"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =	{
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}



class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    """bigbird_pegasus"""
				_lowerCAmelCase  :     List[Any]      	=    ["""past_key_values"""]
				_lowerCAmelCase  :     Optional[int]      	=    {
				    """num_attention_heads""": """encoder_attention_heads""",
				    """hidden_size""": """d_model""",
				    """attention_probs_dropout_prob""": """attention_dropout""",
				}

				def __init__(					self		,     lowerCAmelCase=9_61_03		,     lowerCAmelCase=40_96		,     lowerCAmelCase=16		,     lowerCAmelCase=40_96		,     lowerCAmelCase=16		,     lowerCAmelCase=16		,     lowerCAmelCase=40_96		,     lowerCAmelCase=16		,     lowerCAmelCase=0.0		,     lowerCAmelCase=0.0		,     lowerCAmelCase=True		,     lowerCAmelCase=True		,     lowerCAmelCase="gelu_new"		,     lowerCAmelCase=10_24		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.0		,     lowerCAmelCase=0.0		,     lowerCAmelCase=0.02		,     lowerCAmelCase=2		,     lowerCAmelCase=0.0		,     lowerCAmelCase=True		,     lowerCAmelCase=0		,     lowerCAmelCase=2		,     lowerCAmelCase=1		,     lowerCAmelCase="block_sparse"		,     lowerCAmelCase=64		,     lowerCAmelCase=3		,     lowerCAmelCase=False		,     **lowerCAmelCase		,     ):






										"""simple docstring"""

										snake_case             =      vocab_size
										snake_case             =      max_position_embeddings
										snake_case             =      d_model
										snake_case             =      encoder_ffn_dim
										snake_case             =      encoder_layers
										snake_case             =      encoder_attention_heads
										snake_case             =      decoder_ffn_dim
										snake_case             =      decoder_layers
										snake_case             =      decoder_attention_heads
										snake_case             =      dropout
										snake_case             =      attention_dropout
										snake_case             =      activation_dropout
										snake_case             =      activation_function
										snake_case             =      init_std
										snake_case             =      encoder_layerdrop
										snake_case             =      decoder_layerdrop
										snake_case             =      classifier_dropout
										snake_case             =      use_cache
										snake_case             =      encoder_layers
										snake_case             =      scale_embedding  # scale factor will be sqrt(d_model) if True

										# extra config
										snake_case             =      attention_type
										snake_case             =      block_size
										snake_case             =      num_random_blocks
										snake_case             =      use_bias

										super().__init__(
										    pad_token_id=lowerCAmelCase		,     bos_token_id=lowerCAmelCase		,     eos_token_id=lowerCAmelCase		,     is_encoder_decoder=lowerCAmelCase		,     decoder_start_token_id=lowerCAmelCase		,     **lowerCAmelCase		,     )





class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case             =      OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]     )

																if self.use_past:
																						snake_case             =      {0: 'batch'}
																						snake_case             =      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																else:
																						snake_case             =      {0: 'batch', 1: 'decoder_sequence'}
																						snake_case             =      {0: 'batch', 1: 'decoder_sequence'}

																if self.use_past:
																						self.fill_with_past_key_values_(lowerCAmelCase		,     direction='inputs'     )
										elif self.task == "causal-lm":
																# TODO: figure this case out.
																snake_case             =      OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]     )
																if self.use_past:
																						snake_case       ,snake_case             =      self.num_layers
																						for i in range(lowerCAmelCase     ):
																												snake_case             =      {0: 'batch', 2: 'past_sequence + sequence'}
																												snake_case             =      {0: 'batch', 2: 'past_sequence + sequence'}
										else:
																snake_case             =      OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
																        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
																    ]     )

										return common_inputs

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case             =      super().outputs
										else:
																snake_case             =      super(lowerCAmelCase		,     self     ).outputs
																if self.use_past:
																						snake_case       ,snake_case             =      self.num_layers
																						for i in range(lowerCAmelCase     ):
																												snake_case             =      {0: 'batch', 2: 'past_sequence + sequence'}
																												snake_case             =      {0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										snake_case             =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )

										# Generate decoder inputs
										snake_case             =      seq_length if not self.use_past else 1
										snake_case             =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )
										snake_case             =      {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										snake_case             =      dict(**lowerCAmelCase		,     **lowerCAmelCase     )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
																else:
																						import torch
																snake_case       ,snake_case             =      common_inputs['input_ids'].shape
																snake_case             =      common_inputs['decoder_input_ids'].shape[1]
																snake_case       ,snake_case             =      self.num_attention_heads
																snake_case             =      (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																snake_case             =      decoder_seq_length + 3
																snake_case             =      (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																snake_case             =      torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase		,     lowerCAmelCase     )]		,     dim=1     )

																snake_case             =      []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																snake_case       ,snake_case             =      self.num_layers
																snake_case             =      min(lowerCAmelCase		,     lowerCAmelCase     )
																snake_case             =      max(lowerCAmelCase		,     lowerCAmelCase     ) - min_num_layers
																snake_case             =      'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																for _ in range(lowerCAmelCase     ):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(lowerCAmelCase     ),
																						        torch.zeros(lowerCAmelCase     ),
																						        torch.zeros(lowerCAmelCase     ),
																						        torch.zeros(lowerCAmelCase     ),
																						    )     )
																# TODO: test this.
																snake_case             =      encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																for _ in range(lowerCAmelCase		,     lowerCAmelCase     ):
																						common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase     ), torch.zeros(lowerCAmelCase     ))     )
										return common_inputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										snake_case             =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
																else:
																						import torch
																snake_case       ,snake_case             =      common_inputs['input_ids'].shape
																# Not using the same length for past_key_values
																snake_case             =      seqlen + 2
																snake_case       ,snake_case             =      self.num_layers
																snake_case       ,snake_case             =      self.num_attention_heads
																snake_case             =      (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																snake_case             =      common_inputs['attention_mask'].dtype
																snake_case             =      torch.cat(
																    [common_inputs['attention_mask'], torch.ones(lowerCAmelCase		,     lowerCAmelCase		,     dtype=lowerCAmelCase     )]		,     dim=1     )
																snake_case             =      [
																    (torch.zeros(lowerCAmelCase     ), torch.zeros(lowerCAmelCase     )) for _ in range(lowerCAmelCase     )
																]
										return common_inputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										snake_case             =      compute_effective_axis_dimension(
										    lowerCAmelCase		,     fixed_dimension=OnnxConfig.default_fixed_batch		,     num_token_to_add=0     )

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										snake_case             =      tokenizer.num_special_tokens_to_add(lowerCAmelCase     )
										snake_case             =      compute_effective_axis_dimension(
										    lowerCAmelCase		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=lowerCAmelCase     )

										# Generate dummy inputs according to compute batch and sequence
										snake_case             =      [' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
										snake_case             =      dict(tokenizer(lowerCAmelCase		,     return_tensors=lowerCAmelCase     )     )
										return common_inputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = -1		,     lowerCAmelCase = -1		,     lowerCAmelCase = False		,     lowerCAmelCase = None		,     ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case             =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    lowerCAmelCase		,     batch_size=lowerCAmelCase		,     seq_length=lowerCAmelCase		,     is_pair=lowerCAmelCase		,     framework=lowerCAmelCase     )

										elif self.task == "causal-lm":
																snake_case             =      self._generate_dummy_inputs_for_causal_lm(
																    lowerCAmelCase		,     batch_size=lowerCAmelCase		,     seq_length=lowerCAmelCase		,     is_pair=lowerCAmelCase		,     framework=lowerCAmelCase     )
										else:
																snake_case             =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    lowerCAmelCase		,     batch_size=lowerCAmelCase		,     seq_length=lowerCAmelCase		,     is_pair=lowerCAmelCase		,     framework=lowerCAmelCase     )

										return common_inputs

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case             =      super()._flatten_past_key_values_(lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )
										else:
																snake_case             =      super(lowerCAmelCase		,     self     )._flatten_past_key_values_(
																    lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )



"""simple docstring"""
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SwinaSRImageProcessor
					from transformers.image_transforms import get_image_size



class 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=7		,     lowerCAmelCase=3		,     lowerCAmelCase=18		,     lowerCAmelCase=30		,     lowerCAmelCase=4_00		,     lowerCAmelCase=True		,     lowerCAmelCase=1 / 2_55		,     lowerCAmelCase=True		,     lowerCAmelCase=8		,     ):






										"""simple docstring"""

										snake_case             =      parent
										snake_case             =      batch_size
										snake_case             =      num_channels
										snake_case             =      image_size
										snake_case             =      min_resolution
										snake_case             =      max_resolution
										snake_case             =      do_rescale
										snake_case             =      rescale_factor
										snake_case             =      do_pad
										snake_case             =      pad_size

				def 		snake_case			(					self     ):






										"""simple docstring"""

										return {
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										    "pad_size": self.pad_size,
										}

				def 		snake_case			(					self		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False     ):






										"""simple docstring"""

										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																snake_case             =      []
																for i in range(self.batch_size     ):
																						image_inputs.append(
																						    np.random.randint(
																						        2_55		,     size=(self.num_channels, self.max_resolution, self.max_resolution)		,     dtype=np.uinta     )     )
										else:
																snake_case             =      []
																for i in range(self.batch_size     ):
																						snake_case       ,snake_case             =      np.random.choice(np.arange(self.min_resolution		,     self.max_resolution     )		,     2     )
																						image_inputs.append(np.random.randint(2_55		,     size=(self.num_channels, width, height)		,     dtype=np.uinta     )     )

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																snake_case             =      [Image.fromarray(np.moveaxis(lowerCAmelCase		,     0		,     -1     )     ) for x in image_inputs]

										if torchify:
																snake_case             =      [torch.from_numpy(lowerCAmelCase     ) for x in image_inputs]

										return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    SwinaSRImageProcessor if is_vision_available() else None

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      SwinaSRImageProcessingTester(self     )

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'do_rescale'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'rescale_factor'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'do_pad'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'pad_size'     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      get_image_size(lowerCAmelCase     )
										snake_case             =      self.image_processor_tester.pad_size

										snake_case             =      (old_height // size + 1) * size - old_height
										snake_case             =      (old_width // size + 1) * size - old_width
										return old_height + pad_height, old_width + pad_width

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     Image.Image     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(np.array(image_inputs[0]     )     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase		,     numpify=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     np.ndarray     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(image_inputs[0]     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase		,     torchify=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     torch.Tensor     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(image_inputs[0]     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )


