





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 	A_       :
	def __init__(    self							,					_A							,					_A=9_9							,					_A=1_3							,					_A=7							,					_A=9							,					_A=True							,					_A=True							,					_A=False							,					_A=3_2							,					_A=5							,					_A=4							,					_A=3_7							,					_A=8							,					_A=0.1							,					_A=0.0_02							,					_A=1							,					_A=0							,					_A=0							,					_A=None							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				encoder_seq_length
				UpperCAmelCase   		=				decoder_seq_length
				# For common tests
				UpperCAmelCase   		=				self.decoder_seq_length
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_attention_mask
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				d_ff
				UpperCAmelCase   		=				relative_attention_num_buckets
				UpperCAmelCase   		=				dropout_rate
				UpperCAmelCase   		=				initializer_factor
				UpperCAmelCase   		=				eos_token_id
				UpperCAmelCase   		=				pad_token_id
				UpperCAmelCase   		=				decoder_start_token_id
				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				decoder_layers
	def      _lowercase				(    self		):






				'''simple docstring'''







				return TaConfig.from_pretrained('''google/umt5-base'''		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A=None							,					_A=None							,					_A=None							,					_A=None							,					_A=None							,					):






				'''simple docstring'''







				if attention_mask is None:
							UpperCAmelCase   		=				input_ids.ne(config.pad_token_id		)
				if decoder_attention_mask is None:
							UpperCAmelCase   		=				decoder_input_ids.ne(config.pad_token_id		)
				if head_mask is None:
							UpperCAmelCase   		=				torch.ones(config.num_hidden_layers							,					config.num_attention_heads							,					device=_A		)
				if decoder_head_mask is None:
							UpperCAmelCase   		=				torch.ones(config.num_decoder_layers							,					config.num_attention_heads							,					device=_A		)
				if cross_attn_head_mask is None:
							UpperCAmelCase   		=				torch.ones(
							    config.num_decoder_layers							,					config.num_attention_heads							,					device=_A		)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.encoder_seq_length]							,					self.vocab_size		)
				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.decoder_seq_length]							,					self.vocab_size		)

				# we need to clamp the input ids here to avoid having pad token in between
				# this is because for NllbMoe the position_ids are prepared such that
				# all pad tokens have pos id = 2 and rest are between 2..seq_length
				# and the seq_length here is seq_length - num_pad_tokens
				# but when using past, there is no way of knowing if the past input ids had
				# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
				# position_ids being off by num_pad_tokens in past input
				UpperCAmelCase   		=				input_ids.clamp(self.pad_token_id + 1		)
				UpperCAmelCase   		=				decoder_input_ids.clamp(self.pad_token_id + 1		)

				UpperCAmelCase   		=				self.get_config()
				UpperCAmelCase   		=				config.num_attention_heads
				UpperCAmelCase   		=				self.prepare_inputs_dict(_A							,					_A							,					_A		)
				return config, input_dict
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				self.prepare_config_and_inputs()
				return config, inputs_dict
	def      _lowercase				(    self		):






				'''simple docstring'''







				return TaConfig(
				    vocab_size=1_6_6							,					d_model=self.hidden_size							,					d_ff=self.d_ff							,					d_kv=self.hidden_size // self.num_attention_heads							,					num_layers=self.num_hidden_layers							,					num_decoder_layers=self.decoder_layers							,					num_heads=self.num_attention_heads							,					relative_attention_num_buckets=self.relative_attention_num_buckets							,					dropout_rate=self.dropout_rate							,					initializer_factor=self.initializer_factor							,					eos_token_id=self.eos_token_id							,					bos_token_id=self.pad_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.decoder_start_token_id							,					)
	def      _lowercase				(    self		):






				'''simple docstring'''







				return TaConfig(
				    vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					d_ff=self.d_ff							,					d_kv=self.hidden_size // self.num_attention_heads							,					num_layers=self.num_hidden_layers							,					num_decoder_layers=self.decoder_layers							,					num_heads=self.num_attention_heads							,					relative_attention_num_buckets=self.relative_attention_num_buckets							,					dropout_rate=self.dropout_rate							,					initializer_factor=self.initializer_factor							,					eos_token_id=self.eos_token_id							,					bos_token_id=self.pad_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.decoder_start_token_id							,					)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				UMTaModel(config=_A		)
				model.to(_A		)
				model.eval()
				UpperCAmelCase   		=				model(
				    input_ids=_A							,					decoder_input_ids=_A							,					attention_mask=_A							,					decoder_attention_mask=_A							,					)
				UpperCAmelCase   		=				model(input_ids=_A							,					decoder_input_ids=_A		)
				UpperCAmelCase   		=				result.last_hidden_state
				UpperCAmelCase   		=				result.past_key_values
				UpperCAmelCase   		=				result.encoder_last_hidden_state

				self.parent.assertEqual(encoder_output.size()							,					(self.batch_size, self.encoder_seq_length, self.hidden_size)		)
				self.parent.assertEqual(decoder_output.size()							,					(self.batch_size, self.decoder_seq_length, self.hidden_size)		)
				# There should be `num_layers` key value embeddings stored in decoder_past
				self.parent.assertEqual(len(_A		)							,					config.num_layers		)
				# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
				self.parent.assertEqual(len(decoder_past[0]		)							,					4		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				UMTaModel(config=_A		).get_decoder().to(_A		).eval()
				# first forward pass
				UpperCAmelCase   		=				model(_A							,					use_cache=_A		)
				UpperCAmelCase   		=				model(_A		)
				UpperCAmelCase   		=				model(_A							,					use_cache=_A		)

				self.parent.assertTrue(len(_A		) == len(_A		)		)
				self.parent.assertTrue(len(_A		) == len(_A		) + 1		)

				UpperCAmelCase		,			UpperCAmelCase   		=				outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				UpperCAmelCase   		=				ids_tensor((self.batch_size, 1)							,					config.vocab_size		)

				# append to next input_ids and
				UpperCAmelCase   		=				torch.cat([input_ids, next_tokens]							,					dim=-1		)

				UpperCAmelCase   		=				model(_A		)['''last_hidden_state''']
				UpperCAmelCase   		=				model(_A							,					past_key_values=_A		)['''last_hidden_state''']

				# select random slice
				UpperCAmelCase   		=				ids_tensor((1,)							,					output_from_past.shape[-1]		).item()
				UpperCAmelCase   		=				output_from_no_past[:, -1, random_slice_idx].detach()
				UpperCAmelCase   		=				output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(_A							,					_A							,					atol=1E-3		)		)







	def      _lowercase				(    self							,					_A							,					_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				UMTaModel(config=_A		).to(_A		).half().eval()
				UpperCAmelCase   		=				model(**_A		)['''last_hidden_state''']
				self.parent.assertFalse(torch.isnan(_A		).any().item()		)

@require_torch
class 	A_       (a_     ,							a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	UpperCAmelCase__					=			(UMTaForConditionalGeneration,) if is_torch_available() else ()
	UpperCAmelCase__					=			(
	    {
	        '''conversational''': UMTaForConditionalGeneration,
	        '''feature-extraction''': UMTaModel,
	        '''summarization''': UMTaForConditionalGeneration,
	        '''text2text-generation''': UMTaForConditionalGeneration,
	        '''translation''': UMTaForConditionalGeneration,
	        '''question-answering''': UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	UpperCAmelCase__					=			[0.8, 0.9]
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				UMTaModelTester(self		)
	@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				UpperCAmelCase   		=				UMTaModel(config_and_inputs[0]		).to(_A		)
				with tempfile.TemporaryDirectory() as tmpdirname:
							torch.onnx.export(
							    _A							,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							,					F"""{tmpdirname}/t5_test.onnx"""							,					export_params=_A							,					opset_version=9							,					input_names=['''input_ids''', '''decoder_input_ids''']							,					)
	@unittest.skipIf(torch_device == '''cpu'''							,					'''Cant do half precision'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				UpperCAmelCase   		=				config_and_inputs[0]
				UpperCAmelCase   		=				UMTaForConditionalGeneration(_A		).eval()
				model.to(_A		)

				UpperCAmelCase   		=				{
				    '''head_mask''': torch.zeros(config.num_layers							,					config.num_heads							,					device=_A		),
				    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers							,					config.num_heads							,					device=_A		),
				    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers							,					config.num_heads							,					device=_A		),
				}

				for attn_name, (name, mask) in zip(_A							,					head_masking.items()		):
							UpperCAmelCase   		=				{name: mask}
							# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
							if name == "head_mask":
										UpperCAmelCase   		=				torch.ones(
										    config.num_decoder_layers							,					config.num_heads							,					device=_A		)

							UpperCAmelCase   		=				model.generate(
							    config_and_inputs[1]['''input_ids''']							,					num_beams=1							,					max_length=3							,					output_attentions=_A							,					return_dict_in_generate=_A							,					**_A							,					)
							# We check the state of decoder_attentions and cross_attentions just from the last step
							UpperCAmelCase   		=				out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
							self.assertEqual(sum([w.sum().item() for w in attn_weights]		)							,					0.0		)







	@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass

@require_torch
@require_sentencepiece
@require_tokenizers
class 	A_       (unittest.TestCase			):
	@slow
	@unittest.skip(
	    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''							,					return_dict=_A		).to(_A		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''google/umt5-small'''							,					use_fast=_A							,					legacy=_A		)
				UpperCAmelCase   		=				[
				    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
				    '''No se como puedo <extra_id_0>.''',
				    '''This is the reason why we <extra_id_0> them.''',
				    '''The <extra_id_0> walks in <extra_id_1>, seats''',
				    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
				]
				UpperCAmelCase   		=				tokenizer(_A							,					return_tensors='''pt'''							,					padding=_A		).input_ids
				# fmt: off
				UpperCAmelCase   		=				torch.tensor(
				    [
				        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
				        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
				    ]		)
				# fmt: on
				torch.testing.assert_allclose(_A							,					_A		)

				UpperCAmelCase   		=				model.generate(input_ids.to(_A		)		)
				UpperCAmelCase   		=				[
				    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
				    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				]
				UpperCAmelCase   		=				tokenizer.batch_decode(_A		)
				self.assertEqual(_A							,					_A		)










import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cva.imread(_A							,					0		)
				UpperCAmelCase		,			UpperCAmelCase   		=				img.shape
				UpperCAmelCase   		=				[]
				UpperCAmelCase   		=				img.copy()
				UpperCAmelCase   		=				cva.cvtColor(_A							,					cva.COLOR_GRAY2RGB		)
				UpperCAmelCase		,			UpperCAmelCase   		=				np.gradient(_A		)
				UpperCAmelCase   		=				dx**2
				UpperCAmelCase   		=				dy**2
				UpperCAmelCase   		=				dx * dy
				UpperCAmelCase   		=				0.04
				UpperCAmelCase   		=				self.window_size // 2
				for y in range(_A							,					h - offset		):
							for x in range(_A							,					w - offset		):
										UpperCAmelCase   		=				ixx[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				iyy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				ixy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				det - k * (trace**2)
										# Can change the value
										if r > 0.5:
													corner_list.append([x, y, r]		)
													color_img.itemset((y, x, 0)							,					0		)
													color_img.itemset((y, x, 1)							,					0		)
													color_img.itemset((y, x, 2)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



