
import os

import jsonlines
import numpy as np
from tqdm import tqdm


_lowercase											=    2048
_lowercase											=    4096
_lowercase											=    42
_lowercase											=    os.environ.pop("""PROCESS_TRAIN""", """false""")
_lowercase											=    {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]				)			->						Union[str, Any]:
       def choose_first(UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     Any=False				):
              assert isinstance(UpperCAmelCase_			,							UpperCAmelCase_				)
              if len(UpperCAmelCase_				) == 1:
                     SCREAMING_SNAKE_CASE_							:				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:
                            SCREAMING_SNAKE_CASE_							:				str		={k: [a[k]] for k in a}
                     if len(a['''start_token''']				) > 0:
                            break
              return a

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

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

       SCREAMING_SNAKE_CASE_							:				str		=['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
       if not all(isinstance(answer[k]			,							UpperCAmelCase_				) for k in cols				):
              raise ValueError('''Issue in ID'''			,							example['''id''']				)

       return answer
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Optional[Any]=False				)			->						Dict:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=_get_single_answer(UpperCAmelCase_				)
       # 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
              SCREAMING_SNAKE_CASE_							:				Any		=example['''document''']['''tokens''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=[]
              for i in range(len(doc['''token''']				)				):
                     if not doc["is_html"][i]:
                            context.append(doc['''token'''][i]				)
              return {
                  "context": " ".join(UpperCAmelCase_				),
                  "answer": {
                      "start_token": -1_0_0,  # ignore index in cross-entropy
                      "end_token": -1_0_0,  # 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

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''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

       SCREAMING_SNAKE_CASE_							:				List[Any]		=example['''document''']['''tokens''']
       SCREAMING_SNAKE_CASE_							:				List[Any]		=answer['''start_token''']
       SCREAMING_SNAKE_CASE_							:				str		=answer['''end_token''']

       SCREAMING_SNAKE_CASE_							:				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
       SCREAMING_SNAKE_CASE_							:				Tuple		=''' '''.join(context[start_token:end_token]				)

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

       return {
           "context": " ".join(UpperCAmelCase_				),
           "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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     Optional[int]=2_0_4_8			,							UpperCAmelCase_	:     Optional[int]=4_0_9_6			,							UpperCAmelCase_	:     List[Any]=True				)			->						List[Any]:
       # overlap will be of doc_stride - q_len

       SCREAMING_SNAKE_CASE_							:				List[Any]		=get_context_and_ans(UpperCAmelCase_			,							assertion=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=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"],
                  },
              }

       SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer(example['''question''']['''text''']			,							out['''context''']				).input_ids
       SCREAMING_SNAKE_CASE_							:				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
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[]
              SCREAMING_SNAKE_CASE_							:				Dict		=input_ids[:q_len]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=range(UpperCAmelCase_			,							len(UpperCAmelCase_				)			,							max_length - doc_stride				)
              for i in doc_start_indices:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=i + max_length - q_len
                     SCREAMING_SNAKE_CASE_							:				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": [-1_0_0] * len(UpperCAmelCase_				),
                      "end_token": [-1_0_0] * len(UpperCAmelCase_				),
                      "category": category,
                  },
              }

       SCREAMING_SNAKE_CASE_							:				Dict		=out['''context'''].split()
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=splitted_context[answer['''end_token''']]
       SCREAMING_SNAKE_CASE_							:				str		=len(
           tokenizer(
               ''' '''.join(splitted_context[: answer['''start_token''']]				)			,							add_special_tokens=UpperCAmelCase_			,							).input_ids				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=len(
           tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]				)			,							add_special_tokens=UpperCAmelCase_				).input_ids				)

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

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

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=answer['''start_token''']
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=answer['''end_token''']

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

       if len(UpperCAmelCase_				) <= 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"],
                  },
              }

       SCREAMING_SNAKE_CASE_							:				List[Any]		=input_ids[:q_len]
       SCREAMING_SNAKE_CASE_							:				int		=range(UpperCAmelCase_			,							len(UpperCAmelCase_				)			,							max_length - doc_stride				)

       SCREAMING_SNAKE_CASE_							:				Tuple		=[]
       SCREAMING_SNAKE_CASE_							:				str		=[]
       SCREAMING_SNAKE_CASE_							:				int		=[]
       SCREAMING_SNAKE_CASE_							:				int		=[]  # null, yes, no, long, short
       for i in doc_start_indices:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=i + max_length - q_len
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=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:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=start_token - i + q_len
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=end_token - i + q_len
                     answers_category.append(answer['''category'''][0]				)  # ["short"] -> "short"
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=-1_0_0
                     SCREAMING_SNAKE_CASE_							:				Any		=-1_0_0
                     answers_category.append('''null'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=inputs[-1][start_token : end_token + 1]

              answers_start_token.append(UpperCAmelCase_				)
              answers_end_token.append(UpperCAmelCase_				)
              if assertion:
                     if new != old and new != [tokenizer.cls_token_id]:
                            print('''ISSUE in strided for ID:'''			,							example['''id''']				)
                            print('''New:'''			,							tokenizer.decode(UpperCAmelCase_				)				)
                            print('''Old:'''			,							tokenizer.decode(UpperCAmelCase_				)			,							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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Optional[Any]=2_0_4_8			,							UpperCAmelCase_	:     int=4_0_9_6			,							UpperCAmelCase_	:     Dict=False				)			->						List[Any]:
       SCREAMING_SNAKE_CASE_							:				int		=get_strided_contexts_and_ans(
           UpperCAmelCase_			,							UpperCAmelCase_			,							doc_stride=UpperCAmelCase_			,							max_length=UpperCAmelCase_			,							assertion=UpperCAmelCase_			,							)

       return example
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     Dict				)			->						List[str]:
       with jsonlines.open(UpperCAmelCase_			,							'''a'''				) as writer:
              for example in tqdm(UpperCAmelCase_			,							total=len(UpperCAmelCase_				)			,							desc='''Saving samples ... '''				):
                     SCREAMING_SNAKE_CASE_							:				Tuple		=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

 _lowercase											=    load_dataset("""natural_questions""")
 _lowercase											=    BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

 _lowercase											=    data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

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

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


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

              if hasattr(__A						,       '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

              assert list(sample.shape				) == [1, 768]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
