import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





@skip_mps
class      lowerCAmelCase						(					__SCREAMING_SNAKE_CASE			,		unittest.TestCase			):
					_SCREAMING_SNAKE_CASE     :     List[Any]		=TextToVideoSDPipeline
					_SCREAMING_SNAKE_CASE     :     List[Any]		=TEXT_TO_IMAGE_PARAMS
					_SCREAMING_SNAKE_CASE     :     List[Any]		=TEXT_TO_IMAGE_BATCH_PARAMS
					# No `output_type`.
					_SCREAMING_SNAKE_CASE     :     Union[str, Any]		=frozenset(
					    [
					        """num_inference_steps""",
					        """generator""",
					        """latents""",
					        """return_dict""",
					        """callback""",
					        """callback_steps""",
					    ]			)



					def 			a__  (					self       ):
												torch.manual_seed(0       )
												_A=	UNetaDConditionModel(
												    block_out_channels=(32, 64, 64, 64)				,      layers_per_block=2				,      sample_size=32				,      in_channels=4				,      out_channels=4				,      down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')				,      up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')				,      cross_attention_dim=32				,      attention_head_dim=4				,      )
												_A=	DDIMScheduler(
												    beta_start=0.00085				,      beta_end=0.012				,      beta_schedule='scaled_linear'				,      clip_sample=_a				,      set_alpha_to_one=_a				,      )
												torch.manual_seed(0       )
												_A=	AutoencoderKL(
												    block_out_channels=[32, 64]				,      in_channels=3				,      out_channels=3				,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,      latent_channels=4				,      sample_size=128				,      )
												torch.manual_seed(0       )
												_A=	CLIPTextConfig(
												    bos_token_id=0				,      eos_token_id=2				,      hidden_size=32				,      intermediate_size=37				,      layer_norm_eps=1E-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      hidden_act='gelu'				,      projection_dim=512				,      )
												_A=	CLIPTextModel(_a       )
												_A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

												_A=	{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												}
												return components



					def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=0       ):




												if str(_a       ).startswith('mps'       ):
																			_A=	torch.manual_seed(_a       )
												else:
																			_A=	torch.Generator(device=_a       ).manual_seed(_a       )
												_A=	{
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """guidance_scale""": 6.0,
												    """output_type""": """pt""",
												}
												return inputs



					def 			a__  (					self       ):
												_A=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												_A=	self.get_dummy_components()
												_A=	TextToVideoSDPipeline(**_a       )
												_A=	sd_pipe.to(_a       )
												sd_pipe.set_progress_bar_config(disable=_a       )

												_A=	self.get_dummy_inputs(_a       )
												_A=	"""np"""
												_A=	sd_pipe(**_a       ).frames
												_A=	frames[0][-3:, -3:, -1]

												assert frames[0].shape == (64, 64, 3)
												_A=	np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]       )

												assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



					def 			a__  (					self       ):
												self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_a				,      expected_max_diff=3E-3       )



					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
					def 			a__  (					self       ):
												self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_a				,      expected_max_diff=1E-2       )



					@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'       )
					def 			a__  (					self       ):
												pass



					@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'       )
					def 			a__  (					self       ):
												pass



					@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'       )
					def 			a__  (					self       ):
												pass





					def 			a__  (					self       ):
												return super().test_progress_bar()





@slow
@skip_mps
class      lowerCAmelCase						(					unittest.TestCase			):



					def 			a__  (					self       ):
												_A=	load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'       )

												_A=	TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'       )
												_A=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
												_A=	pipe.to('cuda'       )

												_A=	"""Spiderman is surfing"""
												_A=	torch.Generator(device='cpu'       ).manual_seed(0       )

												_A=	pipe(_a				,      generator=_a				,      num_inference_steps=25				,      output_type='pt'       ).frames
												_A=	video_frames.cpu().numpy()

												assert np.abs(expected_video - video       ).mean() < 5E-2





					def 			a__  (					self       ):
												_A=	load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'       )

												_A=	TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'       )
												_A=	pipe.to('cuda'       )

												_A=	"""Spiderman is surfing"""
												_A=	torch.Generator(device='cpu'       ).manual_seed(0       )

												_A=	pipe(_a				,      generator=_a				,      num_inference_steps=2				,      output_type='pt'       ).frames
												_A=	video_frames.cpu().numpy()

												assert np.abs(expected_video - video       ).mean() < 5E-2
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      UpperCAmelCase_								=							'''pt'''
elif is_tf_available():
      UpperCAmelCase_								=							'''tf'''
else:
      UpperCAmelCase_								=							'''jax'''





class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=ByTaTokenizer
     _SCREAMING_SNAKE_CASE     :     Dict		=False



     def 			a__  (					self       ):
            super().setUp()
            _A=	ByTaTokenizer()
            tokenizer.save_pretrained(self.tmpdirname       )



     @cached_property
     def 			a__  (					self       ):
            return ByTaTokenizer.from_pretrained('google/byt5-small'       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            return self.tokenizer_class.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False				,      lowerCAmelCase__=20				,      lowerCAmelCase__=5       ):
            # XXX The default common tokenizer tests assume that every ID is decodable on its own.
            # This assumption is invalid for ByT5 because single bytes might not be
            # valid utf-8 (byte 128 for instance).
            # Here we're overriding the smallest possible method to provide
            # a clean sequence without making the same assumption.

            _A=	[]
            for i in range(len(lowerCAmelCase__       )       ):
                   try:
                          _A=	tokenizer.decode([i]				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
                   except UnicodeDecodeError:
                          pass
                   toks.append((i, tok)       )

            _A=	list(filter(lambda lowerCAmelCase__       : re.match(r'^[ a-zA-Z]+$'				,      t[1]       )				,      lowerCAmelCase__       )       )
            _A=	list(filter(lambda lowerCAmelCase__       : [t[0]] == tokenizer.encode(t[1]				,      add_special_tokens=lowerCAmelCase__       )				,      lowerCAmelCase__       )       )
            if max_length is not None and len(lowerCAmelCase__       ) > max_length:
                   _A=	toks[:max_length]
            if min_length is not None and len(lowerCAmelCase__       ) < min_length and len(lowerCAmelCase__       ) > 0:
                   while len(lowerCAmelCase__       ) < min_length:
                          _A=	toks + toks
        # toks_str = [t[1] for t in toks]
            _A=	[t[0] for t in toks]

            # Ensure consistency
            _A=	tokenizer.decode(lowerCAmelCase__				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
            if " " not in output_txt and len(lowerCAmelCase__       ) > 1:
                   _A=	(
                       tokenizer.decode([toks_ids[0]]				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
                       + ' '
                       + tokenizer.decode(toks_ids[1:]				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
                   )
            if with_prefix_space:
                   _A=	' ' + output_txt
            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            return output_txt, output_ids



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']       )
            _A=	tokenizer(['hi', 'I went to the gym', '']       )
            self.assertListEqual(batch_with_eos_added['input_ids']				,      batch_without_eos_added['input_ids']       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	'Unicode €.'
            _A=	tokenizer(lowerCAmelCase__       )
            _A=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
            self.assertEqual(encoded['input_ids']				,      lowerCAmelCase__       )

            # decoding
            _A=	tokenizer.decode(lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      'Unicode €.</s>'       )

            _A=	tokenizer('e è é ê ë'       )
            _A=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
            self.assertEqual(encoded['input_ids']				,      lowerCAmelCase__       )
            # decoding
            _A=	tokenizer.decode(lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      'e è é ê ë</s>'       )

            # encode/decode, but with `encode` instead of `__call__`
            self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'       )       )				,      'e è é ê ë</s>'       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
            # fmt: off
            _A=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
            # fmt: on
            _A=	tokenizer(lowerCAmelCase__				,      padding=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )
            self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

            if FRAMEWORK != "jax":
                   _A=	list(batch.input_ids.numpy()[0]       )
            else:
                   _A=	list(batch.input_ids.tolist()[0]       )

            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            self.assertEqual((2, 37)				,      batch.input_ids.shape       )
            self.assertEqual((2, 37)				,      batch.attention_mask.shape       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
            _A=	tokenizer(lowerCAmelCase__				,      padding=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn('input_ids'				,      lowerCAmelCase__       )
            self.assertIn('attention_mask'				,      lowerCAmelCase__       )
            self.assertNotIn('decoder_input_ids'				,      lowerCAmelCase__       )
            self.assertNotIn('decoder_attention_mask'				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	[
                'Summary of the text.',
                'Another summary.',
            ]
            _A=	tokenizer(
                text_target=lowerCAmelCase__				,      max_length=32				,      padding='max_length'				,      truncation=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )
            self.assertEqual(32				,      targets['input_ids'].shape[1]       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	['A long paragraph for summarization. </s>']
            _A=	['Summary of the text. </s>']
            # fmt: off
            _A=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
            _A=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
            # fmt: on

            _A=	tokenizer(lowerCAmelCase__				,      text_target=lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      batch['input_ids'][0]       )
            self.assertEqual(lowerCAmelCase__				,      batch['labels'][0]       )



     def 			a__  (					self       ):
            # safety check on max_len default value so we are sure the test works
            _A=	self.get_tokenizers()
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          self.assertNotEqual(tokenizer.model_max_length				,      42       )

        # Now let's start the test
            _A=	self.get_tokenizers()
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          # Isolate this from the other tests because we save additional tokens/etc
                          _A=	tempfile.mkdtemp()

                          _A=	' He is very happy, UNwant\u00E9d,running'
                          _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          tokenizer.save_pretrained(lowerCAmelCase__       )

                          _A=	tokenizer.__class__.from_pretrained(lowerCAmelCase__       )
                          _A=	after_tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

                          shutil.rmtree(lowerCAmelCase__       )

            _A=	self.get_tokenizers(model_max_length=42       )
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          # Isolate this from the other tests because we save additional tokens/etc
                          _A=	tempfile.mkdtemp()

                          _A=	' He is very happy, UNwant\u00E9d,running'
                          tokenizer.add_tokens(['bim', 'bambam']       )
                          _A=	tokenizer.additional_special_tokens
                          additional_special_tokens.append('new_additional_special_token'       )
                          tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}       )
                          _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          tokenizer.save_pretrained(lowerCAmelCase__       )

                          _A=	tokenizer.__class__.from_pretrained(lowerCAmelCase__       )
                          _A=	after_tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )
                          self.assertIn('new_additional_special_token'				,      after_tokenizer.additional_special_tokens       )
                          self.assertEqual(after_tokenizer.model_max_length				,      42       )

                          _A=	tokenizer.__class__.from_pretrained(lowerCAmelCase__				,      model_max_length=43       )
                          self.assertEqual(tokenizer.model_max_length				,      43       )

                          shutil.rmtree(lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	[]
            if self.test_slow_tokenizer:
                   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

            if self.test_rust_tokenizer:
                   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

            for tokenizer_class, tokenizer_utils in tokenizer_list:
                   with tempfile.TemporaryDirectory() as tmp_dir:
                          tokenizer_utils.save_pretrained(lowerCAmelCase__       )

                          with open(os.path.join(lowerCAmelCase__				,      'special_tokens_map.json'       )				,      encoding='utf-8'       ) as json_file:
                                 _A=	json.load(lowerCAmelCase__       )

                          with open(os.path.join(lowerCAmelCase__				,      'tokenizer_config.json'       )				,      encoding='utf-8'       ) as json_file:
                                 _A=	json.load(lowerCAmelCase__       )

                          _A=	[f"<extra_id_{i}>" for i in range(125       )]

                          _A=	added_tokens_extra_ids + [
                              'an_additional_special_token'
                          ]
                          _A=	added_tokens_extra_ids + [
                              'an_additional_special_token'
                          ]

                          with open(os.path.join(lowerCAmelCase__				,      'special_tokens_map.json'       )				,      'w'				,      encoding='utf-8'       ) as outfile:
                                 json.dump(lowerCAmelCase__				,      lowerCAmelCase__       )
                          with open(os.path.join(lowerCAmelCase__				,      'tokenizer_config.json'       )				,      'w'				,      encoding='utf-8'       ) as outfile:
                                 json.dump(lowerCAmelCase__				,      lowerCAmelCase__       )

                          # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                          # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                          # "special_tokens_map.json" files
                          _A=	tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      )
                          self.assertIn(
                              'an_additional_special_token'				,      tokenizer_without_change_in_init.additional_special_tokens       )
                          # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                          self.assertEqual(
                              ['an_additional_special_token']				,      tokenizer_without_change_in_init.convert_ids_to_tokens(
                                  tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']       )       )				,      )

                          # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                          _A=	added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'				,      lstrip=lowerCAmelCase__       )]
                          _A=	tokenizer_class.from_pretrained(
                              lowerCAmelCase__				,      additional_special_tokens=lowerCAmelCase__				,      )

                          self.assertIn('a_new_additional_special_token'				,      tokenizer.additional_special_tokens       )
                          self.assertEqual(
                              ['a_new_additional_special_token']				,      tokenizer.convert_ids_to_tokens(
                                  tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']       )       )				,      )



     def 			a__  (					self       ):
            _A=	[]
            if self.test_slow_tokenizer:
                   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

            if self.test_rust_tokenizer:
                   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

            for tokenizer_class, tokenizer_utils in tokenizer_list:
                   with tempfile.TemporaryDirectory() as tmp_dir:
                          tokenizer_utils.save_pretrained(lowerCAmelCase__       )

                          _A=	tokenizer_class.from_pretrained(lowerCAmelCase__       )

                          self.assertTrue(tokenizer.decode([255]       ) == ''       )



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
            # and special added tokens as tokens
            _A=	self.get_tokenizers(fast=lowerCAmelCase__				,      do_lower_case=lowerCAmelCase__       )
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          _A=	['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                          _A=	tokenizer.convert_tokens_to_string(lowerCAmelCase__       )

                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	self.get_tokenizers()
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          _A=	[
                              'bos_token',
                              'eos_token',
                              'unk_token',
                              'sep_token',
                              'pad_token',
                              'cls_token',
                              'mask_token',
                          ]

                          _A=	0
                          _A=	tokenizer.convert_ids_to_tokens(
                              lowerCAmelCase__				,      skip_special_tokens=lowerCAmelCase__       )

                          for attr in attributes_list:
                                 setattr(lowerCAmelCase__				,      attr + '_id'				,      lowerCAmelCase__       )
                                 self.assertEqual(getattr(lowerCAmelCase__				,      lowerCAmelCase__       )				,      lowerCAmelCase__       )
                                 self.assertEqual(getattr(lowerCAmelCase__				,      attr + '_id'       )				,      lowerCAmelCase__       )

                                 setattr(lowerCAmelCase__				,      attr + '_id'				,      lowerCAmelCase__       )
                                 self.assertEqual(getattr(lowerCAmelCase__				,      lowerCAmelCase__       )				,      lowerCAmelCase__       )
                                 self.assertEqual(getattr(lowerCAmelCase__				,      attr + '_id'       )				,      lowerCAmelCase__       )

                          setattr(lowerCAmelCase__				,      'additional_special_tokens_ids'				,      []       )
                          self.assertListEqual(getattr(lowerCAmelCase__				,      'additional_special_tokens'       )				,      []       )
                          self.assertListEqual(getattr(lowerCAmelCase__				,      'additional_special_tokens_ids'       )				,      []       )

                          setattr(lowerCAmelCase__				,      'additional_special_tokens_ids'				,      [token_id_to_test_setters]       )
                          self.assertListEqual(getattr(lowerCAmelCase__				,      'additional_special_tokens'       )				,      [token_to_test_setters]       )
                          self.assertListEqual(getattr(lowerCAmelCase__				,      'additional_special_tokens_ids'       )				,      [token_id_to_test_setters]       )