



"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)







class  __lowercase  (       unittest.TestCase):







						"""simple docstring"""







						def __init__(self ,		lowercase__ ,		lowercase__=13 ,		lowercase__=7 ,		lowercase__=True ,		lowercase__=True ,		lowercase__=True ,		lowercase__=True ,		lowercase__=99 ,		lowercase__=32 ,		lowercase__=5 ,		lowercase__=4 ,		lowercase__=37 ,		lowercase__="gelu" ,		lowercase__=0.1 ,		lowercase__=0.1 ,		lowercase__=5_12 ,		lowercase__=16 ,		lowercase__=2 ,		lowercase__=0.02 ,		lowercase__=4 ,		):
											snake_case_       :		str			       =       parent
											snake_case_       :		Optional[int]			       =       batch_size
											snake_case_       :		List[Any]			       =       seq_length
											snake_case_       :		List[str]			       =       is_training
											snake_case_       :		List[Any]			       =       use_attention_mask
											snake_case_       :		List[str]			       =       use_token_type_ids
											snake_case_       :		Any			       =       use_labels
											snake_case_       :		List[str]			       =       vocab_size
											snake_case_       :		List[str]			       =       hidden_size
											snake_case_       :		Tuple			       =       num_hidden_layers
											snake_case_       :		Optional[Any]			       =       num_attention_heads
											snake_case_       :		Tuple			       =       intermediate_size
											snake_case_       :		Optional[Any]			       =       hidden_act
											snake_case_       :		Any			       =       hidden_dropout_prob
											snake_case_       :		List[str]			       =       attention_probs_dropout_prob
											snake_case_       :		List[str]			       =       max_position_embeddings
											snake_case_       :		Tuple			       =       type_vocab_size
											snake_case_       :		Optional[int]			       =       type_sequence_label_size
											snake_case_       :		Any			       =       initializer_range
											snake_case_       :		Any			       =       num_choices







						def     __UpperCamelCase				(self							):
											snake_case_       :		Optional[Any]			       =       ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size							)

											snake_case_       :		Dict			       =       None
											if self.use_attention_mask:
																snake_case_       :		Optional[int]			       =       random_attention_mask([self.batch_size, self.seq_length]							)

											snake_case_       :		str			       =       None
											if self.use_token_type_ids:
																snake_case_       :		Union[str, Any]			       =       ids_tensor([self.batch_size, self.seq_length] ,		self.type_vocab_size							)

											snake_case_       :		Dict			       =       RobertaPreLayerNormConfig(
											    vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		num_hidden_layers=self.num_hidden_layers ,		num_attention_heads=self.num_attention_heads ,		intermediate_size=self.intermediate_size ,		hidden_act=self.hidden_act ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		max_position_embeddings=self.max_position_embeddings ,		type_vocab_size=self.type_vocab_size ,		is_decoder=__snake_case ,		initializer_range=self.initializer_range ,		)

											return config, input_ids, token_type_ids, attention_mask







						def     __UpperCamelCase				(self							):
											snake_case_       :		int			       =       self.prepare_config_and_inputs()
											snake_case_       :		Dict			       =       config_and_inputs
											snake_case_       :		Optional[int]			       =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict







						def     __UpperCamelCase				(self							):
											snake_case_       :		Union[str, Any]			       =       self.prepare_config_and_inputs()
											snake_case_       :		Dict			       =       config_and_inputs

											snake_case_       :		int			       =       True
											snake_case_       :		int			       =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
											snake_case_       :		int			       =       ids_tensor([self.batch_size, self.seq_length] ,		vocab_size=2							)

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  __lowercase  (       UpperCamelCase__       ,     unittest.TestCase):







						"""simple docstring"""







						_A		:				List[Any]											=		True

						_A		:				Dict											=		(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Optional[int]			       =       FlaxRobertaPreLayerNormModelTester(self							)







						@slow
						def     __UpperCamelCase				(self							):

											for model_class_name in self.all_model_classes:
																snake_case_       :		List[str]			       =       model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,		from_pt=__snake_case							)
																snake_case_       :		Any			       =       model(np.ones((1, 1)							)							)
																self.assertIsNotNone(__snake_case							)







@require_flax
class  __lowercase  (       unittest.TestCase):







						"""simple docstring"""







						@slow
						def     __UpperCamelCase				(self							):
											snake_case_       :		List[Any]			       =       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,		from_pt=__snake_case							)

											snake_case_       :		int			       =       np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] ,		dtype=jnp.intaa							)
											snake_case_       :		Optional[int]			       =       model(__snake_case							)[0]
											snake_case_       :		str			       =       [1, 11, 5_02_65]
											self.assertEqual(list(output.shape							) ,		__snake_case							)
											# compare the actual values for a slice.
											snake_case_       :		List[Any]			       =       np.array(
											    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] ,		dtype=np.floataa							)
											self.assertTrue(np.allclose(output[:, :3, :3] ,		__snake_case ,		atol=1e-4							)							)







						@slow
						def     __UpperCamelCase				(self							):
											snake_case_       :		Optional[int]			       =       FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,		from_pt=__snake_case							)

											snake_case_       :		int			       =       np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] ,		dtype=jnp.intaa							)
											snake_case_       :		List[Any]			       =       model(__snake_case							)[0]
											# compare the actual values for a slice.
											snake_case_       :		List[Any]			       =       np.array(
											    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] ,		dtype=np.floataa							)
											self.assertTrue(np.allclose(output[:, :3, :3] ,		__snake_case ,		atol=1e-4							)							)










import os

import jsonlines
import numpy as np
from tqdm import tqdm


UpperCAmelCase_		:					Dict          =  2048
UpperCAmelCase_		:					int          =  4096
UpperCAmelCase_		:					Any          =  42
UpperCAmelCase_		:					Optional[int]          =  os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_		:					str          =  {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}




def 		lowerCAmelCase_ (						lowerCamelCase ):
				def choose_first(lowerCamelCase						,  lowerCamelCase=False ):
								assert isinstance(lowerCamelCase						,  lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												__magic_name__						:						List[str]										=answer[0]
												return {k: [answer[k]] for k in answer} if is_long_answer else answer
								for a in answer:
												if is_long_answer:
																__magic_name__						:						Tuple										={k: [a[k]] for k in a}
												if len(a["""start_token"""] ) > 0:
																break
								return a

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

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

				__magic_name__						:						int										=["""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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=_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
								__magic_name__						:						Any										=example["""document"""]["""tokens"""]
								__magic_name__						:						str										=[]
								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

				__magic_name__						:						Dict										=["""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

				__magic_name__						:						Tuple										=example["""document"""]["""tokens"""]
				__magic_name__						:						Optional[int]										=answer["""start_token"""]
				__magic_name__						:						List[Any]										=answer["""end_token"""]

				__magic_name__						:						Optional[Any]										=[]
				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
				__magic_name__						:						Optional[int]										=""" """.join(context[start_token:end_token] )

				# checking above code
				if assertion:
								__magic_name__						:						List[str]										=doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
								__magic_name__						:						str										=doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
								__magic_name__						:						Dict										=""" """.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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=2048						,  lowerCamelCase=4096						,  lowerCamelCase=True ):
				# overlap will be of doc_stride - q_len

				__magic_name__						:						Any										=get_context_and_ans(lowerCamelCase						,  assertion=lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=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"],
								    },
								}

				__magic_name__						:						List[Any]										=tokenizer(example["""question"""]["""text"""]						,  out["""context"""] ).input_ids
				__magic_name__						:						Dict										=input_ids.index(tokenizer.sep_token_id ) + 1

				# return yes/no
				if answer["category"][0] in ["yes", "no"]:  # category is list with one element
								__magic_name__						:						List[str]										=[]
								__magic_name__						:						int										=[]
								__magic_name__						:						List[str]										=input_ids[:q_len]
								__magic_name__						:						Dict										=range(lowerCamelCase						,  len(lowerCamelCase )						,  max_length - doc_stride )
								for i in doc_start_indices:
												__magic_name__						:						List[Any]										=i + max_length - q_len
												__magic_name__						:						Tuple										=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,
								    },
								}

				__magic_name__						:						int										=out["""context"""].split()
				__magic_name__						:						Any										=splitted_context[answer["""end_token"""]]
				__magic_name__						:						str										=len(
				    tokenizer(
				        """ """.join(splitted_context[: answer["""start_token"""]] )						,  add_special_tokens=lowerCamelCase						,  ).input_ids )
				__magic_name__						:						Optional[int]										=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
				__magic_name__						:						Union[str, Any]										=len(tokenizer(lowerCamelCase						,  add_special_tokens=lowerCamelCase ).input_ids )
				if num_sub_tokens > 1:
								answer["end_token"] += num_sub_tokens - 1

				__magic_name__						:						str										=input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
				__magic_name__						:						Dict										=answer["""start_token"""]
				__magic_name__						:						int										=answer["""end_token"""]

				if assertion:
								__magic_name__						:						Any										=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"],
								    },
								}

				__magic_name__						:						Any										=input_ids[:q_len]
				__magic_name__						:						Union[str, Any]										=range(lowerCamelCase						,  len(lowerCamelCase )						,  max_length - doc_stride )

				__magic_name__						:						Any										=[]
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						str										=[]  # null, yes, no, long, short
				for i in doc_start_indices:
								__magic_name__						:						List[Any]										=i + max_length - q_len
								__magic_name__						:						Dict										=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:
												__magic_name__						:						List[Any]										=start_token - i + q_len
												__magic_name__						:						Optional[Any]										=end_token - i + q_len
												answers_category.append(answer["""category"""][0] )  # ["short"] -> "short"
								else:
												__magic_name__						:						Optional[Any]										=-100
												__magic_name__						:						Optional[Any]										=-100
												answers_category.append("""null""" )
								__magic_name__						:						Optional[int]										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=2048						,  lowerCamelCase=4096						,  lowerCamelCase=False ):
				__magic_name__						:						List[Any]										=get_strided_contexts_and_ans(
				    lowerCamelCase						,  lowerCamelCase						,  doc_stride=lowerCamelCase						,  max_length=lowerCamelCase						,  assertion=lowerCamelCase						,  )

				return example




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with jsonlines.open(lowerCamelCase						,  """a""" ) as writer:
								for example in tqdm(lowerCamelCase						,  total=len(lowerCamelCase )						,  desc="""Saving samples ... """ ):
												__magic_name__						:						int										=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

							UpperCAmelCase_		:					Optional[int]          =  load_dataset("natural_questions")
							UpperCAmelCase_		:					Optional[int]          =  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

							UpperCAmelCase_		:					str          =  data["train" if PROCESS_TRAIN == "true" else "validation"]

							UpperCAmelCase_		:					Optional[int]          =  {
							    "tokenizer": tokenizer,
							    "doc_stride": DOC_STRIDE,
							    "max_length": MAX_LENGTH,
							    "assertion": False,
							}
							UpperCAmelCase_		:					int          =  data.map(prepare_inputs, fn_kwargs=fn_kwargs)
							UpperCAmelCase_		:					Optional[Any]          =  data.remove_columns(["annotations", "document", "id", "question"])
							print(data)

							np.random.seed(SEED)
							UpperCAmelCase_		:					int          =  "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
							save_to_disk(data, file_name=cache_file_name)




