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       ,  lowerCAmelCase_       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=13       ,  lowerCAmelCase_=(32, 128)       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=27       ,  lowerCAmelCase_=38       ,  lowerCAmelCase_=99       ,  lowerCAmelCase_=99       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=4.0       ,  lowerCAmelCase_=257       ,  lowerCAmelCase_=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 __lowerCAmelCase      (   self       ):
		_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 __lowerCAmelCase      (   self       ):
		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       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.prepare_config_and_inputs()
		_lowercase   ,  _lowercase   =config_and_inputs
		_lowercase   ={"pixel_values": pixel_values}
		return config, inputs_dict

@require_torch
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE              =					{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
	__SCREAMING_SNAKE_CASE              =					False

	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =MgpstrModelTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=lowerCAmelCase_       ,  has_text_modality=lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		self.config_tester.run_common_tests()


	def __lowerCAmelCase      (   self       ):
		_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 __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _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 __lowerCAmelCase      (   self       ):
		_lowercase   ,  _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 __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _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 __lowerCAmelCase      (   self       ):
		def check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_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   ,  _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 __lowerCAmelCase      (   self       ):
		_lowercase   ,  _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 __lowerCAmelCase      (   self       ):
		pass

def 	__lowerCamelCase							(				)      ->       Tuple:
	_lowercase   ="https://i.postimg.cc/ZKwLg2Gw/367-14.png"
	_lowercase   =Image.open(requests.get(__a      ,							stream=__a		).raw		).convert("RGB"		)
	return im
@require_vision
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	@slow
	def __lowerCAmelCase      (   self       ):
		_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(
		    [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]       ,  device=lowerCAmelCase_       ,  )

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





import numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if labels.ndim != 1:
			print("Error: Input labels must be one dimensional"       )

		return np.mean((labels - prediction) ** 2       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if x.ndim != 1:
			print("Error: Input data set must be one dimensional"       )
			return
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			print("Error: X and y have different lengths"       )
			return
		if y.ndim != 1:
			print("Error: Data set labels must be one dimensional"       )
			return

		if len(lowerCAmelCase_       ) < 2 * self.min_leaf_size:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		if self.depth == 1:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		_lowercase   =0
		_lowercase   =self.mean_squared_error(lowerCAmelCase_       ,  np.mean(lowerCAmelCase_       )       ) * 2
		for i in range(len(lowerCAmelCase_       )       ):
			if len(x[:i]       ) < self.min_leaf_size:
				continue
			elif len(x[i:]       ) < self.min_leaf_size:
				continue
			else:
				_lowercase   =self.mean_squared_error(x[:i]       ,  np.mean(y[:i]       )       )
				_lowercase   =self.mean_squared_error(x[i:]       ,  np.mean(y[i:]       )       )
				_lowercase   =error_left + error_right
				if error < min_error:
					_lowercase   =i
					_lowercase   =error

		if best_split != 0:
			_lowercase   =x[:best_split]
			_lowercase   =y[:best_split]
			_lowercase   =x[best_split:]
			_lowercase   =y[best_split:]

			_lowercase   =x[best_split]
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			self.left.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
			self.right.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =np.mean(lowerCAmelCase_       )

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if self.prediction is not None:
			return self.prediction
		elif self.left or self.right is not None:
			if x >= self.decision_boundary:
				return self.right.predict(lowerCAmelCase_       )
			else:
				return self.left.predict(lowerCAmelCase_       )
		else:
			print("Error: Decision tree not yet trained"       )
			return None

class 	_a  :



	"""simple docstring"""



	@staticmethod
	def __lowerCAmelCase      (   lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =float(0       )
		for label in labels:
			squared_error_sum += (label - prediction) ** 2

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

	_lowercase   =(np.random.rand(10		) * 2) - 1
	_lowercase   =np.array([tree.predict(__a		) for x in test_cases]		)
	_lowercase   =np.mean((predictions - test_cases) ** 2		)

	print("Test values: " + str(__a		)		)
	print("Predictions: " + str(__a		)		)
	print("Average error: " + str(__a		)		)


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod(name="mean_squarred_error", verbose=True)




