



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	):


						'''simple docstring'''





						_snake_case			:     Optional[int]      							=      ViTImageProcessor if is_vision_available() else None



						@property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



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

							# fmt: off
							__lowercase		       =			["[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
							__lowercase		       =			dict(zip(lowerCamelCase							,      range(len(lowerCamelCase	)	)	)	)

							__lowercase		       =			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(lowerCamelCase	) + "\n"	)

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



						def     _snake_case	(	self	:							Union[str, Any]							,      **lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							str							,      **lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							shutil.rmtree(self.tmpdirname	)



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



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

							__lowercase		       =			Image.fromarray(np.moveaxis(lowerCamelCase							,      0							,      -1	)	)

							return image_input



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.get_tokenizer()
							__lowercase		       =			self.get_image_processor()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)
							processor.save_pretrained(self.tmpdirname	)
							__lowercase		       =			MgpstrProcessor.from_pretrained(self.tmpdirname							,      use_fast=lowerCamelCase	)

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

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



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.get_tokenizer()
							__lowercase		       =			self.get_image_processor()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)
							processor.save_pretrained(self.tmpdirname	)

							__lowercase		       =			self.get_tokenizer(bos_token="(BOS)"							,      eos_token="(EOS)"	)
							__lowercase		       =			self.get_image_processor(do_normalize=lowerCamelCase							,      padding_value=1.0	)

							__lowercase		       =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname							,      bos_token="(BOS)"							,      eos_token="(EOS)"							,      do_normalize=lowerCamelCase							,      padding_value=1.0	)

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

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



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.get_image_processor()
							__lowercase		       =			self.get_tokenizer()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)

							__lowercase		       =			self.prepare_image_inputs()

							__lowercase		       =			image_processor(lowerCamelCase							,      return_tensors="np"	)
							__lowercase		       =			processor(images=lowerCamelCase							,      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     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.get_image_processor()
							__lowercase		       =			self.get_tokenizer()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)

							__lowercase		       =			"test"

							__lowercase		       =			processor(text=lowerCamelCase	)

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



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.get_image_processor()
							__lowercase		       =			self.get_tokenizer()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)

							__lowercase		       =			"test"
							__lowercase		       =			self.prepare_image_inputs()

							__lowercase		       =			processor(text=lowerCamelCase							,      images=lowerCamelCase	)

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

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



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






							'''simple docstring'''



							__lowercase		       =			self.get_image_processor()
							__lowercase		       =			self.get_tokenizer()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)

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

							__lowercase		       =			processor.char_decode(lowerCamelCase	)
							__lowercase		       =			tokenizer.batch_decode(lowerCamelCase	)
							__lowercase		       =			[seq.replace(" "							,      ""	) for seq in decoded_tok]

							self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.get_image_processor()
							__lowercase		       =			self.get_tokenizer()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)

							__lowercase		       =			None
							__lowercase		       =			self.prepare_image_inputs()

							__lowercase		       =			processor(text=lowerCamelCase							,      images=lowerCamelCase	)

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



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.get_image_processor()
							__lowercase		       =			self.get_tokenizer()

							__lowercase		       =			MgpstrProcessor(tokenizer=lowerCamelCase							,      image_processor=lowerCamelCase	)

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

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

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








import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class   _A   :


						'''simple docstring'''





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






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    sample_size=32							,      layers_per_block=1							,      block_out_channels=[32, 64]							,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]							,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,      in_channels=3							,      out_channels=6							,      cross_attention_dim=32							,      encoder_hid_dim=32							,      attention_head_dim=8							,      addition_embed_type="text"							,      addition_embed_type_num_heads=2							,      cross_attention_norm="group_norm"							,      resnet_time_scale_shift="scale_shift"							,      act_fn="gelu"							,      )
							unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      thresholding=lowerCamelCase							,      dynamic_thresholding_ratio=0.95							,      sample_max_value=1.0							,      prediction_type="epsilon"							,      variance_type="learned_range"							,      )

							torch.manual_seed(0	)
							__lowercase		       =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    sample_size=32							,      layers_per_block=[1, 2]							,      block_out_channels=[32, 64]							,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]							,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,      in_channels=6							,      out_channels=6							,      cross_attention_dim=32							,      encoder_hid_dim=32							,      attention_head_dim=8							,      addition_embed_type="text"							,      addition_embed_type_num_heads=2							,      cross_attention_norm="group_norm"							,      resnet_time_scale_shift="scale_shift"							,      act_fn="gelu"							,      class_embed_type="timestep"							,      mid_block_scale_factor=1.414							,      time_embedding_act_fn="gelu"							,      time_embedding_dim=32							,      )
							unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      thresholding=lowerCamelCase							,      dynamic_thresholding_ratio=0.95							,      sample_max_value=1.0							,      prediction_type="epsilon"							,      variance_type="learned_range"							,      )

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      )

							torch.manual_seed(0	)
							__lowercase		       =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			self.pipeline_class(**lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)

							__lowercase		       =			inputs["prompt"]
							__lowercase		       =			inputs["generator"]
							__lowercase		       =			inputs["num_inference_steps"]
							__lowercase		       =			inputs["output_type"]

							if "image" in inputs:
								__lowercase		       =			inputs["image"]
							else:
								__lowercase		       =			None

							if "mask_image" in inputs:
								__lowercase		       =			inputs["mask_image"]
							else:
								__lowercase		       =			None

							if "original_image" in inputs:
								__lowercase		       =			inputs["original_image"]
							else:
								__lowercase		       =			None

							__lowercase       ,    __lowercase		       =			pipe.encode_prompt(lowerCamelCase	)

							# inputs with prompt converted to embeddings
							__lowercase		       =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								__lowercase		       =			image

							if mask_image is not None:
								__lowercase		       =			mask_image

							if original_image is not None:
								__lowercase		       =			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowerCamelCase	)
								__lowercase		       =			self.pipeline_class.from_pretrained(lowerCamelCase	)
								pipe_loaded.to(lowerCamelCase	)
								pipe_loaded.set_progress_bar_config(disable=lowerCamelCase	)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(lowerCamelCase							,      lowerCamelCase	) is None							,      f"""`{optional_component}` did not stay set to None after loading."""							,      )

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)

							__lowercase		       =			inputs["generator"]
							__lowercase		       =			inputs["num_inference_steps"]
							__lowercase		       =			inputs["output_type"]

							# inputs with prompt converted to embeddings
							__lowercase		       =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								__lowercase		       =			image

							if mask_image is not None:
								__lowercase		       =			mask_image

							if original_image is not None:
								__lowercase		       =			original_image

							__lowercase		       =			pipe_loaded(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(to_np(lowerCamelCase	) - to_np(lowerCamelCase	)	).max()
							self.assertLess(lowerCamelCase							,      1e-4	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			self.pipeline_class(**lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowerCamelCase	)
								__lowercase		       =			self.pipeline_class.from_pretrained(lowerCamelCase	)
								pipe_loaded.to(lowerCamelCase	)
								pipe_loaded.set_progress_bar_config(disable=lowerCamelCase	)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe_loaded(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(to_np(lowerCamelCase	) - to_np(lowerCamelCase	)	).max()
							self.assertLess(lowerCamelCase							,      1e-4	)



