




'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, 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 torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class 						a				:


				def __init__(		self    :					Tuple       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					str=3       ,				lowercase_    :					Optional[int]=32       ,				lowercase_    :					str=3       ,				lowercase_    :					int=10       ,				lowercase_    :					int=[10, 20, 30, 40]       ,				lowercase_    :					Optional[Any]=[1, 1, 2, 1]       ,				lowercase_    :					List[str]=True       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					int="relu"       ,				lowercase_    :					Dict=3       ,				lowercase_    :					Dict=None       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				image_size
									snake_case_									=				num_channels
									snake_case_									=				embeddings_size
									snake_case_									=				hidden_sizes
									snake_case_									=				depths
									snake_case_									=				is_training
									snake_case_									=				use_labels
									snake_case_									=				hidden_act
									snake_case_									=				num_labels
									snake_case_									=				scope
									snake_case_									=				len(lowercase_					)


				def   A_ (		self    :					Any					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

									snake_case_									=				self.get_config()

									return config, pixel_values, labels


				def   A_ (		self    :					Optional[Any]					):
									return RegNetConfig(
									    num_channels=self.num_channels       ,				embeddings_size=self.embeddings_size       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				hidden_act=self.hidden_act       ,				num_labels=self.num_labels       ,				)


				def   A_ (		self    :					int       ,				lowercase_    :					List[str]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[int]					):
									snake_case_									=				RegNetModel(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_					)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Tuple					):
									snake_case_									=				self.num_labels
									snake_case_									=				RegNetForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_       ,				labels=lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)					)


				def   A_ (		self    :					str					):
									snake_case_									=				self.prepare_config_and_inputs()
									snake_case_			,snake_case_			,snake_case_									=				config_and_inputs
									snake_case_									=				{'''pixel_values''': pixel_values}
									return config, inputs_dict







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				snake_case_      =		(
				    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					Tuple					):
									snake_case_									=				RegNetModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()


				def   A_ (		self    :					List[Any]					):
									return


				@unittest.skip(reason='''RegNet does not use inputs_embeds'''					)
				def   A_ (		self    :					Any					):
									pass


				@unittest.skip(reason='''RegNet does not support input and output embeddings'''					)
				def   A_ (		self    :					Optional[Any]					):
									pass


				def   A_ (		self    :					Dict					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_									=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]       ,				lowercase_					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_					)


				def   A_ (		self    :					Optional[Any]					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														snake_case_									=				model_class(config=lowercase_					)
														for name, module in model.named_modules():
																			if isinstance(lowercase_       ,				(nn.BatchNormad, nn.GroupNorm)					):
																								self.assertTrue(
																								    torch.all(module.weight == 1					)       ,				msg=F"Parameter {name} of model {model_class} seems not properly initialized"       ,				)
																								self.assertTrue(
																								    torch.all(module.bias == 0					)       ,				msg=F"Parameter {name} of model {model_class} seems not properly initialized"       ,				)


				def   A_ (		self    :					List[str]					):
									def check_hidden_states_output(lowercase_    :					Optional[int]       ,				lowercase_    :					Tuple       ,				lowercase_    :					List[str]					):
														snake_case_									=				model_class(lowercase_					)
														model.to(lowercase_					)
														model.eval()

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

														snake_case_									=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														snake_case_									=				self.model_tester.num_stages
														self.assertEqual(len(lowercase_					)       ,				expected_num_stages + 1					)

														# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]					)       ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,				)

									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_									=				['''basic''', '''bottleneck''']
									for model_class in self.all_model_classes:
														for layer_type in layers_type:
																			snake_case_									=				layer_type
																			snake_case_									=				True
																			check_hidden_states_output(lowercase_       ,				lowercase_       ,				lowercase_					)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			snake_case_									=				True

																			check_hidden_states_output(lowercase_       ,				lowercase_       ,				lowercase_					)


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_					)


				@slow
				def   A_ (		self    :					List[Any]					):
									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_									=				RegNetModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)







def 		__magic_name__    (	)		->					Any:

					'''simple docstring'''

					snake_case_									=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image






@require_torch
@require_vision
class 						a				(   unittest.TestCase     ):


				@cached_property
				def   A_ (		self    :					Any					):
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
									    if is_vision_available()
									    else None
									)


				@slow
				def   A_ (		self    :					Tuple					):
									snake_case_									=				RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(lowercase_					)

									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(images=lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)

									# forward pass
									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									# verify the logits
									snake_case_									=				torch.Size((1, 1000)					)
									self.assertEqual(outputs.logits.shape       ,				lowercase_					)

									snake_case_									=				torch.tensor([-0.4180, -1.5051, -3.4836]					).to(lowercase_					)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowercase_       ,				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


a  :				Dict									=  logging.get_logger(__name__)

a  :				List[str]									=  {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"marian"
				snake_case_      =		["past_key_values"]
				snake_case_      =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


				def __init__(		self    :					List[Any]       ,				lowercase_    :					Optional[Any]=5_8101       ,				lowercase_    :					Dict=None       ,				lowercase_    :					List[str]=1024       ,				lowercase_    :					Optional[Any]=12       ,				lowercase_    :					int=4096       ,				lowercase_    :					Any=16       ,				lowercase_    :					Optional[int]=12       ,				lowercase_    :					str=4096       ,				lowercase_    :					Union[str, Any]=16       ,				lowercase_    :					Dict=0.0       ,				lowercase_    :					Union[str, Any]=0.0       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Union[str, Any]=True       ,				lowercase_    :					int="gelu"       ,				lowercase_    :					Dict=1024       ,				lowercase_    :					int=0.1       ,				lowercase_    :					Tuple=0.0       ,				lowercase_    :					Tuple=0.0       ,				lowercase_    :					Tuple=0.02       ,				lowercase_    :					int=5_8100       ,				lowercase_    :					Optional[Any]=False       ,				lowercase_    :					Any=5_8100       ,				lowercase_    :					Optional[int]=0       ,				lowercase_    :					Dict=0       ,				lowercase_    :					List[str]=True       ,				**lowercase_    :					Any       ,				):
									snake_case_									=				vocab_size
									snake_case_									=				decoder_vocab_size or 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_									=				use_cache
									snake_case_									=				encoder_layers
									snake_case_									=				scale_embedding  # scale factor will be sqrt(d_model) if True
									snake_case_									=				share_encoder_decoder_embeddings
									super().__init__(
									    pad_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				is_encoder_decoder=lowercase_       ,				decoder_start_token_id=lowercase_       ,				forced_eos_token_id=lowercase_       ,				**lowercase_       ,				)







class 						a				(   _lowerCamelCase     ):


				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def   A_ (		self    :					Union[str, Any]					):
									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_(lowercase_       ,				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(lowercase_					):
																								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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def   A_ (		self    :					Dict					):
									if self.task in ["default", "seq2seq-lm"]:
														snake_case_									=				super().outputs
									else:
														snake_case_									=				super(lowercase_       ,				self					).outputs
														if self.use_past:
																			snake_case_			,snake_case_									=				self.num_layers
																			for i in range(lowercase_					):
																								snake_case_									=				{0: '''batch''', 2: '''past_sequence + sequence'''}
																								snake_case_									=				{0: '''batch''', 2: '''past_sequence + sequence'''}
									return common_outputs


				def   A_ (		self    :					Dict       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									snake_case_									=				self._generate_dummy_inputs_for_encoder_and_decoder(
									    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)

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

									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(lowercase_       ,				lowercase_					)]       ,				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(lowercase_       ,				lowercase_					)
														snake_case_									=				max(lowercase_       ,				lowercase_					) - min_num_layers
														snake_case_									=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									snake_case_									=				self._generate_dummy_inputs_for_encoder_and_decoder(
									    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)

									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(lowercase_       ,				lowercase_       ,				dtype=lowercase_					)]       ,				dim=1					)
														snake_case_									=				[
														    (torch.zeros(lowercase_					), torch.zeros(lowercase_					)) for _ in range(lowercase_					)
														]
									return common_inputs


				def   A_ (		self    :					List[str]       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									snake_case_									=				compute_effective_axis_dimension(
									    lowercase_       ,				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(lowercase_					)
									snake_case_									=				compute_effective_axis_dimension(
									    lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=lowercase_					)

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


				def   A_ (		self    :					Any       ,				lowercase_    :					PreTrainedTokenizer       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				):
									if self.task in ["default", "seq2seq-lm"]:
														snake_case_									=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    lowercase_       ,				batch_size=lowercase_       ,				seq_length=lowercase_       ,				is_pair=lowercase_       ,				framework=lowercase_					)

									else:
														snake_case_									=				self._generate_dummy_inputs_for_causal_lm(
														    lowercase_       ,				batch_size=lowercase_       ,				seq_length=lowercase_       ,				is_pair=lowercase_       ,				framework=lowercase_					)

									return common_inputs


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[str]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int       ,				lowercase_    :					List[str]					):
									if self.task in ["default", "seq2seq-lm"]:
														snake_case_									=				super()._flatten_past_key_values_(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)
									else:
														snake_case_									=				super(lowercase_       ,				self					)._flatten_past_key_values_(
														    lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)


				@property
				def   A_ (		self    :					List[str]					):
									return 1e-4

