



"""simple docstring"""






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						self.config_class(**self.inputs_dict   )
										self.parent.assertTrue(hasattr(a    ,       "hidden_sizes"   )   )
										self.parent.assertTrue(hasattr(a    ,       "num_encoder_blocks"   )   )



class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a=13    ,       a=64    ,       a=3    ,       a=4    ,       a=[2, 2, 2, 2]    ,       a=[8, 4, 2, 1]    ,       a=[16, 32, 64, 1_28]    ,       a=[1, 4, 8, 16]    ,       a=False    ,       a=True    ,       a="gelu"    ,       a=0.1    ,       a=0.02    ,       a=3    ,       a=None    ,       ):




										"""simple docstring"""





										snake_case_						:Any								=						parent
										snake_case_						:Any								=						batch_size
										snake_case_						:Optional[int]								=						image_size
										snake_case_						:str								=						num_channels
										snake_case_						:Optional[Any]								=						num_encoder_blocks
										snake_case_						:Optional[int]								=						sr_ratios
										snake_case_						:Optional[Any]								=						depths
										snake_case_						:int								=						hidden_sizes
										snake_case_						:Tuple								=						downsampling_rates
										snake_case_						:Union[str, Any]								=						is_training
										snake_case_						:Any								=						use_labels
										snake_case_						:List[Any]								=						hidden_act
										snake_case_						:Optional[Any]								=						hidden_dropout_prob
										snake_case_						:Optional[Any]								=						initializer_range
										snake_case_						:List[str]								=						num_labels
										snake_case_						:Tuple								=						scope







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										snake_case_						:Dict								=						None
										if self.use_labels:
															snake_case_						:List[Any]								=						ids_tensor([self.batch_size, self.image_size, self.image_size]    ,       self.num_labels   )

										snake_case_						:int								=						PoolFormerConfig(
										    image_size=self.image_size    ,       num_channels=self.num_channels    ,       num_encoder_blocks=self.num_encoder_blocks    ,       depths=self.depths    ,       hidden_sizes=self.hidden_sizes    ,       hidden_act=self.hidden_act    ,       hidden_dropout_prob=self.hidden_dropout_prob    ,       initializer_range=self.initializer_range    ,       )

										return config, pixel_values, labels







					def 		_a (      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:str								=						PoolFormerModel(config=a   )
										model.to(a   )
										model.eval()
										snake_case_						:Optional[Any]								=						model(a   )
										snake_case_						:str								=						self.image_size // 32.0
										self.parent.assertEqual(
										    result.last_hidden_state.shape    ,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						self.prepare_config_and_inputs()
										snake_case_	,  snake_case_	,  snake_case_						:Optional[int]								=						config_and_inputs
										snake_case_						:int								=						{"pixel_values": pixel_values}
										return config, inputs_dict



@require_torch
class 	__lowerCAmelCase							(__UpperCamelCase					,__UpperCamelCase					,unittest.TestCase  ):


					'''simple docstring'''


					a__        =     (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
					a__        =     (
					    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					a__        =     False
					a__        =     False
					a__        =     False
					a__        =     False
					a__        =     False







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:List[Any]								=						PoolFormerModelTester(self   )
										snake_case_						:List[Any]								=						PoolFormerConfigTester(self    ,       config_class=a   )







					def 		_a (      self   ):




										"""simple docstring"""





										self.config_tester.run_common_tests()







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:str								=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a   )







					@unittest.skip("PoolFormer does not use inputs_embeds"   )
					def 		_a (      self   ):




										"""simple docstring"""





										pass







					@unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"   )
					def 		_a (      self   ):




										"""simple docstring"""





										pass







					def 		_a (      self   ):




										"""simple docstring"""





										def check_hidden_states_output(a    ,       a    ,       a   ):
															snake_case_						:Dict								=						model_class(a   )
															model.to(a   )
															model.eval()

															with torch.no_grad():
																				snake_case_						:int								=						model(**self._prepare_for_class(a    ,       a   )   )

															snake_case_						:Dict								=						outputs.hidden_states

															snake_case_						:Tuple								=						self.model_tester.num_encoder_blocks
															self.assertEqual(len(a   )    ,       a   )

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:]   )    ,       [
															        self.model_tester.hidden_sizes[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]    ,       )

										snake_case_	,  snake_case_						:Optional[int]								=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															snake_case_						:List[str]								=						True
															check_hidden_states_output(a    ,       a    ,       a   )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_						:Union[str, Any]								=						True

															check_hidden_states_output(a    ,       a    ,       a   )







					def 		_a (      self   ):




										"""simple docstring"""





										if not self.model_tester.is_training:
															return

										snake_case_	,  snake_case_						:Optional[Any]								=						self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_						:Optional[int]								=						True

										for model_class in self.all_model_classes:
															if model_class in get_values(a   ):
																				continue
															snake_case_						:Any								=						model_class(a   )
															model.to(a   )
															model.train()
															snake_case_						:List[Any]								=						self._prepare_for_class(a    ,       a    ,       return_labels=a   )
															snake_case_						:List[Any]								=						model(**a   ).loss
															loss.backward()







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_	,  snake_case_						:Any								=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															snake_case_						:Optional[Any]								=						model_class(a   )
															snake_case_						:List[Any]								=						inspect.signature(model.forward   )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case_						:Optional[int]								=						[*signature.parameters.keys()]

															snake_case_						:str								=						["pixel_values"]
															self.assertListEqual(arg_names[:1]    ,       a   )







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_						:Optional[Any]								=						PoolFormerModel.from_pretrained(a   )
															self.assertIsNotNone(a   )



def      A      (			):




					"""simple docstring"""
					snake_case_						:int								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
					return image

@require_torch
class 	__lowerCAmelCase							(unittest.TestCase  ):


					'''simple docstring'''


					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:List[Any]								=						PoolFormerImageProcessor()
										snake_case_						:int								=						PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12"   ).to(a   )

										snake_case_						:Union[str, Any]								=						image_processor(images=prepare_img()    ,       return_tensors="pt"   ).to(a   )

										# forward pass
										with torch.no_grad():
															snake_case_						:List[Any]								=						model(**a   )

										# verify the logits
										snake_case_						:Dict								=						torch.Size((1, 10_00)   )
										self.assertEqual(outputs.logits.shape    ,       a   )

										snake_case_						:str								=						torch.tensor([-0.6113, 0.1685, -0.0492]   ).to(a   )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,       a    ,       atol=1e-4   )   )






"""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


__UpperCAmelCase      :      Union[str, Any]   					=					logging.get_logger(__name__)

__UpperCAmelCase      :      str   					=					{
    '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							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     'bigbird_pegasus'
					a__        =     ['past_key_values']
					a__        =     {
					    'num_attention_heads': 'encoder_attention_heads',
					    'hidden_size': 'd_model',
					    'attention_probs_dropout_prob': 'attention_dropout',
					}







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




										"""simple docstring"""





										snake_case_						:Optional[int]								=						vocab_size
										snake_case_						:Any								=						max_position_embeddings
										snake_case_						:List[Any]								=						d_model
										snake_case_						:Any								=						encoder_ffn_dim
										snake_case_						:str								=						encoder_layers
										snake_case_						:Union[str, Any]								=						encoder_attention_heads
										snake_case_						:Any								=						decoder_ffn_dim
										snake_case_						:Dict								=						decoder_layers
										snake_case_						:List[Any]								=						decoder_attention_heads
										snake_case_						:Any								=						dropout
										snake_case_						:Union[str, Any]								=						attention_dropout
										snake_case_						:Optional[int]								=						activation_dropout
										snake_case_						:int								=						activation_function
										snake_case_						:int								=						init_std
										snake_case_						:Union[str, Any]								=						encoder_layerdrop
										snake_case_						:Dict								=						decoder_layerdrop
										snake_case_						:Any								=						classifier_dropout
										snake_case_						:Any								=						use_cache
										snake_case_						:Any								=						encoder_layers
										snake_case_						:int								=						scale_embedding  # scale factor will be sqrt(d_model) if True

										# extra config
										snake_case_						:Optional[int]								=						attention_type
										snake_case_						:int								=						block_size
										snake_case_						:Optional[int]								=						num_random_blocks
										snake_case_						:List[str]								=						use_bias

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



class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					@property
					def 		_a (      self   ):




										"""simple docstring"""





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

															if self.use_past:
																				snake_case_						:int								=						{0: "batch"}
																				snake_case_						:List[str]								=						{0: "batch", 1: "past_decoder_sequence + sequence"}
															else:
																				snake_case_						:Optional[int]								=						{0: "batch", 1: "decoder_sequence"}
																				snake_case_						:Optional[Any]								=						{0: "batch", 1: "decoder_sequence"}

															if self.use_past:
																				self.fill_with_past_key_values_(a    ,       direction="inputs"   )
										elif self.task == "causal-lm":
															# TODO: figure this case out.
															snake_case_						:List[str]								=						OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]   )
															if self.use_past:
																				snake_case_	,  snake_case_						:Tuple								=						self.num_layers
																				for i in range(a   ):
																									snake_case_						:int								=						{0: "batch", 2: "past_sequence + sequence"}
																									snake_case_						:List[Any]								=						{0: "batch", 2: "past_sequence + sequence"}
										else:
															snake_case_						:List[Any]								=						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 		_a (      self   ):




										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
															snake_case_						:Optional[int]								=						super().outputs
										else:
															snake_case_						:List[str]								=						super(a    ,       self   ).outputs
															if self.use_past:
																				snake_case_	,  snake_case_						:int								=						self.num_layers
																				for i in range(a   ):
																									snake_case_						:Optional[Any]								=						{0: "batch", 2: "past_sequence + sequence"}
																									snake_case_						:Tuple								=						{0: "batch", 2: "past_sequence + sequence"}
										return common_outputs







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:int								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a    ,       a    ,       a    ,       a    ,       a   )

										# Generate decoder inputs
										snake_case_						:List[str]								=						seq_length if not self.use_past else 1
										snake_case_						:str								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a    ,       a    ,       a    ,       a    ,       a   )
										snake_case_						:Union[str, Any]								=						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										snake_case_						:Tuple								=						dict(**a    ,       **a   )

										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_						:Optional[int]								=						common_inputs["input_ids"].shape
															snake_case_						:Optional[int]								=						common_inputs["decoder_input_ids"].shape[1]
															snake_case_	,  snake_case_						:Optional[int]								=						self.num_attention_heads
															snake_case_						:str								=						(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															snake_case_						:Any								=						decoder_seq_length + 3
															snake_case_						:int								=						(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															snake_case_						:Union[str, Any]								=						torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(a    ,       a   )]    ,       dim=1   )

															snake_case_						:Union[str, Any]								=						[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															snake_case_	,  snake_case_						:Tuple								=						self.num_layers
															snake_case_						:int								=						min(a    ,       a   )
															snake_case_						:Optional[Any]								=						max(a    ,       a   ) - min_num_layers
															snake_case_						:List[Any]								=						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:List[str]								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a    ,       a    ,       a    ,       a    ,       a   )

										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_						:Union[str, Any]								=						common_inputs["input_ids"].shape
															# Not using the same length for past_key_values
															snake_case_						:str								=						seqlen + 2
															snake_case_	,  snake_case_						:Union[str, Any]								=						self.num_layers
															snake_case_	,  snake_case_						:Any								=						self.num_attention_heads
															snake_case_						:List[str]								=						(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															snake_case_						:Optional[Any]								=						common_inputs["attention_mask"].dtype
															snake_case_						:Tuple								=						torch.cat(
															    [common_inputs["attention_mask"], torch.ones(a    ,       a    ,       dtype=a   )]    ,       dim=1   )
															snake_case_						:Optional[int]								=						[
															    (torch.zeros(a   ), torch.zeros(a   )) for _ in range(a   )
															]
										return common_inputs







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						compute_effective_axis_dimension(
										    a    ,       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_						:List[Any]								=						tokenizer.num_special_tokens_to_add(a   )
										snake_case_						:Tuple								=						compute_effective_axis_dimension(
										    a    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=a   )

										# Generate dummy inputs according to compute batch and sequence
										snake_case_						:Any								=						[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
										snake_case_						:List[str]								=						dict(tokenizer(a    ,       return_tensors=a   )   )
										return common_inputs







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





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

										elif self.task == "causal-lm":
															snake_case_						:Optional[Any]								=						self._generate_dummy_inputs_for_causal_lm(
															    a    ,       batch_size=a    ,       seq_length=a    ,       is_pair=a    ,       framework=a   )
										else:
															snake_case_						:List[Any]								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    a    ,       batch_size=a    ,       seq_length=a    ,       is_pair=a    ,       framework=a   )

										return common_inputs







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
															snake_case_						:str								=						super()._flatten_past_key_values_(a    ,       a    ,       a    ,       a   )
										else:
															snake_case_						:Any								=						super(a    ,       self   )._flatten_past_key_values_(
															    a    ,       a    ,       a    ,       a   )

