



"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :Tuple						=			tempfile.mkdtemp()

											snake_case_ :Optional[Any]						=			[
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """want""",
											    """##want""",
											    """##ed""",
											    """wa""",
											    """un""",
											    """runn""",
											    """##ing""",
											    """,""",
											    """low""",
											    """lowest""",
											]
											snake_case_ :int						=			os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]						)
											with open(self.vocab_file						,     """w"""						,     encoding="""utf-8"""						) as vocab_writer:
																	vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

											snake_case_ :List[str]						=			{
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
											    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
											}
											snake_case_ :List[str]						=			os.path.join(self.tmpdirname						,     snake_case						)
											with open(self.image_processor_file						,     """w"""						,     encoding="""utf-8"""						) as fp:
																	json.dump(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						,     **snake_case:							Any						)   ->	List[Any]:
											return BertTokenizer.from_pretrained(self.tmpdirname						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     **snake_case:							List[str]						)   ->	Union[str, Any]:
											return BertTokenizerFast.from_pretrained(self.tmpdirname						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     **snake_case:							Dict						)   ->	Any:
											return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Optional[Any]:
											shutil.rmtree(self.tmpdirname						)






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											snake_case_ :Tuple						=			[np.random.randint(255						,     size=(3, 30, 400)						,     dtype=np.uinta						)]
											snake_case_ :Dict						=			[Image.fromarray(np.moveaxis(snake_case						,     0						,     -1						)						) for x in image_inputs]
											return image_inputs






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Any:
											snake_case_ :Optional[Any]						=			self.get_tokenizer()
											snake_case_ :Tuple						=			self.get_rust_tokenizer()
											snake_case_ :Optional[int]						=			self.get_image_processor()

											snake_case_ :List[Any]						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)
											processor_slow.save_pretrained(self.tmpdirname						)
											snake_case_ :Union[str, Any]						=			AlignProcessor.from_pretrained(self.tmpdirname						,     use_fast=snake_case						)

											snake_case_ :int						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)
											processor_fast.save_pretrained(self.tmpdirname						)
											snake_case_ :int						=			AlignProcessor.from_pretrained(self.tmpdirname						)

											self.assertEqual(processor_slow.tokenizer.get_vocab()						,     tokenizer_slow.get_vocab()						)
											self.assertEqual(processor_fast.tokenizer.get_vocab()						,     tokenizer_fast.get_vocab()						)
											self.assertEqual(tokenizer_slow.get_vocab()						,     tokenizer_fast.get_vocab()						)
											self.assertIsInstance(processor_slow.tokenizer						,     snake_case						)
											self.assertIsInstance(processor_fast.tokenizer						,     snake_case						)

											self.assertEqual(processor_slow.image_processor.to_json_string()						,     image_processor.to_json_string()						)
											self.assertEqual(processor_fast.image_processor.to_json_string()						,     image_processor.to_json_string()						)
											self.assertIsInstance(processor_slow.image_processor						,     snake_case						)
											self.assertIsInstance(processor_fast.image_processor						,     snake_case						)






					def    lowerCAmelCase_				(       self:							str						)   ->	int:
											snake_case_ :List[str]						=			AlignProcessor(tokenizer=self.get_tokenizer()						,     image_processor=self.get_image_processor()						)
											processor.save_pretrained(self.tmpdirname						)

											snake_case_ :List[str]						=			self.get_tokenizer(bos_token="""(BOS)"""						,     eos_token="""(EOS)"""						)
											snake_case_ :Optional[Any]						=			self.get_image_processor(do_normalize=snake_case						,     padding_value=1.0						)

											snake_case_ :int						=			AlignProcessor.from_pretrained(
											    self.tmpdirname						,     bos_token="""(BOS)"""						,     eos_token="""(EOS)"""						,     do_normalize=snake_case						,     padding_value=1.0						)

											self.assertEqual(processor.tokenizer.get_vocab()						,     tokenizer_add_kwargs.get_vocab()						)
											self.assertIsInstance(processor.tokenizer						,     snake_case						)

											self.assertEqual(processor.image_processor.to_json_string()						,     image_processor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.image_processor						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	str:
											snake_case_ :Tuple						=			self.get_image_processor()
											snake_case_ :List[str]						=			self.get_tokenizer()

											snake_case_ :int						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)

											snake_case_ :Optional[Any]						=			self.prepare_image_inputs()

											snake_case_ :Optional[int]						=			image_processor(snake_case						,     return_tensors="""np"""						)
											snake_case_ :Optional[Any]						=			processor(images=snake_case						,     return_tensors="""np"""						)

											for key in input_image_proc.keys():
																	self.assertAlmostEqual(input_image_proc[key].sum()						,     input_processor[key].sum()						,     delta=1E-2						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	List[Any]:
											snake_case_ :List[str]						=			self.get_image_processor()
											snake_case_ :List[Any]						=			self.get_tokenizer()

											snake_case_ :Dict						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)

											snake_case_ :Optional[Any]						=			"""lower newer"""

											snake_case_ :int						=			processor(text=snake_case						)

											snake_case_ :Optional[Any]						=			tokenizer(snake_case						,     padding="""max_length"""						,     max_length=64						)

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]						,     encoded_processor[key]						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Union[str, Any]:
											snake_case_ :Any						=			self.get_image_processor()
											snake_case_ :Optional[int]						=			self.get_tokenizer()

											snake_case_ :Union[str, Any]						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)

											snake_case_ :Dict						=			"""lower newer"""
											snake_case_ :Dict						=			self.prepare_image_inputs()

											snake_case_ :Dict						=			processor(text=snake_case						,     images=snake_case						)

											self.assertListEqual(list(inputs.keys()						)						,     ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]						)

											# test if it raises when no input is passed
											with pytest.raises(snake_case						):
																	processor()






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Optional[Any]:
											snake_case_ :Optional[Any]						=			self.get_image_processor()
											snake_case_ :Optional[Any]						=			self.get_tokenizer()

											snake_case_ :str						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)

											snake_case_ :Optional[int]						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_ :int						=			processor.batch_decode(snake_case						)
											snake_case_ :List[str]						=			tokenizer.batch_decode(snake_case						)

											self.assertListEqual(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	List[str]:
											snake_case_ :List[Any]						=			self.get_image_processor()
											snake_case_ :str						=			self.get_tokenizer()

											snake_case_ :Optional[Any]						=			AlignProcessor(tokenizer=snake_case						,     image_processor=snake_case						)

											snake_case_ :List[Any]						=			"""lower newer"""
											snake_case_ :Dict						=			self.prepare_image_inputs()

											snake_case_ :int						=			processor(text=snake_case						,     images=snake_case						)

											self.assertListEqual(list(inputs.keys()						)						,     processor.model_input_names						)






'''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

