




'''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


'''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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[Any]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  List[str]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Any							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

