'''simple docstring'''




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       UpperCAmelCase__  :






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     Dict:
        torch.manual_seed(0				)
        lowercase_					:       Tuple     				=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'				)

        torch.manual_seed(0				)
        lowercase_					:       Any     				=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'				)

        torch.manual_seed(0				)
        lowercase_					:       Any     				=					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_					:       List[Any]     				=					DDPMScheduler(
            num_train_timesteps=1_000       ,				beta_schedule='squaredcos_cap_v2'       ,				beta_start=0.00_01       ,				beta_end=0.02       ,				thresholding=_a       ,				dynamic_thresholding_ratio=0.95       ,				sample_max_value=1.0       ,				prediction_type='epsilon'       ,				variance_type='learned_range'       ,				)

        torch.manual_seed(0				)
        lowercase_					:       List[str]     				=					IFWatermarker()

        return {
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "watermarker": watermarker,
            "safety_checker": None,
            "feature_extractor": None,
        }
   def 		_lowerCamelCase (self				)    ->     Any:
        torch.manual_seed(0				)
        lowercase_					:       Any     				=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'				)

        torch.manual_seed(0				)
        lowercase_					:       List[Any]     				=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'				)

        torch.manual_seed(0				)
        lowercase_					:       Union[str, Any]     				=					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=1.4_14       ,				time_embedding_act_fn='gelu'       ,				time_embedding_dim=32       ,				)
        unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

        torch.manual_seed(0				)
        lowercase_					:       Tuple     				=					DDPMScheduler(
            num_train_timesteps=1_000       ,				beta_schedule='squaredcos_cap_v2'       ,				beta_start=0.00_01       ,				beta_end=0.02       ,				thresholding=_a       ,				dynamic_thresholding_ratio=0.95       ,				sample_max_value=1.0       ,				prediction_type='epsilon'       ,				variance_type='learned_range'       ,				)

        torch.manual_seed(0				)
        lowercase_					:       Optional[int]     				=					DDPMScheduler(
            num_train_timesteps=1_000       ,				beta_schedule='squaredcos_cap_v2'       ,				beta_start=0.00_01       ,				beta_end=0.02       ,				)

        torch.manual_seed(0				)
        lowercase_					:       Optional[int]     				=					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 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					self.get_dummy_components()
        lowercase_					:       List[Any]     				=					self.pipeline_class(**_a				)
        pipe.to(_a				)
        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       Tuple     				=					self.get_dummy_inputs(_a				)

        lowercase_					:       str     				=					inputs['prompt']
        lowercase_					:       List[str]     				=					inputs['generator']
        lowercase_					:       Tuple     				=					inputs['num_inference_steps']
        lowercase_					:       int     				=					inputs['output_type']

        if "image" in inputs:
             lowercase_					:       str     				=					inputs['image']
        else:
             lowercase_					:       List[str]     				=					None

        if "mask_image" in inputs:
             lowercase_					:       Any     				=					inputs['mask_image']
        else:
             lowercase_					:       Union[str, Any]     				=					None

        if "original_image" in inputs:
             lowercase_					:       str     				=					inputs['original_image']
        else:
             lowercase_					:       Any     				=					None

        lowercase_				,lowercase_					:       Optional[Any]     				=					pipe.encode_prompt(_a				)

        # inputs with prompt converted to embeddings
        lowercase_					:       List[Any]     				=					{
            '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_					:       List[Any]     				=					image

        if mask_image is not None:
             lowercase_					:       Union[str, Any]     				=					mask_image

        if original_image is not None:
             lowercase_					:       Optional[Any]     				=					original_image

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

        lowercase_					:       Union[str, Any]     				=					pipe(**_a				)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_a				)
             lowercase_					:       List[str]     				=					self.pipeline_class.from_pretrained(_a				)
             pipe_loaded.to(_a				)
             pipe_loaded.set_progress_bar_config(disable=_a				)

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

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

        lowercase_					:       str     				=					self.get_dummy_inputs(_a				)

        lowercase_					:       Tuple     				=					inputs['generator']
        lowercase_					:       Optional[int]     				=					inputs['num_inference_steps']
        lowercase_					:       int     				=					inputs['output_type']

        # inputs with prompt converted to embeddings
        lowercase_					:       Any     				=					{
            '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_					:       str     				=					image

        if mask_image is not None:
             lowercase_					:       List[str]     				=					mask_image

        if original_image is not None:
             lowercase_					:       Optional[Any]     				=					original_image

        lowercase_					:       Optional[Any]     				=					pipe_loaded(**_a				)[0]

        lowercase_					:       List[Any]     				=					np.abs(to_np(_a				) - to_np(_a				)				).max()
        self.assertLess(_a       ,				1e-4				)





   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       int     				=					self.get_dummy_components()
        lowercase_					:       Union[str, Any]     				=					self.pipeline_class(**_a				)
        pipe.to(_a				)
        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       Any     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       List[str]     				=					pipe(**_a				)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_a				)
             lowercase_					:       Dict     				=					self.pipeline_class.from_pretrained(_a				)
             pipe_loaded.to(_a				)
             pipe_loaded.set_progress_bar_config(disable=_a				)

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

        lowercase_					:       List[str]     				=					self.get_dummy_inputs(_a				)
        lowercase_					:       Union[str, Any]     				=					pipe_loaded(**_a				)[0]

        lowercase_					:       str     				=					np.abs(to_np(_a				) - to_np(_a				)				).max()
        self.assertLess(_a       ,				1e-4				)





'''simple docstring'''




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       str     =    TransfoXLTokenizer
   A							:       Optional[int]     =    False
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        super().setUp()

        lowercase_					:       Any     				=					[
            '<unk>',
            '[CLS]',
            '[SEP]',
            'want',
            'unwanted',
            'wa',
            'un',
            'running',
            ',',
            'low',
            'l',
        ]
        lowercase_					:       Any     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Union[str, Any]:
        lowercase_					:       str     				=					True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					'<unk> UNwanted , running'
        lowercase_					:       int     				=					'<unk> unwanted, running'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					TransfoXLTokenizer(vocab_file=self.vocab_file       ,				lower_case=_a				)

        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize('<unk> UNwanted , running'				)
        self.assertListEqual(_a       ,				['<unk>', 'unwanted', ',', 'running']				)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				[0, 4, 8, 7]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[str]     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['hello', '!', 'how', 'are', 'you', '?']				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)
        lowercase_					:       int     				=					'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
        lowercase_					:       List[str]     				=					[
            'Hello',
            '(',
            'bracket',
            ')',
            'and',
            'side',
            '@-@',
            'scrolled',
            '[',
            'and',
            ']',
            'Henry',
            '\'s',
            '$',
            '5',
            '@,@',
            '000',
            'with',
            '3',
            '@.@',
            '34',
            'm',
            '.',
            'What',
            '\'s',
            'up',
            '!',
            '?',
        ]

        self.assertListEqual(tokenizer.tokenize(_a				)       ,				_a				)

        self.assertEqual(tokenizer.convert_tokens_to_string(_a				)       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[int]     				=					self.get_tokenizer()
        lowercase_					:       str     				=					len(_a				)

        tokenizer.add_tokens(['new1', 'new2']				)
        tokenizer.move_added_token('new1'       ,				1				)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(_a				)       ,				original_len + 2				)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode('new1'				)       ,				[1]				)
        self.assertEqual(tokenizer.decode([1]				)       ,				'new1'				)




