





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
				import torch


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class 	A_       (unittest.TestCase			):
	UpperCAmelCase__					=			ViTImageProcessor if is_vision_available() else None
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				(3, 3_2, 1_2_8)
				UpperCAmelCase   		=				tempfile.mkdtemp()

				# fmt: off
				UpperCAmelCase   		=				['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
				# fmt: on
				UpperCAmelCase   		=				dict(zip(_A							,					range(len(_A		)		)		)		)

				UpperCAmelCase   		=				os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES['''vocab_file''']		)
				with open(self.vocab_file							,					'''w'''							,					encoding='''utf-8'''		) as fp:
							fp.write(json.dumps(_A		) + '''\n'''		)

				UpperCAmelCase   		=				{
				    '''do_normalize''': False,
				    '''do_resize''': True,
				    '''image_processor_type''': '''ViTImageProcessor''',
				    '''resample''': 3,
				    '''size''': {'''height''': 3_2, '''width''': 1_2_8},
				}
				UpperCAmelCase   		=				os.path.join(self.tmpdirname							,					_A		)
				with open(self.image_processor_file							,					'''w'''							,					encoding='''utf-8'''		) as fp:
							json.dump(_A							,					_A		)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				return MgpstrTokenizer.from_pretrained(self.tmpdirname							,					**_A		)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				return ViTImageProcessor.from_pretrained(self.tmpdirname							,					**_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				shutil.rmtree(self.tmpdirname		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				np.random.randint(2_5_5							,					size=(3, 3_0, 4_0_0)							,					dtype=np.uinta		)

				UpperCAmelCase   		=				Image.fromarray(np.moveaxis(_A							,					0							,					-1		)		)

				return image_input
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_tokenizer()
				UpperCAmelCase   		=				self.get_image_processor()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)
				processor.save_pretrained(self.tmpdirname		)
				UpperCAmelCase   		=				MgpstrProcessor.from_pretrained(self.tmpdirname							,					use_fast=_A		)

				self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer.get_vocab()		)
				self.assertIsInstance(processor.char_tokenizer							,					_A		)

				self.assertEqual(processor.image_processor.to_json_string()							,					image_processor.to_json_string()		)
				self.assertIsInstance(processor.image_processor							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_tokenizer()
				UpperCAmelCase   		=				self.get_image_processor()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)
				processor.save_pretrained(self.tmpdirname		)

				UpperCAmelCase   		=				self.get_tokenizer(bos_token='''(BOS)'''							,					eos_token='''(EOS)'''		)
				UpperCAmelCase   		=				self.get_image_processor(do_normalize=_A							,					padding_value=1.0		)

				UpperCAmelCase   		=				MgpstrProcessor.from_pretrained(
				    self.tmpdirname							,					bos_token='''(BOS)'''							,					eos_token='''(EOS)'''							,					do_normalize=_A							,					padding_value=1.0		)

				self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.char_tokenizer							,					_A		)

				self.assertEqual(processor.image_processor.to_json_string()							,					image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				image_processor(_A							,					return_tensors='''np'''		)
				UpperCAmelCase   		=				processor(images=_A							,					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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''test'''

				UpperCAmelCase   		=				processor(text=_A		)

				UpperCAmelCase   		=				tokenizer(_A		)
				for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''test'''
				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				processor(text=_A							,					images=_A		)

				self.assertListEqual(list(inputs.keys()		)							,					['''pixel_values''', '''labels''']		)

				# test if it raises when no input is passed
				with pytest.raises(_A		):
							processor()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

				UpperCAmelCase   		=				processor.char_decode(_A		)
				UpperCAmelCase   		=				tokenizer.batch_decode(_A		)
				UpperCAmelCase   		=				[seq.replace(''' '''							,					''''''		) for seq in decoded_tok]

				self.assertListEqual(_A							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				processor(text=_A							,					images=_A		)

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







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				MgpstrProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				torch.randn(1							,					2_7							,					3_8		)
				UpperCAmelCase   		=				torch.randn(1							,					2_7							,					5_0_2_5_7		)
				UpperCAmelCase   		=				torch.randn(1							,					2_7							,					3_0_5_2_2		)

				UpperCAmelCase   		=				processor.batch_decode([char_input, bpe_input, wp_input]		)

				self.assertListEqual(list(results.keys()		)							,					['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']		)










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    str       =   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''longformer'''
	def __init__(    self							,					_A = 5_1_2							,					_A = 2							,					_A = 1							,					_A = 0							,					_A = 2							,					_A = 3_0_5_2_2							,					_A = 7_6_8							,					_A = 1_2							,					_A = 1_2							,					_A = 3_0_7_2							,					_A = "gelu"							,					_A = 0.1							,					_A = 0.1							,					_A = 5_1_2							,					_A = 2							,					_A = 0.02							,					_A = 1E-12							,					_A = False							,					**_A							,					):






				'''simple docstring'''







				super().__init__(pad_token_id=_A							,					**_A		)

				UpperCAmelCase   		=				attention_window
				UpperCAmelCase   		=				sep_token_id
				UpperCAmelCase   		=				bos_token_id
				UpperCAmelCase   		=				eos_token_id
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				onnx_export

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A = "default"							,					_A = None		):






				'''simple docstring'''







				super().__init__(_A							,					_A							,					_A		)
				UpperCAmelCase   		=				True
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				        ('''global_attention_mask''', dynamic_axis),
				    ]		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				super().outputs
				if self.task == "default":
							UpperCAmelCase   		=				{0: '''batch'''}
				return outputs
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return 1E-4
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return max(super().default_onnx_opset							,					1_4		)







	def      _lowercase				(    self							,					_A							,					_A = -1							,					_A = -1							,					_A = False							,					_A = None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				super().generate_dummy_inputs(
				    preprocessor=_A							,					batch_size=_A							,					seq_length=_A							,					is_pair=_A							,					framework=_A		)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				UpperCAmelCase   		=				torch.zeros_like(inputs['''input_ids''']		)
				# make every second token global
				UpperCAmelCase   		=				1

				return inputs



