





'''simple docstring'''

import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.bert.modeling_flax_bert import (
			    FlaxBertForMaskedLM,
			    FlaxBertForMultipleChoice,
			    FlaxBertForNextSentencePrediction,
			    FlaxBertForPreTraining,
			    FlaxBertForQuestionAnswering,
			    FlaxBertForSequenceClassification,
			    FlaxBertForTokenClassification,
			    FlaxBertModel,
			)

class       UpperCAmelCase (						unittest.TestCase  ):




		def __init__(self      :	List[Any]							,			A__      :	int							,			A__      :	Tuple=1_3							,			A__      :	List[str]=7							,			A__      :	List[str]=True							,			A__      :	List[Any]=True							,			A__      :	List[str]=True							,			A__      :	List[str]=True							,			A__      :	Any=9_9							,			A__      :	int=3_2							,			A__      :	Dict=5							,			A__      :	Any=4							,			A__      :	Union[str, Any]=3_7							,			A__      :	str="gelu"							,			A__      :	str=0.1							,			A__      :	Any=0.1							,			A__      :	Tuple=5_1_2							,			A__      :	Union[str, Any]=1_6							,			A__      :	Union[str, Any]=2							,			A__      :	Any=0.0_2							,			A__      :	Optional[int]=4							,			)					-> Optional[int]:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = seq_length
						lowercase         = is_training
						lowercase         = use_attention_mask
						lowercase         = use_token_type_ids
						lowercase         = use_labels
						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = type_sequence_label_size
						lowercase         = initializer_range
						lowercase         = num_choices




		def 				UpperCAmelCase__   (self      :	int				)					-> Union[str, Any]:
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size				)

						lowercase         = None
						if self.use_attention_mask:
										lowercase         = random_attention_mask([self.batch_size, self.seq_length]				)

						lowercase         = None
						if self.use_token_type_ids:
										lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			self.type_vocab_size				)

						lowercase         = BertConfig(
						    vocab_size=self.vocab_size							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			type_vocab_size=self.type_vocab_size							,			is_decoder=A__							,			initializer_range=self.initializer_range							,			)

						return config, input_ids, token_type_ids, attention_mask




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[str]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = config_and_inputs

						lowercase         = True
						lowercase         = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2				)

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)




@require_flax
class       UpperCAmelCase (						_lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	Dict 			=		True

		UpperCAmelCase					:	Tuple 			=		(
		    (
		        FlaxBertModel,
		        FlaxBertForPreTraining,
		        FlaxBertForMaskedLM,
		        FlaxBertForMultipleChoice,
		        FlaxBertForQuestionAnswering,
		        FlaxBertForNextSentencePrediction,
		        FlaxBertForSequenceClassification,
		        FlaxBertForTokenClassification,
		        FlaxBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)




		def 				UpperCAmelCase__   (self      :	str				)					-> Optional[Any]:
						lowercase         = FlaxBertModelTester(self				)





		@slow
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[Any]:
						# Only check this for base model, not necessary for all model classes.
						# This will also help speed-up tests.
						lowercase         = FlaxBertModel.from_pretrained("bert-base-cased"				)
						lowercase         = model(np.ones((1, 1)				)				)
						self.assertIsNotNone(A__				)











'''simple docstring'''

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       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	Optional[int] 			=		ViTImageProcessor if is_vision_available() else None




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = (3, 3_2, 1_2_8)
						lowercase         = tempfile.mkdtemp()

						# fmt: off
						lowercase         = ["[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
						lowercase         = dict(zip(A__							,			range(len(A__				)				)				)				)

						lowercase         = 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"				)

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




		def 				UpperCAmelCase__   (self      :	Dict							,			**A__      :	List[Any]				)					-> Union[str, Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Tuple				)					-> Tuple:
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> int:
						shutil.rmtree(self.tmpdirname				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta				)

						lowercase         = Image.fromarray(np.moveaxis(A__							,			0							,			-1				)				)

						return image_input




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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)

						lowercase         = self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"				)
						lowercase         = self.get_image_processor(do_normalize=A__							,			padding_value=1.0				)

						lowercase         = 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 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = self.prepare_image_inputs()

						lowercase         = image_processor(A__							,			return_tensors="np"				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"

						lowercase         = processor(text=A__				)

						lowercase         = tokenizer(A__				)
						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"
						lowercase         = self.prepare_image_inputs()

						lowercase         = 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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

						lowercase         = processor.char_decode(A__				)
						lowercase         = tokenizer.batch_decode(A__				)
						lowercase         = [seq.replace(" "							,			""				) for seq in decoded_tok]

						self.assertListEqual(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = None
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

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





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

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

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




