
import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class      lowerCAmelCase__  (			tf.keras.layers.Layer    ):






		def __init__(	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							List							,    SCREAMING_SNAKE_CASE__	:							bool							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							str = "longest"							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    SCREAMING_SNAKE_CASE__	:							int = 5_12							,    SCREAMING_SNAKE_CASE__	:							int = None							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    **SCREAMING_SNAKE_CASE__	:							int							,    )  ->							Dict:
						super().__init__()
						if use_fast_bert_tokenizer:
										__lowerCamelCase					      =			FastBertTokenizer(
										    SCREAMING_SNAKE_CASE__							,    token_out_type=tf.intaa							,    lower_case_nfd_strip_accents=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
						else:
										__lowerCamelCase					      =			tf.lookup.StaticVocabularyTable(
										    tf.lookup.KeyValueTensorInitializer(
										        keys=SCREAMING_SNAKE_CASE__							,    key_dtype=tf.string							,    values=tf.range(tf.size(SCREAMING_SNAKE_CASE__							,    out_type=tf.intaa						)							,    dtype=tf.intaa						)							,    value_dtype=tf.intaa							,    )							,    num_oov_buckets=1							,    )
										__lowerCamelCase					      =			BertTokenizerLayer(
										    SCREAMING_SNAKE_CASE__							,    token_out_type=tf.intaa							,    lower_case=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			vocab_list
						__lowerCamelCase					      =			do_lower_case
						__lowerCamelCase					      =			cls_token_id or vocab_list.index('''[CLS]'''						)
						__lowerCamelCase					      =			sep_token_id or vocab_list.index('''[SEP]'''						)
						__lowerCamelCase					      =			pad_token_id or vocab_list.index('''[PAD]'''						)
						__lowerCamelCase					      =			ShrinkLongestTrimmer(max_length - 3							,    axis=1						)  # Allow room for special tokens
						__lowerCamelCase					      =			max_length
						__lowerCamelCase					      =			padding
						__lowerCamelCase					      =			truncation
						__lowerCamelCase					      =			pad_to_multiple_of
						__lowerCamelCase					      =			return_token_type_ids
						__lowerCamelCase					      =			return_attention_mask






		@classmethod
		def 				__A (	cls	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							"PreTrainedTokenizerBase"							,    **SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							int:  # noqa: F821
						__lowerCamelCase					      =			kwargs.pop('''do_lower_case'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.do_lower_case if do_lower_case is None else do_lower_case
						__lowerCamelCase					      =			kwargs.pop('''cls_token_id'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.cls_token_id if cls_token_id is None else cls_token_id
						__lowerCamelCase					      =			kwargs.pop('''sep_token_id'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.sep_token_id if sep_token_id is None else sep_token_id
						__lowerCamelCase					      =			kwargs.pop('''pad_token_id'''							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.pad_token_id if pad_token_id is None else pad_token_id

						__lowerCamelCase					      =			tokenizer.get_vocab()
						__lowerCamelCase					      =			sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]							,    key=lambda SCREAMING_SNAKE_CASE__						: x[1]						)
						__lowerCamelCase					      =			[entry[0] for entry in vocab]
						return cls(
						    vocab_list=SCREAMING_SNAKE_CASE__							,    do_lower_case=SCREAMING_SNAKE_CASE__							,    cls_token_id=SCREAMING_SNAKE_CASE__							,    sep_token_id=SCREAMING_SNAKE_CASE__							,    pad_token_id=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__							,    )






		@classmethod
		def 				__A (	cls	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Union[str, os.PathLike]							,    *SCREAMING_SNAKE_CASE__	:							List[Any]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							str:
						try:
										__lowerCamelCase					      =			BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							,    *SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
						except:  # noqa: E722
										from .tokenization_bert_fast import BertTokenizerFast

										__lowerCamelCase					      =			BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__							,    *SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
						return cls.from_tokenizer(SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							int						)  ->							Optional[Any]:
						if self.do_lower_case:
										__lowerCamelCase					      =			case_fold_utfa(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			self.tf_tokenizer.tokenize(SCREAMING_SNAKE_CASE__						)
						return tokens.merge_dims(1							,    -1						)






		def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=None							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=None							,    SCREAMING_SNAKE_CASE__	:							List[Any]=None							,    SCREAMING_SNAKE_CASE__	:							Tuple=None							,    SCREAMING_SNAKE_CASE__	:							int=None							,    SCREAMING_SNAKE_CASE__	:							int=None							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=None							,    )  ->							List[str]:
						if padding is None:
										__lowerCamelCase					      =			self.padding
						if padding not in ("longest", "max_length"):
										raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''						)
						if max_length is not None and text_pair is not None:
										# Because we have to instantiate a Trimmer to do it properly
										raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''						)
						if max_length is None:
										__lowerCamelCase					      =			self.max_length
						if truncation is None:
										__lowerCamelCase					      =			self.truncation
						if pad_to_multiple_of is None:
										__lowerCamelCase					      =			self.pad_to_multiple_of
						if return_token_type_ids is None:
										__lowerCamelCase					      =			self.return_token_type_ids
						if return_attention_mask is None:
										__lowerCamelCase					      =			self.return_attention_mask
						if not isinstance(SCREAMING_SNAKE_CASE__							,    tf.Tensor						):
										__lowerCamelCase					      =			tf.convert_to_tensor(SCREAMING_SNAKE_CASE__						)
						if text_pair is not None and not isinstance(SCREAMING_SNAKE_CASE__							,    tf.Tensor						):
										__lowerCamelCase					      =			tf.convert_to_tensor(SCREAMING_SNAKE_CASE__						)
						if text_pair is not None:
										if text.shape.rank > 1:
														raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''						)
										if text_pair.shape.rank > 1:
														raise ValueError('''text_pair should not be multidimensional!'''						)
						if text.shape.rank == 2:
										__lowerCamelCase      ,					__lowerCamelCase					      =			text[:, 0], text[:, 1]
						__lowerCamelCase					      =			self.unpaired_tokenize(SCREAMING_SNAKE_CASE__						)
						if text_pair is None:  # Unpaired text
										if truncation:
														__lowerCamelCase					      =			text[:, : max_length - 2]  # Allow room for special tokens
										__lowerCamelCase      ,					__lowerCamelCase					      =			combine_segments(
										    (text,)							,    start_of_sequence_id=self.cls_token_id							,    end_of_segment_id=self.sep_token_id						)
						else:  # Paired text
										__lowerCamelCase					      =			self.unpaired_tokenize(SCREAMING_SNAKE_CASE__						)
										if truncation:
														__lowerCamelCase      ,					__lowerCamelCase					      =			self.paired_trimmer.trim([text, text_pair]						)
										__lowerCamelCase      ,					__lowerCamelCase					      =			combine_segments(
										    (text, text_pair)							,    start_of_sequence_id=self.cls_token_id							,    end_of_segment_id=self.sep_token_id						)
						if padding == "longest":
										__lowerCamelCase					      =			input_ids.bounding_shape(axis=1						)
										if pad_to_multiple_of is not None:
														# No ceiling division in tensorflow, so we negate floordiv instead
														__lowerCamelCase					      =			pad_to_multiple_of * (-tf.math.floordiv(-pad_length							,    SCREAMING_SNAKE_CASE__						))
						else:
										__lowerCamelCase					      =			max_length

						__lowerCamelCase      ,					__lowerCamelCase					      =			pad_model_inputs(SCREAMING_SNAKE_CASE__							,    max_seq_length=SCREAMING_SNAKE_CASE__							,    pad_value=self.pad_token_id						)
						__lowerCamelCase					      =			{'''input_ids''': input_ids}
						if return_attention_mask:
										__lowerCamelCase					      =			attention_mask
						if return_token_type_ids:
										__lowerCamelCase      ,					__lowerCamelCase					      =			pad_model_inputs(
										    SCREAMING_SNAKE_CASE__							,    max_seq_length=SCREAMING_SNAKE_CASE__							,    pad_value=self.pad_token_id						)
										__lowerCamelCase					      =			token_type_ids
						return output







		def 				__A (	self	:							List[str]						)  ->							Optional[Any]:
						return {
						    "vocab_list": self.vocab_list,
						    "do_lower_case": self.do_lower_case,
						    "cls_token_id": self.cls_token_id,
						    "sep_token_id": self.sep_token_id,
						    "pad_token_id": self.pad_token_id,
						}





import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
					from PIL import Image

					from transformers import MgpstrProcessor

class      lowerCAmelCase__  :






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]=False							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=13							,    SCREAMING_SNAKE_CASE__	:							Any=(32, 1_28)							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=4							,    SCREAMING_SNAKE_CASE__	:							List[Any]=3							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=27							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=38							,    SCREAMING_SNAKE_CASE__	:							Dict=99							,    SCREAMING_SNAKE_CASE__	:							int=99							,    SCREAMING_SNAKE_CASE__	:							Tuple=32							,    SCREAMING_SNAKE_CASE__	:							Any=5							,    SCREAMING_SNAKE_CASE__	:							str=4							,    SCREAMING_SNAKE_CASE__	:							List[str]=4.0							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=2_57							,    SCREAMING_SNAKE_CASE__	:							Tuple=None							,    )  ->							List[Any]:
						__lowerCamelCase					      =			parent
						__lowerCamelCase					      =			is_training
						__lowerCamelCase					      =			batch_size
						__lowerCamelCase					      =			image_size
						__lowerCamelCase					      =			patch_size
						__lowerCamelCase					      =			num_channels
						__lowerCamelCase					      =			max_token_length
						__lowerCamelCase					      =			num_character_labels
						__lowerCamelCase					      =			num_bpe_labels
						__lowerCamelCase					      =			num_wordpiece_labels
						__lowerCamelCase					      =			hidden_size
						__lowerCamelCase					      =			num_hidden_layers
						__lowerCamelCase					      =			num_attention_heads
						__lowerCamelCase					      =			mlp_ratio
						__lowerCamelCase					      =			patch_embeds_hidden_size
						__lowerCamelCase					      =			output_hidden_states






		def 				__A (	self	:							Any						)  ->							Optional[int]:
						__lowerCamelCase					      =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
						__lowerCamelCase					      =			self.get_config()
						return config, pixel_values






		def 				__A (	self	:							int						)  ->							Union[str, Any]:
						return MgpstrConfig(
						    image_size=self.image_size							,    patch_size=self.patch_size							,    num_channels=self.num_channels							,    max_token_length=self.max_token_length							,    num_character_labels=self.num_character_labels							,    num_bpe_labels=self.num_bpe_labels							,    num_wordpiece_labels=self.num_wordpiece_labels							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    mlp_ratio=self.mlp_ratio							,    output_hidden_states=self.output_hidden_states							,    )






		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							List[Any]:
						__lowerCamelCase					      =			MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						with torch.no_grad():
										__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(
						    generated_ids[0][0].shape							,    (self.batch_size, self.max_token_length, self.num_character_labels)						)







		def 				__A (	self	:							Dict						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			self.prepare_config_and_inputs()
						__lowerCamelCase      ,					__lowerCamelCase					      =			config_and_inputs
						__lowerCamelCase					      =			{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_torch
class      lowerCAmelCase__  (			__lowercase						,			__lowercase						,			unittest.TestCase    ):
		a__ :		Dict   						=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
		a__ :		Optional[int]   						=	{"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
		a__ :		Any   						=	False

		a__ :		List[Any]   						=	False
		a__ :		Any   						=	False
		a__ :		int   						=	False
		a__ :		Tuple   						=	False






		def 				__A (	self	:							Optional[Any]						)  ->							str:
						__lowerCamelCase					      =			MgpstrModelTester(self						)
						__lowerCamelCase					      =			ConfigTester(self							,    config_class=SCREAMING_SNAKE_CASE__							,    has_text_modality=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[Any]						)  ->							Tuple:
						self.config_tester.run_common_tests()






		def 				__A (	self	:							List[str]						)  ->							Any:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)






		@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''						)
		def 				__A (	self	:							Optional[int]						)  ->							Dict:
						pass






		def 				__A (	self	:							Tuple						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										self.assertIsInstance(model.get_input_embeddings()							,    (nn.Module)						)
										__lowerCamelCase					      =			model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__							,    nn.Linear						)						)






		def 				__A (	self	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

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

										__lowerCamelCase					      =			['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,    SCREAMING_SNAKE_CASE__						)






		@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''						)
		def 				__A (	self	:							Optional[Any]						)  ->							str:
						pass






		def 				__A (	self	:							str						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										if not model_class.supports_gradient_checkpointing:
														continue

										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										self.assertTrue(model.is_gradient_checkpointing						)






		def 				__A (	self	:							List[str]						)  ->							List[str]:
						def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						):
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()

										with torch.no_grad():
														__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

										__lowerCamelCase					      =			outputs.hidden_states

										__lowerCamelCase					      =			getattr(
										    self.model_tester							,    '''expected_num_hidden_layers'''							,    self.model_tester.num_hidden_layers + 1						)
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]						)							,    [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]							,    )

						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			True
										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

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

										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Tuple						)  ->							int:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			_config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(config=SCREAMING_SNAKE_CASE__						)
										for name, param in model.named_parameters():
														if isinstance(SCREAMING_SNAKE_CASE__							,    (nn.Linear, nn.Convad, nn.LayerNorm)						):
																		if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,    [0.0, 1.0]							,    msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,    )







		@unittest.skip(reason='''Retain_grad is tested in individual model tests'''						)
		def 				__A (	self	:							int						)  ->							Union[str, Any]:
						pass





def 				__magic_name__				(     )    ->     Optional[Any]:
				__lowerCamelCase					      =			'''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
				__lowerCamelCase					      =			Image.open(requests.get(__lowerCAmelCase	,						stream=__lowerCAmelCase							).raw							).convert('''RGB'''							)
				return im







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






		@slow
		def 				__A (	self	:							List[str]						)  ->							Optional[int]:
						__lowerCamelCase					      =			'''alibaba-damo/mgp-str-base'''
						__lowerCamelCase					      =			MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			prepare_img()
						__lowerCamelCase					      =			processor(images=SCREAMING_SNAKE_CASE__							,    return_tensors='''pt'''						).pixel_values.to(SCREAMING_SNAKE_CASE__						)

						# forward pass
						with torch.no_grad():
										__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

						# verify the logits
						self.assertEqual(outputs.logits[0].shape							,    torch.Size((1, 27, 38)						)						)

						__lowerCamelCase					      =			processor.batch_decode(outputs.logits						)
						__lowerCamelCase					      =			'''ticket'''

						self.assertEqual(out_strs['''generated_text'''][0]							,    SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			torch.tensor(
						    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]							,    device=SCREAMING_SNAKE_CASE__							,    )

						self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]							,    SCREAMING_SNAKE_CASE__							,    atol=1e-4						)						)



