




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			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=13			,	SCREAMING_SNAKE_CASE=(32, 128)			,	SCREAMING_SNAKE_CASE=4			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=27			,	SCREAMING_SNAKE_CASE=38			,	SCREAMING_SNAKE_CASE=99			,	SCREAMING_SNAKE_CASE=99			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=5			,	SCREAMING_SNAKE_CASE=4			,	SCREAMING_SNAKE_CASE=4.0			,	SCREAMING_SNAKE_CASE=257			,	SCREAMING_SNAKE_CASE=None			,	):



					"""simple docstring"""







					snake_case						:  List[Any]        =			parent
					snake_case						:  Any        =			is_training
					snake_case						:  Dict        =			batch_size
					snake_case						:  Any        =			image_size
					snake_case						:  List[str]        =			patch_size
					snake_case						:  Tuple        =			num_channels
					snake_case						:  Dict        =			max_token_length
					snake_case						:  List[str]        =			num_character_labels
					snake_case						:  str        =			num_bpe_labels
					snake_case						:  List[Any]        =			num_wordpiece_labels
					snake_case						:  str        =			hidden_size
					snake_case						:  Dict        =			num_hidden_layers
					snake_case						:  List[str]        =			num_attention_heads
					snake_case						:  str        =			mlp_ratio
					snake_case						:  str        =			patch_embeds_hidden_size
					snake_case						:  List[str]        =			output_hidden_states


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)
					snake_case						:  List[str]        =			self.get_config()
					return config, pixel_values


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					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 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  int        =			MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE							)
					model.to(SCREAMING_SNAKE_CASE							)
					model.eval()
					with torch.no_grad():
							snake_case						:  Tuple        =			model(SCREAMING_SNAKE_CASE							)
					self.parent.assertEqual(
					    generated_ids[0][0].shape			,	(self.batch_size, self.max_token_length, self.num_character_labels)							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			self.prepare_config_and_inputs()
					snake_case						:  str        =			config_and_inputs
					snake_case						:  int        =			{"pixel_values": pixel_values}
					return config, inputs_dict







@require_torch
class      lowerCamelCase__      (					lowerCamelCase_			,					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: Optional[Any]        =       (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
			a__	: List[str]        =       {"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
			a__	: List[Any]        =       False

			a__	: int        =       False
			a__	: str        =       False
			a__	: Dict        =       False
			a__	: List[Any]        =       False


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			MgpstrModelTester(self							)
					snake_case						:  List[Any]        =			ConfigTester(self			,	config_class=SCREAMING_SNAKE_CASE			,	has_text_modality=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case						:  List[Any]        =			model_class(SCREAMING_SNAKE_CASE							)
							self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module)							)
							snake_case						:  Union[str, Any]        =			model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE			,	nn.Linear							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.model_tester.prepare_config_and_inputs_for_common()

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

							snake_case						:  Tuple        =			["pixel_values"]
							self.assertListEqual(arg_names[:1]			,	SCREAMING_SNAKE_CASE							)


			@unittest.skip(reason="MgpstrModel does not support feedforward chunking"							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			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

							snake_case						:  Dict        =			True
							snake_case						:  Any        =			model_class(SCREAMING_SNAKE_CASE							)
							self.assertTrue(model.is_gradient_checkpointing							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					def check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
							snake_case						:  Any        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()

							with torch.no_grad():
									snake_case						:  Optional[int]        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)

							snake_case						:  int        =			outputs.hidden_states

							snake_case						:  Dict        =			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]			,	)

					snake_case						:  List[str]        =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case						:  Any        =			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"]
							snake_case						:  Dict        =			True

							check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			self.model_tester.prepare_config_and_inputs_for_common()

					snake_case						:  Optional[int]        =			_config_zero_init(SCREAMING_SNAKE_CASE							)
					for model_class in self.all_model_classes:
							snake_case						:  Dict        =			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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass







def 					UpperCamelCase__       (						):
		snake_case						:  str        =			"https://i.postimg.cc/ZKwLg2Gw/367-14.png"
		snake_case						:  Dict        =			Image.open(requests.get(lowercase__    ,       stream=lowercase__				).raw				).convert("RGB"				)
		return im
@require_vision
@require_torch
class      lowerCamelCase__      (					unittest.TestCase     ):


			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"alibaba-damo/mgp-str-base"
					snake_case						:  List[str]        =			MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE							).to(SCREAMING_SNAKE_CASE							)
					snake_case						:  Tuple        =			MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE							)

					snake_case						:  Optional[int]        =			prepare_img()
					snake_case						:  List[str]        =			processor(images=SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).pixel_values.to(SCREAMING_SNAKE_CASE							)

					# forward pass
					with torch.no_grad():
							snake_case						:  str        =			model(SCREAMING_SNAKE_CASE							)

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

					snake_case						:  int        =			processor.batch_decode(outputs.logits							)
					snake_case						:  List[Any]        =			"ticket"

					self.assertEqual(out_strs["generated_text"][0]			,	SCREAMING_SNAKE_CASE							)

					snake_case						:  Dict        =			torch.tensor(
					    [[[-39.53_97, -44.40_24, -36.18_44], [-61.47_09, -63.86_39, -58.34_54], [-74.02_25, -68.54_94, -71.21_64]]]			,	device=SCREAMING_SNAKE_CASE			,	)

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







"""simple docstring"""

import matplotlib.pyplot as plt
import numpy as np



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Any        =			len(lowercase__				)  # Number of training samples
		snake_case						:  str        =			np.eye(lowercase__				)  # Initialize weights as identity matrix
		for j in range(lowercase__				):
				snake_case						:  Any        =			point - x_train[j]
				snake_case						:  Dict        =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

		return weights



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  str        =			weight_matrix(lowercase__    ,       lowercase__    ,       lowercase__				)
		snake_case						:  str        =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Optional[int]        =			np.zeros(len(lowercase__				)				)  # Initialize array of predictions
		for i, item in enumerate(lowercase__				):
				snake_case						:  Dict        =			item @ local_weight(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)

		return y_pred



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       str    ,       lowercase__					:       str				):
		import seaborn as sns

		snake_case						:  Union[str, Any]        =			sns.load_dataset(lowercase__				)
		snake_case						:  Any        =			np.array(data[x_name]				)
		snake_case						:  str        =			np.array(data[y_name]				)

		snake_case						:  Any        =			np.ones(len(lowercase__				)				)

		# pairing elements of one and x_data
		snake_case						:  Optional[int]        =			np.column_stack((one, x_data)				)

		return x_train, x_data, y_data



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       str    ,       lowercase__					:       str    ,       ):
		snake_case						:  List[str]        =			np.sort(lowercase__    ,       axis=0				)
		plt.scatter(lowercase__    ,       lowercase__    ,       color="blue"				)
		plt.plot(
		    x_train_sorted[:, 1]    ,       preds[x_train[:, 1].argsort(0				)]    ,       color="yellow"    ,       linewidth=5    ,       )
		plt.title("Local Weighted Regression"				)
		plt.xlabel(lowercase__				)
		plt.ylabel(lowercase__				)
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							__A						,    __A						,    __A							=     load_data("tips", "total_bill", "tip")
							__A							=     local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")

