
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule







class 			__A( a			):
				snake_case_						   =    42
				snake_case_						   =    jnp.floataa
				snake_case_						   =    True




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								super().setup()
								__a										=    nn.Dense(5		,     dtype=self.dtype				)




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    super().__call__(*_snake_case		,     **_snake_case				)
								__a										=    self.cls(outputs[2]				)
								return outputs[:2] + (cls_out,)







class 			__A( a			):
				snake_case_						   =    FlaxBigBirdForNaturalQuestionsModule



def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__						,    a__  )       ->  Dict:
				def cross_entropy(a__						,    a__						,    a__=None  ):
								__a										=    logits.shape[-1]
								__a										=    (labels[..., None] == jnp.arange(a__  )[None]).astype('''f4'''  )
								__a										=    jax.nn.log_softmax(a__						,    axis=-1  )
								__a										=    -jnp.sum(labels * logits						,    axis=-1  )
								if reduction is not None:
												__a										=    reduction(a__  )
								return loss

				__a										=    partial(a__						,    reduction=jnp.mean  )
				__a										=    cross_entropy(a__						,    a__  )
				__a										=    cross_entropy(a__						,    a__  )
				__a										=    cross_entropy(a__						,    a__  )
				return (start_loss + end_loss + pooled_loss) / 3







@dataclass
class 			__A:
				snake_case_						   =    "google/bigbird-roberta-base"
				snake_case_						   =    3_0_0_0
				snake_case_						   =    1_0_5_0_0

				snake_case_						   =    1_2_8
				snake_case_						   =    3

				snake_case_						   =    1
				snake_case_						   =    5

				# tx_args
				snake_case_						   =    3E-5
				snake_case_						   =    0.0
				snake_case_						   =    2_0_0_0_0
				snake_case_						   =    0.0_095

				snake_case_						   =    "bigbird-roberta-natural-questions"
				snake_case_						   =    "training-expt"
				snake_case_						   =    "data/nq-training.jsonl"
				snake_case_						   =    "data/nq-validation.jsonl"




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								os.makedirs(self.base_dir		,     exist_ok=_snake_case				)
								__a										=    os.path.join(self.base_dir		,     self.save_dir				)
								__a										=    self.batch_size_per_device * jax.device_count()







@dataclass
class 			__A:
				snake_case_						   =    42
				snake_case_						   =    4_0_9_6  # no dynamic padding on TPUs




				def __call__(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								__a										=    self.collate_fn(_snake_case				)
								__a										=    jax.tree_util.tree_map(_snake_case		,     _snake_case				)
								return batch




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								__a							,     __a										=    self.fetch_inputs(features['''input_ids''']				)
								__a										=    {
								    '''input_ids''': jnp.array(_snake_case		,     dtype=jnp.intaa				),
								    '''attention_mask''': jnp.array(_snake_case		,     dtype=jnp.intaa				),
								    '''start_labels''': jnp.array(features['''start_token''']		,     dtype=jnp.intaa				),
								    '''end_labels''': jnp.array(features['''end_token''']		,     dtype=jnp.intaa				),
								    '''pooled_labels''': jnp.array(features['''category''']		,     dtype=jnp.intaa				),
								}
								return batch




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    [self._fetch_inputs(_snake_case				) for ids in input_ids]
								return zip(*_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    [1 for _ in range(len(_snake_case				)				)]
								while len(_snake_case				) < self.max_length:
												input_ids.append(self.pad_id				)
												attention_mask.append(0				)
								return input_ids, attention_mask



def     __lowerCAmelCase			(			a__						,    a__						,    a__=None  )       ->  List[str]:
				if seed is not None:
								__a										=    dataset.shuffle(seed=a__  )
				for i in range(len(a__  ) // batch_size  ):
								__a										=    dataset[i * batch_size : (i + 1) * batch_size]
								yield dict(a__  )


@partial(jax.pmap						,    axis_name='''batch'''  )
def     __lowerCAmelCase			(			a__						,    a__						,    **a__  )       ->  Any:
				def loss_fn(a__  ):
								__a										=    model_inputs.pop('''start_labels'''  )
								__a										=    model_inputs.pop('''end_labels'''  )
								__a										=    model_inputs.pop('''pooled_labels'''  )

								__a										=    state.apply_fn(**a__						,    params=a__						,    dropout_rng=a__						,    train=a__  )
								__a							,     __a							,     __a										=    outputs

								return state.loss_fn(
								    a__						,    a__						,    a__						,    a__						,    a__						,    a__						,    )

				__a							,     __a										=    jax.random.split(a__  )
				__a										=    jax.value_and_grad(a__  )
				__a							,     __a										=    grad_fn(state.params  )
				__a										=    jax.lax.pmean({'''loss''': loss}						,    axis_name='''batch'''  )
				__a										=    jax.lax.pmean(a__						,    '''batch'''  )

				__a										=    state.apply_gradients(grads=a__  )
				return state, metrics, new_drp_rng


@partial(jax.pmap						,    axis_name='''batch'''  )
def     __lowerCAmelCase			(			a__						,    **a__  )       ->  str:
				__a										=    model_inputs.pop('''start_labels'''  )
				__a										=    model_inputs.pop('''end_labels'''  )
				__a										=    model_inputs.pop('''pooled_labels'''  )

				__a										=    state.apply_fn(**a__						,    params=state.params						,    train=a__  )
				__a							,     __a							,     __a										=    outputs

				__a										=    state.loss_fn(a__						,    a__						,    a__						,    a__						,    a__						,    a__  )
				__a										=    jax.lax.pmean({'''loss''': loss}						,    axis_name='''batch'''  )
				return metrics







class 			__A( train_state.TrainState			):
				snake_case_						   =    struct.field(pytree_node=a			)







@dataclass
class 			__A:
				snake_case_						   =    42
				snake_case_						   =    42
				snake_case_						   =    42
				snake_case_						   =    42
				snake_case_						   =    42
				snake_case_						   =    42
				snake_case_						   =    None




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case=None				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    model.params
								__a										=    TrainState.create(
								    apply_fn=model.__call__		,     params=_snake_case		,     tx=_snake_case		,     loss_fn=_snake_case		,     )
								if ckpt_dir is not None:
												__a							,     __a							,     __a							,     __a							,     __a										=    restore_checkpoint(_snake_case		,     _snake_case				)
												__a										=    {
												    '''lr''': args.lr,
												    '''init_lr''': args.init_lr,
												    '''warmup_steps''': args.warmup_steps,
												    '''num_train_steps''': num_train_steps,
												    '''weight_decay''': args.weight_decay,
												}
												__a							,     __a										=    build_tx(**_snake_case				)
												__a										=    train_state.TrainState(
												    step=_snake_case		,     apply_fn=model.__call__		,     params=_snake_case		,     tx=_snake_case		,     opt_state=_snake_case		,     )
												__a										=    args
												__a										=    data_collator
												__a										=    lr
												__a										=    params
								__a										=    jax_utils.replicate(_snake_case				)
								return state




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.args
								__a										=    len(_snake_case				) // args.batch_size

								__a										=    jax.random.PRNGKey(0				)
								__a										=    jax.random.split(_snake_case		,     jax.device_count()				)
								for epoch in range(args.max_epochs				):
												__a										=    jnp.array(0		,     dtype=jnp.floataa				)
												__a										=    get_batched_dataset(_snake_case		,     args.batch_size		,     seed=_snake_case				)
												__a										=    0
												for batch in tqdm(_snake_case		,     total=_snake_case		,     desc=F"""Running EPOCH-{epoch}"""				):
																__a										=    self.data_collator(_snake_case				)
																__a							,     __a							,     __a										=    self.train_step_fn(_snake_case		,     _snake_case		,     **_snake_case				)
																running_loss += jax_utils.unreplicate(metrics['''loss''']				)
																i += 1
																if i % args.logging_steps == 0:
																				__a										=    jax_utils.unreplicate(state.step				)
																				__a										=    running_loss.item() / i
																				__a										=    self.scheduler_fn(state_step - 1				)

																				__a										=    self.evaluate(_snake_case		,     _snake_case				)
																				__a										=    {
																				    '''step''': state_step.item(),
																				    '''eval_loss''': eval_loss.item(),
																				    '''tr_loss''': tr_loss,
																				    '''lr''': lr.item(),
																				}
																				tqdm.write(str(_snake_case				)				)
																				self.logger.log(_snake_case		,     commit=_snake_case				)

																if i % args.save_steps == 0:
																				self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""		,     state=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    get_batched_dataset(_snake_case		,     self.args.batch_size				)
								__a										=    len(_snake_case				) // self.args.batch_size
								__a										=    jnp.array(0		,     dtype=jnp.floataa				)
								__a										=    0
								for batch in tqdm(_snake_case		,     total=_snake_case		,     desc='''Evaluating ... '''				):
												__a										=    self.data_collator(_snake_case				)
												__a										=    self.val_step_fn(_snake_case		,     **_snake_case				)
												running_loss += jax_utils.unreplicate(metrics['''loss''']				)
												i += 1
								return running_loss / i




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    jax_utils.unreplicate(_snake_case				)
								print(F"""SAVING CHECKPOINT IN {save_dir}"""		,     end=''' ... '''				)
								self.model_save_fn(_snake_case		,     params=state.params				)
								with open(os.path.join(_snake_case		,     '''opt_state.msgpack'''				)		,     '''wb'''				) as f:
												f.write(to_bytes(state.opt_state				)				)
								joblib.dump(self.args		,     os.path.join(_snake_case		,     '''args.joblib'''				)				)
								joblib.dump(self.data_collator		,     os.path.join(_snake_case		,     '''data_collator.joblib'''				)				)
								with open(os.path.join(_snake_case		,     '''training_state.json'''				)		,     '''w'''				) as f:
												json.dump({'''step''': state.step.item()}		,     _snake_case				)
								print('''DONE'''				)



def     __lowerCAmelCase			(			a__						,    a__  )       ->  List[Any]:
				print(F"""RESTORING CHECKPOINT FROM {save_dir}"""						,    end=''' ... '''  )
				with open(os.path.join(a__						,    '''flax_model.msgpack'''  )						,    '''rb'''  ) as f:
								__a										=    from_bytes(state.params						,    f.read()  )

				with open(os.path.join(a__						,    '''opt_state.msgpack'''  )						,    '''rb'''  ) as f:
								__a										=    from_bytes(state.opt_state						,    f.read()  )

				__a										=    joblib.load(os.path.join(a__						,    '''args.joblib'''  )  )
				__a										=    joblib.load(os.path.join(a__						,    '''data_collator.joblib'''  )  )

				with open(os.path.join(a__						,    '''training_state.json'''  )						,    '''r'''  ) as f:
								__a										=    json.load(a__  )
				__a										=    training_state['''step''']

				print('''DONE'''  )
				return params, opt_state, step, args, data_collator


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  Optional[Any]:
				__a										=    num_train_steps - warmup_steps
				__a										=    optax.linear_schedule(init_value=a__						,    end_value=a__						,    transition_steps=a__  )
				__a										=    optax.linear_schedule(init_value=a__						,    end_value=1e-7						,    transition_steps=a__  )
				__a										=    optax.join_schedules(schedules=[warmup_fn, decay_fn]						,    boundaries=[warmup_steps]  )
				return lr






def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__  )       ->  str:
				def weight_decay_mask(a__  ):
								__a										=    traverse_util.flatten_dict(a__  )
								__a										=    {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
								return traverse_util.unflatten_dict(a__  )

				__a										=    scheduler_fn(a__						,    a__						,    a__						,    a__  )

				__a										=    optax.adamw(learning_rate=a__						,    weight_decay=a__						,    mask=a__  )
				return tx, lr



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DetaImageProcessor







class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]=7					,      __lowerCamelCase							:			Dict=3					,      __lowerCamelCase							:			Union[str, Any]=30					,      __lowerCamelCase							:			Union[str, Any]=400					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			Tuple=None					,      __lowerCamelCase							:			str=True					,      __lowerCamelCase							:			List[str]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			str=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			Any=True					,      __lowerCamelCase							:			Union[str, Any]=1 / 255					,      __lowerCamelCase							:			Optional[Any]=True					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
             lowerCamelCase__		:				Dict    =  parent
             lowerCamelCase__		:				Optional[int]    =  batch_size
             lowerCamelCase__		:				str    =  num_channels
             lowerCamelCase__		:				Optional[Any]    =  min_resolution
             lowerCamelCase__		:				List[Any]    =  max_resolution
             lowerCamelCase__		:				int    =  do_resize
             lowerCamelCase__		:				Union[str, Any]    =  size
             lowerCamelCase__		:				Union[str, Any]    =  do_normalize
             lowerCamelCase__		:				int    =  image_mean
             lowerCamelCase__		:				Optional[int]    =  image_std
             lowerCamelCase__		:				List[Any]    =  do_rescale
             lowerCamelCase__		:				Optional[Any]    =  rescale_factor
             lowerCamelCase__		:				Any    =  do_pad





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]=False       ):


             '''simple docstring'''



             if not batched:
                   lowerCamelCase__		:				Tuple    =  image_inputs[0]
                   if isinstance(__lowerCamelCase					,      Image.Image       ):
                         lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  image.size
                   else:
                         lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.shape[1], image.shape[2]
                   if w < h:
                         lowerCamelCase__		:				List[Any]    =  int(self.size["shortest_edge"] * h / w       )
                         lowerCamelCase__		:				Optional[Any]    =  self.size["shortest_edge"]
                   elif w > h:
                         lowerCamelCase__		:				List[Any]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				List[str]    =  int(self.size["shortest_edge"] * w / h       )
                   else:
                         lowerCamelCase__		:				Optional[int]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				Union[str, Any]    =  self.size["shortest_edge"]

             else:
                   lowerCamelCase__		:				Dict    =  []
                   for image in image_inputs:
                         lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  self.get_expected_values([image]       )
                         expected_values.append((expected_height, expected_width)       )
                   lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[0]       )[0]
                   lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[1]       )[1]

             return expected_height, expected_width







@require_torch
@require_vision
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			DetaImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  DetaImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "image_mean"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "image_std"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_resize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_rescale"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_pad"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "size"       )       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size					,      {"shortest_edge": 18, "longest_edge": 1333}       )
             self.assertEqual(image_processor.do_pad					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				List[str]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      Image.Image       )

             # Test not batched input
             lowerCamelCase__		:				Optional[int]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

             # Test batched
             lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )					,      )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             lowerCamelCase__		:				Optional[int]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      np.ndarray       )

             # Test not batched input
             lowerCamelCase__		:				Any    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

             # Test batched
             lowerCamelCase__		:				Optional[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )					,      )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				Dict    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      torch.Tensor       )

             # Test not batched input
             lowerCamelCase__		:				Dict    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

             # Test batched
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             self.assertEqual(
                 encoded_images.shape					,      (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )					,      )





       @slow
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Dict    =  json.loads(f.read()       )

             lowerCamelCase__		:				Any    =  {"image_id": 39769, "annotations": target}

             # encode them
             lowerCamelCase__		:				Union[str, Any]    =  DetaImageProcessor()
             lowerCamelCase__		:				List[str]    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[str]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				Tuple    =  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Union[str, Any]    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				int    =  torch.tensor([75, 75, 63, 65, 17, 17]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify orig_size
             lowerCamelCase__		:				int    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )





       @slow
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Tuple    =  json.loads(f.read()       )

             lowerCamelCase__		:				List[str]    =  {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

             lowerCamelCase__		:				Union[str, Any]    =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

             # encode them
             lowerCamelCase__		:				Tuple    =  DetaImageProcessor(format="coco_panoptic"       )
             lowerCamelCase__		:				Dict    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      masks_path=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[Any]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				List[Any]    =  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Any    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				int    =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				Tuple    =  torch.tensor([17, 17, 63, 75, 75, 93]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify masks
             lowerCamelCase__		:				Union[str, Any]    =  822873
             self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,      __lowerCamelCase       )
             # verify orig_size
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )


