



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 	a__       :


					"""simple docstring"""



					def __init__(		self     :str      ,  lowercase__     :List[str]      ,  lowercase__     :str=False      ,  lowercase__     :Union[str, Any]=13      ,  lowercase__     :Union[str, Any]=(32, 128)      ,  lowercase__     :Any=4      ,  lowercase__     :Tuple=3      ,  lowercase__     :List[str]=27      ,  lowercase__     :Optional[Any]=38      ,  lowercase__     :Optional[Any]=99      ,  lowercase__     :Union[str, Any]=99      ,  lowercase__     :Dict=32      ,  lowercase__     :Optional[Any]=5      ,  lowercase__     :Optional[int]=4      ,  lowercase__     :Any=4.0      ,  lowercase__     :List[str]=257      ,  lowercase__     :Optional[Any]=None      ,  ):
										lowercase						   =       parent
										lowercase						   =       is_training
										lowercase						   =       batch_size
										lowercase						   =       image_size
										lowercase						   =       patch_size
										lowercase						   =       num_channels
										lowercase						   =       max_token_length
										lowercase						   =       num_character_labels
										lowercase						   =       num_bpe_labels
										lowercase						   =       num_wordpiece_labels
										lowercase						   =       hidden_size
										lowercase						   =       num_hidden_layers
										lowercase						   =       num_attention_heads
										lowercase						   =       mlp_ratio
										lowercase						   =       patch_embeds_hidden_size
										lowercase						   =       output_hidden_states

					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]    )
										lowercase						   =       self.get_config()
										return config, pixel_values

					def 				__UpperCAmelCase		(		self     :List[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 				__UpperCAmelCase		(		self     :Union[str, Any]      ,  lowercase__     :Optional[Any]      ,  lowercase__     :Union[str, Any]    ):
										lowercase						   =       MgpstrForSceneTextRecognition(_lowerCamelCase    )
										model.to(_lowerCamelCase    )
										model.eval()
										with torch.no_grad():
															lowercase						   =       model(_lowerCamelCase    )
										self.parent.assertEqual(
										    generated_ids[0][0].shape      ,  (self.batch_size, self.max_token_length, self.num_character_labels)    )



					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										lowercase						   =       self.prepare_config_and_inputs()
										lowercase						   =       config_and_inputs
										lowercase						   =       {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 	a__       (       a__  ,			a__  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				int					 =				(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
					A__  :				Any					 =				{'''feature-extraction''': MgpstrForSceneTextRecognition} if is_torch_available() else {}
					A__  :				str					 =				False

					A__  :				Optional[int]					 =				False
					A__  :				Union[str, Any]					 =				False
					A__  :				Dict					 =				False
					A__  :				Any					 =				False

					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       MgpstrModelTester(self    )
										lowercase						   =       ConfigTester(self      ,  config_class=_lowerCamelCase      ,  has_text_modality=_lowerCamelCase    )

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										self.config_tester.run_common_tests()

					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase    )

					@unittest.skip(reason='MgpstrModel does not use inputs_embeds'    )
					def 				__UpperCAmelCase		(		self     :Dict    ):
										pass

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

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

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

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

															lowercase						   =       ['''pixel_values''']
															self.assertListEqual(arg_names[:1]      ,  _lowerCamelCase    )

					@unittest.skip(reason='MgpstrModel does not support feedforward chunking'    )
					def 				__UpperCAmelCase		(		self     :int    ):
										pass

					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       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

															lowercase						   =       True
															lowercase						   =       model_class(_lowerCamelCase    )
															self.assertTrue(model.is_gradient_checkpointing    )

					def 				__UpperCAmelCase		(		self     :Dict    ):







										def check_hidden_states_output(lowercase__     :Tuple      ,  lowercase__     :Optional[int]      ,  lowercase__     :Any    ):
															lowercase						   =       model_class(_lowerCamelCase    )
															model.to(_lowerCamelCase    )
															model.eval()

															with torch.no_grad():
																				lowercase						   =       model(**self._prepare_for_class(_lowerCamelCase      ,  _lowerCamelCase    )    )

															lowercase						   =       outputs.hidden_states

															lowercase						   =       getattr(
															    self.model_tester      ,  'expected_num_hidden_layers'      ,  self.model_tester.num_hidden_layers + 1    )
															self.assertEqual(len(_lowerCamelCase    )      ,  _lowerCamelCase    )

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

										lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowercase						   =       True
															check_hidden_states_output(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase    )

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

															check_hidden_states_output(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase    )

					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

										lowercase						   =       _config_zero_init(_lowerCamelCase    )
										for model_class in self.all_model_classes:
															lowercase						   =       model_class(config=_lowerCamelCase    )
															for name, param in model.named_parameters():
																				if isinstance(_lowerCamelCase      ,  (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 				__UpperCAmelCase		(		self     :int    ):
										pass





def 		__snake_case						(    ):
					"""simple docstring"""






					lowercase						   =       '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
					lowercase						   =       Image.open(requests.get(lowerCamelCase__       ,			stream=lowerCamelCase__				).raw				).convert('RGB'				)
					return im




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


					"""simple docstring"""



					@slow
					def 				__UpperCAmelCase		(		self     :List[str]    ):
										lowercase						   =       '''alibaba-damo/mgp-str-base'''
										lowercase						   =       MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase    ).to(_lowerCamelCase    )
										lowercase						   =       MgpstrProcessor.from_pretrained(_lowerCamelCase    )

										lowercase						   =       prepare_img()
										lowercase						   =       processor(images=_lowerCamelCase      ,  return_tensors='pt'    ).pixel_values.to(_lowerCamelCase    )

										# forward pass
										with torch.no_grad():
															lowercase						   =       model(_lowerCamelCase    )

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

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

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

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

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












'''simple docstring'''




import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


snake_case__ 							=    {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
snake_case__ 							=    set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))




def     snake_case__     (			lowerCamelCase__					: List[str]      ,		lowerCamelCase__					: Tuple=0      ,		lowerCamelCase__					: Tuple=None      ) -> Optional[Any]:
					return [" ".join(x.split(''' '''      )[begin:end]      ) for x in answers if len(x.split(''' '''      )      ) > 1]




def     snake_case__     (			lowerCamelCase__					: Any      ,		lowerCamelCase__					: Any=False      ) -> List[Any]:
					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										A_			:  Union[str, Any]			=				(
										    given_answers + get_sub_answers(lowerCamelCase__      ,		begin=1      ) + get_sub_answers(lowerCamelCase__      ,		end=-1      )
										)
					A_			:  List[Any]			=				[]
					for answer in given_answers:
										A_			:  Dict			=				answer.replace('''_'''      ,		''' '''      ).lower()
										A_			:  str			=				''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias      )
										answers.append(''' '''.join(alias.split()      ).strip()      )
					return set(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: Optional[Any]      ,		lowerCamelCase__					: Dict      ,		lowerCamelCase__					: Union[str, Any]=1      ,		lowerCamelCase__					: Any=1_0_0      ) -> Any:
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )

					A_			:  List[Any]			=				best_end_idx[:, None] - best_start_idx[None, :]
					A_			:  Optional[Any]			=				jnp.logical_or(widths < 0      ,		widths > max_size      )
					A_			:  Optional[int]			=				(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
					A_			:  str			=				jnp.argmax(lowerCamelCase__      ).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]




def     snake_case__     (			lowerCamelCase__					: List[str]      ) -> Union[str, Any]:
					A_			:  str			=				sample['''question''']['''text''']
					A_			:  Tuple			=				sample['''document''']['''tokens''']['''token''']
					A_			:  List[Any]			=				sample['''document''']['''tokens''']['''is_html''']
					A_			:  Tuple			=				sample['''annotations''']['''long_answer''']
					A_			:  List[Any]			=				sample['''annotations''']['''short_answers''']

					A_			:  Optional[Any]			=				''' '''.join([context[i] for i in range(len(lowerCamelCase__      )      ) if not is_html[i]]      )

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					A_			:  List[Any]			=				[]
					for s in short_answers:
										short_targets.extend(s['''text''']      )
					A_			:  Tuple			=				list(set(lowerCamelCase__      )      )

					A_			:  Tuple			=				[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										A_			:  Dict			=				context[s['''start_token'''] : s['''end_token''']]
										A_			:  Tuple			=				is_html[s['''start_token'''] : s['''end_token''']]
										A_			:  Optional[Any]			=				''' '''.join([answer[i] for i in range(len(lowerCamelCase__      )      ) if not html[i]]      )
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase__      )

					A_			:  str			=				'''long_short''' if len(short_targets + long_targets      ) > 0 else '''null'''

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}




def     snake_case__     (			) -> List[Any]:
					A_			:  int			=				load_from_disk('''natural-questions-validation'''      )
					A_			:  Optional[int]			=				dataset.map(lowerCamelCase__      ).remove_columns(['''annotations''', '''document''', '''id''']      )
					print(lowerCamelCase__      )

					A_			:  Tuple			=				dataset.filter(lambda lowerCamelCase__      : (len(x['''question''']      ) + len(x['''context''']      )) < 4 * 4_0_9_6      )
					A_			:  Optional[Any]			=				short_validation_dataset.filter(lambda lowerCamelCase__      : x["category"] != "null"      )
					short_validation_dataset

					A_			:  Union[str, Any]			=				'''vasudevgupta/flax-bigbird-natural-questions'''
					A_			:  List[str]			=				FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase__      )
					A_			:  int			=				BigBirdTokenizerFast.from_pretrained(lowerCamelCase__      )

					@jax.jit
					def forward(*lowerCamelCase__					: Any      ,		**lowerCamelCase__					: Optional[Any]      ):
										A_			,A_			,A_			:  Tuple			=				model(*lowerCamelCase__      ,		**lowerCamelCase__      )
										return start_logits, end_logits, jnp.argmax(lowerCamelCase__      ,		axis=-1      )

					def evaluate(lowerCamelCase__					: List[str]      ):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										A_			:  List[Any]			=				tokenizer(
										    example['''question''']      ,		example['''context''']      ,		return_tensors='''np'''      ,		max_length=4_0_9_6      ,		padding='''max_length'''      ,		truncation=lowerCamelCase__      ,		)

										A_			,A_			,A_			:  Optional[Any]			=				forward(**lowerCamelCase__      )

										A_			:  Optional[Any]			=				CATEGORY_MAPPING[category.item()]

										A_			:  str			=				example['''long'''] + example['''short''']
										if example["category"] in ["yes", "no", "null"]:
															A_			:  str			=				[example['''category''']]
										A_			:  Optional[Any]			=				example['''category'''] != '''null'''
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															A_			:  int			=				[predicted_category]
															A_			:  Optional[int]			=				example['''output'''] == example['''targets''']
															A_			:  Dict			=				predicted_category != '''null'''
															return example

										A_			:  List[Any]			=				3_8 if predicted_category == '''short''' else 1_0_2_4
										A_			,A_			:  str			=				get_best_valid_start_end_idx(
										    start_scores[0]      ,		end_scores[0]      ,		top_k=8      ,		max_size=lowerCamelCase__      )

										A_			:  List[Any]			=				inputs['''input_ids'''][0].tolist()
										A_			:  Dict			=				[tokenizer.decode(input_ids[start_score : end_score + 1]      )]

										A_			:  Optional[int]			=				expand_to_aliases(example['''targets''']      ,		make_sub_answers=lowerCamelCase__      )
										A_			:  str			=				expand_to_aliases(example['''output''']      )

										# some preprocessing to both prediction and answer
										A_			:  Tuple			=				{''''''.join(a.split()      ) for a in answers}
										A_			:  Optional[int]			=				{''''''.join(p.split()      ) for p in predictions}
										A_			:  List[str]			=				{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

										# if there is a common element, it's a exact match
										A_			:  List[Any]			=				len(list(answers & predictions      )      ) > 0
										A_			:  Dict			=				predicted_category != '''null''' and len(lowerCamelCase__      ) > 0

										return example

					A_			:  Dict			=				short_validation_dataset.map(lowerCamelCase__      )

					A_			:  List[str]			=				len(lowerCamelCase__      )
					A_			:  Any			=				len(short_validation_dataset.filter(lambda lowerCamelCase__      : x["match"] == 1      )      )
					print('''EM score:'''      ,		(matched / total) * 1_0_0      ,		'''%'''      )


if __name__ == "__main__":
							main()



