
'''simple docstring'''





import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

UpperCAmelCase            = 0.25 + 0.0j
UpperCAmelCase            = -0.4 + 0.6j
UpperCAmelCase            = -0.1 + 0.651j
UpperCAmelCase            = -2.0
UpperCAmelCase            = 56
UpperCAmelCase            = 2.0
UpperCAmelCase            = 666

def   A			(							A_				:       complex     ,				A_				:       numpy.ndarray							):
						return numpy.exp(A_							) + c_parameter

def   A			(							A_				:       complex     ,				A_				:       numpy.ndarray							):
						return z_values * z_values + c_parameter

def   A			(							A_				:       float     ,				A_				:       int							):
						snake_case							:  Union[str, Any]			=     numpy.linspace(-window_size     ,				A_     ,				A_							)
						snake_case							:  Union[str, Any]			=     x.reshape((nb_pixels, 1)							)
						snake_case							:  List[str]			=     numpy.linspace(-window_size     ,				A_     ,				A_							)
						snake_case							:  Optional[Any]			=     y.reshape((1, nb_pixels)							)
						return x + 1.0j * y

def   A			(							A_				:       Callable[[Any, numpy.ndarray], numpy.ndarray]     ,				A_				:       Any     ,				A_				:       int     ,				A_				:       numpy.ndarray     ,				A_				:       float | None = None     ,				):
						snake_case							:  Optional[Any]			=     z_a.astype('''complex64'''							)
						for _ in range(A_							):
												snake_case							:  Optional[int]			=     eval_function(A_     ,				A_							)
												if infinity is not None:
																		numpy.nan_to_num(A_     ,				copy=A_     ,				nan=A_							)
																		snake_case							:  Dict			=     infinity
						return z_n

def   A			(							A_				:       str     ,				A_				:       Any     ,				A_				:       float     ,				A_				:       numpy.ndarray     ,				):
						snake_case							:  Tuple			=     (abs(A_							)).transpose()
						snake_case							:  List[str]			=     abs_z_final[::-1, :]
						pyplot.matshow(abs_z_final < escape_radius							)
						pyplot.title(F"""Julia set of ${function_label}$, $c={function_params}$"""							)
						pyplot.show()

def   A			(							):
						warnings.filterwarnings(
						    '''ignore'''     ,				category=A_     ,				message='''overflow encountered in multiply'''							)
						warnings.filterwarnings(
						    '''ignore'''     ,				category=A_     ,				message='''invalid value encountered in multiply'''     ,				)
						warnings.filterwarnings(
						    '''ignore'''     ,				category=A_     ,				message='''overflow encountered in absolute'''							)
						warnings.filterwarnings(
						    '''ignore'''     ,				category=A_     ,				message='''overflow encountered in exp'''							)


if __name__ == "__main__":
						UpperCAmelCase            = prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						UpperCAmelCase            = 24
						UpperCAmelCase            = 2 * abs(c_cauliflower) + 1
						UpperCAmelCase            = iterate_function(
						    eval_quadratic_polynomial,
						    c_cauliflower,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("z^2+c", c_cauliflower, escape_radius, z_final)

						UpperCAmelCase            = 64
						UpperCAmelCase            = 2 * abs(c_polynomial_a) + 1
						UpperCAmelCase            = iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

						UpperCAmelCase            = 161
						UpperCAmelCase            = 2 * abs(c_polynomial_a) + 1
						UpperCAmelCase            = iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

						UpperCAmelCase            = 12
						UpperCAmelCase            = 1_0000.0
						UpperCAmelCase            = iterate_function(
						    eval_exponential,
						    c_exponential,
						    nb_iterations,
						    z_a + 2,
						    infinity=1.0e10,
						)
						show_results("e^z+c", c_exponential, escape_radius, z_final)



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 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							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  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      patch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      max_token_length
													__UpperCamelCase  	=      num_character_labels
													__UpperCamelCase  	=      num_bpe_labels
													__UpperCamelCase  	=      num_wordpiece_labels
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      mlp_ratio
													__UpperCamelCase  	=      patch_embeds_hidden_size
													__UpperCamelCase  	=      output_hidden_states



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )
													__UpperCamelCase  	=      self.get_config()
													return config, pixel_values



							def      A__		(	self      )-> Tuple:





													'''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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													with torch.no_grad():
																			__UpperCamelCase  	=      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      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase	,				__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






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

							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_  ,						has_text_modality=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      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      )-> List[str]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

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



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCamelCase  	=      ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]  ,						SCREAMING_SNAKE_CASE_      )



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





													'''simple docstring'''






													pass



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      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

																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertTrue(model.is_gradient_checkpointing      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													def check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()

																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )

																			__UpperCamelCase  	=      outputs.hidden_states

																			__UpperCamelCase  	=      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]  ,						)

													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      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"]
																			__UpperCamelCase  	=      True

																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase  	=      _config_zero_init(SCREAMING_SNAKE_CASE_      )
													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      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      )-> List[Any]:





													'''simple docstring'''






													pass





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

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






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

							"""simple docstring"""






							@slow
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      '''alibaba-damo/mgp-str-base'''
													__UpperCamelCase  	=      MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      prepare_img()
													__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values.to(SCREAMING_SNAKE_CASE_      )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )

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

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

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

													__UpperCamelCase  	=      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=SCREAMING_SNAKE_CASE_  ,						)

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




