import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class        lowerCamelCase__				(   unittest.TestCase				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							tempfile.mkdtemp()

										# fmt: off
										_lowerCAmelCase							=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
										# fmt: on
										_lowerCAmelCase							=							dict(zip(lowercase__  ,  range(len(lowercase__						)						)						)						)
										_lowerCAmelCase							=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
										_lowerCAmelCase							=							{"""unk_token""": """<unk>"""}

										_lowerCAmelCase							=							os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['vocab_file']						)
										_lowerCAmelCase							=							os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['merges_file']						)
										with open(self.vocab_file  ,  'w'  ,  encoding='utf-8'						) as fp:
																	fp.write(json.dumps(lowercase__						) + '\n'						)
										with open(self.merges_file  ,  'w'  ,  encoding='utf-8'						) as fp:
																	fp.write('\n'.join(lowercase__						)						)

										_lowerCAmelCase							=							{
										    """do_resize""": True,
										    """size""": 20,
										    """do_center_crop""": True,
										    """crop_size""": 18,
										    """do_normalize""": True,
										    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
										    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
										}
										_lowerCAmelCase							=							os.path.join(self.tmpdirname  ,  lowercase__						)
										with open(self.image_processor_file  ,  'w'  ,  encoding='utf-8'						) as fp:
																	json.dump(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  **lowercase__	:	Union[str, Any]						):
										return CLIPTokenizer.from_pretrained(self.tmpdirname  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  **lowercase__	:	Optional[int]						):
										return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  **lowercase__	:	Tuple						):
										return ViTImageProcessor.from_pretrained(self.tmpdirname  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										shutil.rmtree(self.tmpdirname						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							[np.random.randint(2_55  ,  size=(3, 30, 4_00)  ,  dtype=np.uinta						)]

										_lowerCAmelCase							=							[Image.fromarray(np.moveaxis(lowercase__  ,  0  ,  -1						)						) for x in image_inputs]

										return image_inputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.get_tokenizer()
										_lowerCAmelCase							=							self.get_rust_tokenizer()
										_lowerCAmelCase							=							self.get_image_processor()

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)
										processor_slow.save_pretrained(self.tmpdirname						)
										_lowerCAmelCase							=							CLIPSegProcessor.from_pretrained(self.tmpdirname  ,  use_fast=lowercase__						)

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)
										processor_fast.save_pretrained(self.tmpdirname						)
										_lowerCAmelCase							=							CLIPSegProcessor.from_pretrained(self.tmpdirname						)

										self.assertEqual(processor_slow.tokenizer.get_vocab()  ,  tokenizer_slow.get_vocab()						)
										self.assertEqual(processor_fast.tokenizer.get_vocab()  ,  tokenizer_fast.get_vocab()						)
										self.assertEqual(tokenizer_slow.get_vocab()  ,  tokenizer_fast.get_vocab()						)
										self.assertIsInstance(processor_slow.tokenizer  ,  lowercase__						)
										self.assertIsInstance(processor_fast.tokenizer  ,  lowercase__						)

										self.assertEqual(processor_slow.image_processor.to_json_string()  ,  image_processor.to_json_string()						)
										self.assertEqual(processor_fast.image_processor.to_json_string()  ,  image_processor.to_json_string()						)
										self.assertIsInstance(processor_slow.image_processor  ,  lowercase__						)
										self.assertIsInstance(processor_fast.image_processor  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=self.get_tokenizer()  ,  image_processor=self.get_image_processor()						)
										processor.save_pretrained(self.tmpdirname						)

										_lowerCAmelCase							=							self.get_tokenizer(bos_token='(BOS)'  ,  eos_token='(EOS)'						)
										_lowerCAmelCase							=							self.get_image_processor(do_normalize=lowercase__  ,  padding_value=1.0						)

										_lowerCAmelCase							=							CLIPSegProcessor.from_pretrained(
										    self.tmpdirname  ,  bos_token='(BOS)'  ,  eos_token='(EOS)'  ,  do_normalize=lowercase__  ,  padding_value=1.0						)

										self.assertEqual(processor.tokenizer.get_vocab()  ,  tokenizer_add_kwargs.get_vocab()						)
										self.assertIsInstance(processor.tokenizer  ,  lowercase__						)

										self.assertEqual(processor.image_processor.to_json_string()  ,  image_processor_add_kwargs.to_json_string()						)
										self.assertIsInstance(processor.image_processor  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							self.get_image_processor()
										_lowerCAmelCase							=							self.get_tokenizer()

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)

										_lowerCAmelCase							=							self.prepare_image_inputs()

										_lowerCAmelCase							=							image_processor(lowercase__  ,  return_tensors='np'						)
										_lowerCAmelCase							=							processor(images=lowercase__  ,  return_tensors='np'						)

										for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()  ,  input_processor[key].sum()  ,  delta=1e-2						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.get_image_processor()
										_lowerCAmelCase							=							self.get_tokenizer()

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)

										_lowerCAmelCase							=							"""lower newer"""

										_lowerCAmelCase							=							processor(text=lowercase__						)

										_lowerCAmelCase							=							tokenizer(lowercase__						)

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]  ,  encoded_processor[key]						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							self.get_image_processor()
										_lowerCAmelCase							=							self.get_tokenizer()

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)

										_lowerCAmelCase							=							"""lower newer"""
										_lowerCAmelCase							=							self.prepare_image_inputs()

										_lowerCAmelCase							=							processor(text=lowercase__  ,  images=lowercase__						)

										self.assertListEqual(list(inputs.keys()						)  ,  ['input_ids', 'attention_mask', 'pixel_values']						)

										# test if it raises when no input is passed
										with pytest.raises(lowercase__						):
																	processor()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase							=							self.get_image_processor()
										_lowerCAmelCase							=							self.get_tokenizer()

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)

										_lowerCAmelCase							=							self.prepare_image_inputs()
										_lowerCAmelCase							=							self.prepare_image_inputs()

										_lowerCAmelCase							=							processor(images=lowercase__  ,  visual_prompt=lowercase__						)

										self.assertListEqual(list(inputs.keys()						)  ,  ['pixel_values', 'conditional_pixel_values']						)

										# test if it raises when no input is passed
										with pytest.raises(lowercase__						):
																	processor()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.get_image_processor()
										_lowerCAmelCase							=							self.get_tokenizer()

										_lowerCAmelCase							=							CLIPSegProcessor(tokenizer=lowercase__  ,  image_processor=lowercase__						)

										_lowerCAmelCase							=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_lowerCAmelCase							=							processor.batch_decode(lowercase__						)
										_lowerCAmelCase							=							tokenizer.batch_decode(lowercase__						)

										self.assertListEqual(lowercase__  ,  lowercase__						)












import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		'linear'
	__magic_name__   :       Dict      	=		'cosine'
	__magic_name__   :       Optional[int]      	=		'cosine_with_restarts'
	__magic_name__   :       List[str]      	=		'polynomial'
	__magic_name__   :       Any      	=		'constant'
	__magic_name__   :       Union[str, Any]      	=		'constant_with_warmup'
	__magic_name__   :       str      	=		'piecewise_constant'






def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int = -1				)     ->						Optional[Any]:
					return LambdaLR(snake_case		,	lambda snake_case				: 1		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int = -1				)     ->						List[Any]:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1.0		,	snake_case				)				)
										return 1.0

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		str		,	snake_case   :		int = -1				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Optional[Any]             =      {}
					__UpperCamelCase					:	int             =      step_rules.split(""","""				)
					for rule_str in rule_list[:-1]:
										__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      rule_str.split(""":"""				)
										__UpperCamelCase					:	int             =      int(snake_case				)
										__UpperCamelCase					:	Union[str, Any]             =      float(snake_case				)
										__UpperCamelCase					:	Optional[int]             =      value
					__UpperCamelCase					:	Dict             =      float(rule_list[-1]				)

					def create_rules_function(snake_case   :		List[str]		,	snake_case   :		Any				):
										def rule_func(snake_case   :		int				) -> float:
															__UpperCamelCase					:	Any             =      sorted(rules_dict.keys()				)
															for i, sorted_step in enumerate(snake_case				):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					__UpperCamelCase					:	Tuple             =      create_rules_function(snake_case		,	snake_case				)

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		int		,	snake_case   :		Optional[Any]		,	snake_case   :		Union[str, Any]		,	snake_case   :		str=-1				)     ->						str:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										return max(
										    0.0		,	float(num_training_steps - current_step				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		float = 0.5		,	snake_case   :		int = -1				)     ->						List[str]:

					def lr_lambda(snake_case   :		Dict				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	Dict             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * float(snake_case				) * 2.0 * progress				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int = 1		,	snake_case   :		int = -1				)     ->						Tuple:

					def lr_lambda(snake_case   :		Optional[int]				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	List[str]             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										if progress >= 1.0:
															return 0.0
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * ((float(snake_case				) * progress) % 1.0)				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[Any]		,	snake_case   :		Optional[int]		,	snake_case   :		str=1e-7		,	snake_case   :		List[str]=1.0		,	snake_case   :		Dict=-1				)     ->						Tuple:
					__UpperCamelCase					:	Tuple             =      optimizer.defaults["""lr"""]
					if not (lr_init > lr_end):
										raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''				)

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															__UpperCamelCase					:	List[str]             =      lr_init - lr_end
															__UpperCamelCase					:	Any             =      num_training_steps - num_warmup_steps
															__UpperCamelCase					:	List[str]             =      1 - (current_step - num_warmup_steps) / decay_steps
															__UpperCamelCase					:	List[Any]             =      lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)


a__		     =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 				A__ (snake_case   :		Union[str, SchedulerType]		,	snake_case   :		Optimizer		,	snake_case   :		Optional[str] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		int = 1		,	snake_case   :		float = 1.0		,	snake_case   :		int = -1		,	)     ->						Dict:
					__UpperCamelCase					:	List[str]             =      SchedulerType(snake_case				)
					__UpperCamelCase					:	int             =      TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(snake_case		,	last_epoch=snake_case				)

					if name == SchedulerType.PIECEWISE_CONSTANT:
										return schedule_func(snake_case		,	step_rules=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_warmup_steps`
					if num_warmup_steps is None:
										raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''				)

					if name == SchedulerType.CONSTANT_WITH_WARMUP:
										return schedule_func(snake_case		,	num_warmup_steps=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_training_steps`
					if num_training_steps is None:
										raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''				)

					if name == SchedulerType.COSINE_WITH_RESTARTS:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	num_cycles=snake_case		,	last_epoch=snake_case		,	)

					if name == SchedulerType.POLYNOMIAL:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	power=snake_case		,	last_epoch=snake_case		,	)

					return schedule_func(
					    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	last_epoch=snake_case				)






