
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.xglm.modeling_tf_xglm import (
							    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFXGLMForCausalLM,
							    TFXGLMModel,
							)

@require_tf
class        _UpperCamelCase				:






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = XGLMConfig
			__UpperCamelCase      :						int					      = {}
			__UpperCamelCase      :						str					      = """gelu"""

			def __init__(       self :       Tuple						,	snake_case_ :       int						,	snake_case_ :       str=14						,	snake_case_ :       Tuple=7						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       List[str]=True						,	snake_case_ :       int=99						,	snake_case_ :       Optional[int]=32						,	snake_case_ :       Any=2						,	snake_case_ :       Optional[Any]=4						,	snake_case_ :       Optional[int]=37						,	snake_case_ :       List[Any]="gelu"						,	snake_case_ :       Union[str, Any]=0.1						,	snake_case_ :       Any=0.1						,	snake_case_ :       Optional[int]=512						,	snake_case_ :       Tuple=0.02						,	):
							UpperCamelCase_:							str	=						parent
							UpperCamelCase_:							Tuple	=						batch_size
							UpperCamelCase_:							Optional[int]	=						seq_length
							UpperCamelCase_:							List[Any]	=						is_training
							UpperCamelCase_:							str	=						use_input_mask
							UpperCamelCase_:							Optional[Any]	=						use_labels
							UpperCamelCase_:							List[str]	=						vocab_size
							UpperCamelCase_:							Optional[int]	=						d_model
							UpperCamelCase_:							str	=						num_hidden_layers
							UpperCamelCase_:							Any	=						num_attention_heads
							UpperCamelCase_:							Dict	=						ffn_dim
							UpperCamelCase_:							Any	=						activation_function
							UpperCamelCase_:							Optional[Any]	=						activation_dropout
							UpperCamelCase_:							Dict	=						attention_dropout
							UpperCamelCase_:							int	=						max_position_embeddings
							UpperCamelCase_:							Union[str, Any]	=						initializer_range
							UpperCamelCase_:							List[Any]	=						None
							UpperCamelCase_:							Optional[int]	=						0
							UpperCamelCase_:							str	=						2
							UpperCamelCase_:							List[str]	=						1

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							return XGLMConfig.from_pretrained("""facebook/xglm-564M"""      )

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							List[str]	=						tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size      )						,	clip_value_min=0						,	clip_value_max=3      )

							UpperCamelCase_:							Union[str, Any]	=						None
							if self.use_input_mask:
											UpperCamelCase_:							Any	=						random_attention_mask([self.batch_size, self.seq_length]      )

							UpperCamelCase_:							Optional[Any]	=						self.get_config()

							UpperCamelCase_:							Tuple	=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]						,	2      )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)

			def       lowerCAmelCase__	(       self :       List[str]      ):
							return XGLMConfig(
							    vocab_size=self.vocab_size						,	d_model=self.hidden_size						,	num_layers=self.num_hidden_layers						,	attention_heads=self.num_attention_heads						,	ffn_dim=self.ffn_dim						,	activation_function=self.activation_function						,	activation_dropout=self.activation_dropout						,	attention_dropout=self.attention_dropout						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	use_cache=snake_case_						,	bos_token_id=self.bos_token_id						,	eos_token_id=self.eos_token_id						,	pad_token_id=self.pad_token_id						,	return_dict=snake_case_						,	)




			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							int	=						self.prepare_config_and_inputs()

							(
							    UpperCamelCase_       
							):							Tuple	=						config_and_inputs

							UpperCamelCase_:							Tuple	=						{
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict




@require_tf
class        _UpperCamelCase				(					_A     ,       _A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			__UpperCamelCase      :						List[str]					      = (TFXGLMForCausalLM,) if is_tf_available() else ()
			__UpperCamelCase      :						List[Any]					      = (
			    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			__UpperCamelCase      :						Any					      = False
			__UpperCamelCase      :						int					      = False
			__UpperCamelCase      :						List[str]					      = False

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Optional[int]	=						TFXGLMModelTester(self      )
							UpperCamelCase_:							Any	=						ConfigTester(self						,	config_class=snake_case_						,	n_embd=37      )

			def       lowerCAmelCase__	(       self :       List[str]      ):
							self.config_tester.run_common_tests()

			@slow
			def       lowerCAmelCase__	(       self :       List[Any]      ):







							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase_:							Tuple	=						TFXGLMModel.from_pretrained(snake_case_      )
											self.assertIsNotNone(snake_case_      )




			@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""      )
			def       lowerCAmelCase__	(       self :       Dict      ):
							super().test_resize_token_embeddings()




@require_tf
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			@slow
			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       Optional[Any]=True      ):
							UpperCamelCase_:							Union[str, Any]	=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""      )
							UpperCamelCase_:							Dict	=						tf.convert_to_tensor([[2, 268, 9865]]						,	dtype=tf.intaa      )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							UpperCamelCase_:							int	=						[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
							# fmt: on
							UpperCamelCase_:							Dict	=						model.generate(snake_case_						,	do_sample=snake_case_						,	num_beams=1      )
							if verify_outputs:
											self.assertListEqual(output_ids[0].numpy().tolist()						,	snake_case_      )

			@slow
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""      )
							UpperCamelCase_:							str	=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""      )

							tf.random.set_seed(0      )
							UpperCamelCase_:							int	=						tokenizer("""Today is a nice day and"""						,	return_tensors="""tf"""      )
							UpperCamelCase_:							List[Any]	=						tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(""":/CPU:0"""      ):
											UpperCamelCase_:							Tuple	=						model.generate(snake_case_						,	do_sample=snake_case_						,	seed=[7, 0]      )
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.decode(output_ids[0]						,	skip_special_tokens=snake_case_      )

							UpperCamelCase_:							int	=						(
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							List[Any]	=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""      )
							UpperCamelCase_:							Union[str, Any]	=						XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""      )

							UpperCamelCase_:							Any	=						'left'

							# use different length sentences to test batching
							UpperCamelCase_:							List[Any]	=						[
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When',
							    'Hello, my dog is a little',
							]

							UpperCamelCase_:							Dict	=						tokenizer(snake_case_						,	return_tensors="""tf"""						,	padding=snake_case_      )
							UpperCamelCase_:							int	=						inputs['input_ids']

							UpperCamelCase_:							Dict	=						model.generate(input_ids=snake_case_						,	attention_mask=inputs["""attention_mask"""]						,	max_new_tokens=12      )

							UpperCamelCase_:							Union[str, Any]	=						tokenizer(sentences[0]						,	return_tensors="""tf"""      ).input_ids
							UpperCamelCase_:							int	=						model.generate(input_ids=snake_case_						,	max_new_tokens=12      )

							UpperCamelCase_:							Tuple	=						tokenizer(sentences[1]						,	return_tensors="""tf"""      ).input_ids
							UpperCamelCase_:							Any	=						model.generate(input_ids=snake_case_						,	max_new_tokens=12      )

							UpperCamelCase_:							List[str]	=						tokenizer.batch_decode(snake_case_						,	skip_special_tokens=snake_case_      )
							UpperCamelCase_:							Optional[int]	=						tokenizer.decode(output_non_padded[0]						,	skip_special_tokens=snake_case_      )
							UpperCamelCase_:							Optional[int]	=						tokenizer.decode(output_padded[0]						,	skip_special_tokens=snake_case_      )

							UpperCamelCase_:							Any	=						[
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
							    'a single',
							    'Hello, my dog is a little bit of a shy one, but he is very friendly',
							]
							self.assertListEqual(snake_case_						,	snake_case_      )
							self.assertListEqual(snake_case_						,	[non_padded_sentence, padded_sentence]      )








import cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

			def __str__(       self :       int      ):
							return str(self.k      )




			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						cva.imread(snake_case_						,	0      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						img.shape
							UpperCamelCase_:							list[list[int]]	=						[]
							UpperCamelCase_:							int	=						img.copy()
							UpperCamelCase_:							Any	=						cva.cvtColor(snake_case_						,	cva.COLOR_GRAY2RGB      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						np.gradient(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						dx**2
							UpperCamelCase_:							Dict	=						dy**2
							UpperCamelCase_:							Optional[Any]	=						dx * dy
							UpperCamelCase_:							str	=						0.04
							UpperCamelCase_:							int	=						self.window_size // 2
							for y in range(snake_case_						,	h - offset      ):
											for x in range(snake_case_						,	w - offset      ):
															UpperCamelCase_:							List[Any]	=						ixx[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							int	=						iyy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							List[str]	=						ixy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							Dict	=						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)						,	255      )
							return color_img, corner_list


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






