusive
						        "category": answer["category"],  # either long or short
						        "span": new,  # extra
						    },
						}

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=2048 ,    _lowerCAmelCase=4096 ,    _lowerCAmelCase=True						):
						# overlap will be of doc_stride - q_len

						__lowerCAmelCase     =  get_context_and_ans(_lowerCAmelCase ,    assertion=_lowerCAmelCase						)
						__lowerCAmelCase     =  out["""answer"""]

						# later, removing these samples
						if answer["start_token"] == -1:
												return {
												    "example_id": example["id"],
												    "input_ids": [[-1]],
												    "labels": {
												        "start_token": [-1],
												        "end_token": [-1],
												        "category": ["null"],
												    },
												}

						__lowerCAmelCase     =  tokenizer(example["""question"""]["""text"""] ,    out["""context"""]						).input_ids
						__lowerCAmelCase     =  input_ids.index(tokenizer.sep_token_id						) + 1

						# return yes/no
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												__lowerCAmelCase     =  []
												__lowerCAmelCase     =  []
												__lowerCAmelCase     =  input_ids[:q_len]
												__lowerCAmelCase     =  range(_lowerCAmelCase ,    len(_lowerCAmelCase						) ,    max_length - doc_stride						)
												for i in doc_start_indices:
																		__lowerCAmelCase     =  i + max_length - q_len
																		__lowerCAmelCase     =  input_ids[i:end_index]
																		inputs.append(q_indices + slice						)
																		category.append(answer["""category"""][0]						)
																		if slice[-1] == tokenizer.sep_token_id:
																								break

												return {
												    "example_id": example["id"],
												    "input_ids": inputs,
												    "labels": {
												        "start_token": [-100] * len(_lowerCAmelCase						),
												        "end_token": [-100] * len(_lowerCAmelCase						),
												        "category": category,
												    },
												}

						__lowerCAmelCase     =  out["""context"""].split()
						__lowerCAmelCase     =  splitted_context[answer["""end_token"""]]
						__lowerCAmelCase     =  len(
						    tokenizer(
						        """ """.join(splitted_context[: answer["""start_token"""]]						) ,    add_special_tokens=_lowerCAmelCase ,    ).input_ids						)
						__lowerCAmelCase     =  len(
						    tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]						) ,    add_special_tokens=_lowerCAmelCase						).input_ids						)

						answer["start_token"] += q_len
						answer["end_token"] += q_len

						# fixing end token
						__lowerCAmelCase     =  len(tokenizer(_lowerCAmelCase ,    add_special_tokens=_lowerCAmelCase						).input_ids						)
						if num_sub_tokens > 1:
												answer["end_token"] += num_sub_tokens - 1

						__lowerCAmelCase     =  input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
						__lowerCAmelCase     =  answer["""start_token"""]
						__lowerCAmelCase     =  answer["""end_token"""]

						if assertion:
												__lowerCAmelCase     =  tokenizer.decode(_lowerCAmelCase						)
												if answer["span"] != new:
																		print("""ISSUE IN TOKENIZATION"""						)
																		print("""OLD:""" ,    answer["""span"""]						)
																		print("""NEW:""" ,    _lowerCAmelCase ,    end="""\n\n"""						)

						if len(_lowerCAmelCase						) <= max_length:
												return {
												    "example_id": example["id"],
												    "input_ids": [input_ids],
												    "labels": {
												        "start_token": [answer["start_token"]],
												        "end_token": [answer["end_token"]],
												        "category": answer["category"],
												    },
												}

						__lowerCAmelCase     =  input_ids[:q_len]
						__lowerCAmelCase     =  range(_lowerCAmelCase ,    len(_lowerCAmelCase						) ,    max_length - doc_stride						)

						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  []  # null, yes, no, long, short
						for i in doc_start_indices:
												__lowerCAmelCase     =  i + max_length - q_len
												__lowerCAmelCase     =  input_ids[i:end_index]
												inputs.append(q_indices + slice						)
												assert len(inputs[-1]						) <= max_length, "Issue in truncating length"

												if start_token >= i and end_token <= end_index - 1:
																		__lowerCAmelCase     =  start_token - i + q_len
																		__lowerCAmelCase     =  end_token - i + q_len
																		answers_category.append(answer["""category"""][0]						)  # ["short"] -> "short"
												else:
																		__lowerCAmelCase     =  -100
																		__lowerCAmelCase     =  -100
																		answers_category.append("""null"""						)
												__lowerCAmelCase     =  inputs[-1][start_token : end_token + 1]

												answers_start_token.append(_lowerCAmelCase						)
												answers_end_token.append(_lowerCAmelCase						)
												if assertion:
																		if new != old and new != [tokenizer.cls_token_id]:
																								print("""ISSUE in strided for ID:""" ,    example["""id"""]						)
																								print("""New:""" ,    tokenizer.decode(_lowerCAmelCase						)						)
																								print("""Old:""" ,    tokenizer.decode(_lowerCAmelCase						) ,    end="""\n\n"""						)
												if slice[-1] == tokenizer.sep_token_id:
																		break

						return {
						    "example_id": example["id"],
						    "input_ids": inputs,
						    "labels": {
						        "start_token": answers_start_token,
						        "end_token": answers_end_token,
						        "category": answers_category,
						    },
						}

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=2048 ,    _lowerCAmelCase=4096 ,    _lowerCAmelCase=False						):
						__lowerCAmelCase     =  get_strided_contexts_and_ans(
						    _lowerCAmelCase ,    _lowerCAmelCase ,    doc_stride=_lowerCAmelCase ,    max_length=_lowerCAmelCase ,    assertion=_lowerCAmelCase ,    )

						return example

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						with jsonlines.open(_lowerCAmelCase ,    """a"""						) as writer:
												for example in tqdm(_lowerCAmelCase ,    total=len(_lowerCAmelCase						) ,    desc="""Saving samples ... """						):
																		__lowerCAmelCase     =  example["""labels"""]
																		for ids, start, end, cat in zip(
																		    example["""input_ids"""] ,    labels["""start_token"""] ,    labels["""end_token"""] ,    labels["""category"""] ,    ):
																								if start == -1 and end == -1:
																														continue  # leave waste samples with no answer
																								if cat == "null" and np.random.rand() < 0.6:
																														continue  # removing 50 % samples
																								writer.write(
																								    {
																								        """input_ids""": ids,
																								        """start_token""": start,
																								        """end_token""": end,
																								        """category""": CATEGORY_MAPPING[cat],
																								    }						)


if __name__ == "__main__":
			from datasets import load_dataset

			from transformers import BigBirdTokenizer

			SCREAMING_SNAKE_CASE_					     =	load_dataset('''natural_questions''')
			SCREAMING_SNAKE_CASE_					     =	BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

			SCREAMING_SNAKE_CASE_					     =	data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

			SCREAMING_SNAKE_CASE_					     =	{
			    '''tokenizer''': tokenizer,
			    '''doc_stride''': DOC_STRIDE,
			    '''max_length''': MAX_LENGTH,
			    '''assertion''': False,
			}
			SCREAMING_SNAKE_CASE_					     =	data.map(prepare_inputs, fn_kwargs=fn_kwargs)
			SCREAMING_SNAKE_CASE_					     =	data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
			print(data)

			np.random.seed(SEED)
			SCREAMING_SNAKE_CASE_					     =	'''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
			save_to_disk(data, file_name=cache_file_name)


"""simple docstring"""
import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class       lowerCAmelCase_		(				A__				,				A__				,				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		StableUnCLIPPipeline
							_snake_case						      =		TEXT_TO_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							_snake_case						      =		False




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  32
													__lowerCAmelCase     =  embedder_hidden_size

													# prior components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTextModelWithProjection(
													    CLIPTextConfig(
													        bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=snake_case_    ,				projection_dim=snake_case_    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  PriorTransformer(
													    num_attention_heads=2    ,				attention_head_dim=12    ,				embedding_dim=snake_case_    ,				num_layers=1    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDPMScheduler(
													    variance_type="""fixed_small_log"""    ,				prediction_type="""sample"""    ,				num_train_timesteps=1_000    ,				clip_sample=snake_case_    ,				clip_sample_range=5.0    ,				beta_schedule="""squaredcos_cap_v2"""    ,				)

													# regular denoising components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  StableUnCLIPImageNormalizer(embedding_dim=snake_case_				)
													__lowerCAmelCase     =  DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTextModel(
													    CLIPTextConfig(
													        bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=snake_case_    ,				projection_dim=32    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  UNetaDConditionModel(
													    sample_size=32    ,				in_channels=4    ,				out_channels=4    ,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")    ,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")    ,				block_out_channels=(32, 64)    ,				attention_head_dim=(2, 4)    ,				class_embed_type="""projection"""    ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,				cross_attention_dim=snake_case_    ,				layers_per_block=1    ,				upcast_attention=snake_case_    ,				use_linear_projection=snake_case_    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDIMScheduler(
													    beta_schedule="""scaled_linear"""    ,				beta_start=0.00_085    ,				beta_end=0.012    ,				prediction_type="""v_prediction"""    ,				set_alpha_to_one=snake_case_    ,				steps_offset=1    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  AutoencoderKL()

													__lowerCAmelCase     =  {
													    # prior components
													    """prior_tokenizer""": prior_tokenizer,
													    """prior_text_encoder""": prior_text_encoder,
													    """prior""": prior,
													    """prior_scheduler""": prior_scheduler,
													    # image noising components
													    """image_normalizer""": image_normalizer,
													    """image_noising_scheduler""": image_noising_scheduler,
													    # regular denoising components
													    """tokenizer""": tokenizer,
													    """text_encoder""": text_encoder,
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													}

													return components




							def    A__						( self    ,				snake_case_    ,				snake_case_=0				)       ->  Optional[Any]:
													if str(snake_case_				).startswith("""mps"""				):
																			__lowerCAmelCase     =  torch.manual_seed(snake_case_				)
													else:
																			__lowerCAmelCase     =  torch.Generator(device=snake_case_				).manual_seed(snake_case_				)
													__lowerCAmelCase     =  {
													    """prompt""": """A painting of a squirrel eating a burger""",
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """prior_num_inference_steps""": 2,
													    """output_type""": """numpy""",
													}
													return inputs




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device == """cpu"""

													self._test_attention_slicing_forward_pass(test_max_difference=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device in ["""cpu""", """mps"""]

													self._test_inference_batch_single_identical(test_max_difference=snake_case_				)
@slow
@require_torch_gpu
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							def    A__						( self				)       ->  Optional[int]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""				)

													__lowerCAmelCase     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""    ,				torch_dtype=torch.floataa				)
													pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													# stable unclip will oom when integration tests are run on a V100,
													# so turn on memory savings
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													__lowerCAmelCase     =  torch.Generator(device="""cpu"""				).manual_seed(0				)
													__lowerCAmelCase     =  pipe("""anime turle"""    ,				generator=snake_case_    ,				output_type="""np"""				)

													__lowerCAmelCase     =  output.images[0]

													assert image.shape == (768, 768, 3)

													assert_mean_pixel_difference(snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  Tuple:
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													__lowerCAmelCase     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""    ,				torch_dtype=torch.floataa				)
													__lowerCAmelCase     =  pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													__lowerCAmelCase     =  pipe(
													    """anime turtle"""    ,				prior_num_inference_steps=2    ,				num_inference_steps=2    ,				output_type="""np"""    ,				)

													__lowerCAmelCase     =  torch.cuda.max_memory_allocated()
													# make sure that less than 7 GB is allocated
													assert mem_bytes < 7 * 10**9
