



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class     _A       (	unittest.TestCase    ):
       UpperCamelCase__  :    str											=						ViTImageProcessor if is_vision_available() else None



       @property
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				(3, 32, 128)
           __a						       =				tempfile.mkdtemp()

           # fmt: off
           __a						       =				['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           __a						       =				dict(zip(__SCREAMING_SNAKE_CASE     ,		range(len(__SCREAMING_SNAKE_CASE))))

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

           __a						       =				{
               '''do_normalize''': False,
               '''do_resize''': True,
               '''image_processor_type''': '''ViTImageProcessor''',
               '''resample''': 3,
               '''size''': {'''height''': 32, '''width''': 128},
           }
           __a						       =				os.path.join(self.tmpdirname     ,		__SCREAMING_SNAKE_CASE)
           with open(self.image_processor_file     ,		'''w'''     ,		encoding='''utf-8''') as fp:
               json.dump(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           return ViTImageProcessor.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				np.random.randint(255     ,		size=(3, 30, 400)     ,		dtype=np.uinta)

           __a						       =				Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE     ,		0     ,		-1))

           return image_input



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_tokenizer()
           __a						       =				self.get_image_processor()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)
           processor.save_pretrained(self.tmpdirname)
           __a						       =				MgpstrProcessor.from_pretrained(self.tmpdirname     ,		use_fast=__SCREAMING_SNAKE_CASE)

           self.assertEqual(processor.char_tokenizer.get_vocab()     ,		tokenizer.get_vocab())
           self.assertIsInstance(processor.char_tokenizer     ,		__SCREAMING_SNAKE_CASE)

           self.assertEqual(processor.image_processor.to_json_string()     ,		image_processor.to_json_string())
           self.assertIsInstance(processor.image_processor     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_tokenizer()
           __a						       =				self.get_image_processor()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)
           processor.save_pretrained(self.tmpdirname)

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

           __a						       =				MgpstrProcessor.from_pretrained(
               self.tmpdirname     ,		bos_token='''(BOS)'''     ,		eos_token='''(EOS)'''     ,		do_normalize=__SCREAMING_SNAKE_CASE     ,		padding_value=1.0)

           self.assertEqual(processor.char_tokenizer.get_vocab()     ,		tokenizer_add_kwargs.get_vocab())
           self.assertIsInstance(processor.char_tokenizer     ,		__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				self.prepare_image_inputs()

           __a						       =				image_processor(__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')
           __a						       =				processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')

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



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				'''test'''

           __a						       =				processor(text=__SCREAMING_SNAKE_CASE)

           __a						       =				tokenizer(__SCREAMING_SNAKE_CASE)
           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]     ,		encoded_processor[key])



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				'''test'''
           __a						       =				self.prepare_image_inputs()

           __a						       =				processor(text=__SCREAMING_SNAKE_CASE     ,		images=__SCREAMING_SNAKE_CASE)

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

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



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

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

           __a						       =				processor.char_decode(__SCREAMING_SNAKE_CASE)
           __a						       =				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)
           __a						       =				[seq.replace(''' '''     ,		'''''') for seq in decoded_tok]

           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				None
           __a						       =				self.prepare_image_inputs()

           __a						       =				processor(text=__SCREAMING_SNAKE_CASE     ,		images=__SCREAMING_SNAKE_CASE)

           self.assertListEqual(list(inputs.keys())     ,		processor.model_input_names)





       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_tokenizer()

           __a						       =				MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,		image_processor=__SCREAMING_SNAKE_CASE)

           __a						       =				torch.randn(1     ,		27     ,		38)
           __a						       =				torch.randn(1     ,		27     ,		50_257)
           __a						       =				torch.randn(1     ,		27     ,		30_522)

           __a						       =				processor.batch_decode([char_input, bpe_input, wp_input])

           self.assertListEqual(list(results.keys())     ,		['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''])










import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						(DDPMParallelScheduler,)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__SCREAMING_SNAKE_CASE     ,		prediction_type=__SCREAMING_SNAKE_CASE     ,		sample_max_value=__SCREAMING_SNAKE_CASE     ,		)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for t in [0, 500, 999]:
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				self.dummy_sample_deter + 0.1
           __a						       =				self.dummy_sample_deter - 0.1

           __a						       =				samplea.shape[0]
           __a						       =				torch.stack([samplea, samplea, samplea]     ,		dim=0)
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				model(samples.flatten(0     ,		1)     ,		timesteps.flatten(0     ,		1))
           __a						       =				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE     ,		timesteps.flatten(0     ,		1)     ,		samples.flatten(0     ,		1))

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 11_53.18_33) < 1E-2
           assert abs(result_mean.item() - 0.50_05) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_58.96_06) < 1E-2
           assert abs(result_mean.item() - 0.33_72) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_02.02_96) < 1E-2
           assert abs(result_mean.item() - 0.26_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)

           __a						       =				scheduler.timesteps

           for i, timestep in enumerate(__SCREAMING_SNAKE_CASE):
               if i == len(__SCREAMING_SNAKE_CASE) - 1:
                   __a						       =				-1
               else:
                   __a						       =				timesteps[i + 1]

               __a						       =				scheduler.previous_timestep(__SCREAMING_SNAKE_CASE)
               __a						       =				prev_t.item()

               self.assertEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 51, 0]

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''`custom_timesteps` must be in descending order.'''):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]
           __a						       =				len(__SCREAMING_SNAKE_CASE)

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
               scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE     ,		timesteps=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __SCREAMING_SNAKE_CASE     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)





