





'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class 			_lowercase						(       unittest.TestCase					):







					def     UpperCamelCase			( self    ) ->	str:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					@property
					def     UpperCamelCase			( self    ) ->	Tuple:
						torch.manual_seed(0    )
						snake_case				=			UNetaDModel(
						    sample_size=(32, 64)    ,   in_channels=1    ,   out_channels=1    ,   layers_per_block=2    ,   block_out_channels=(1_28, 1_28)    ,   down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')    ,   up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')    ,   )
						return model







					@property
					def     UpperCamelCase			( self    ) ->	List[str]:
						torch.manual_seed(0    )
						snake_case				=			UNetaDConditionModel(
						    sample_size=(64, 32)    ,   in_channels=1    ,   out_channels=1    ,   layers_per_block=2    ,   block_out_channels=(1_28, 1_28)    ,   down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')    ,   up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')    ,   cross_attention_dim=10    ,   )
						return model







					@property
					def     UpperCamelCase			( self    ) ->	Union[str, Any]:
						torch.manual_seed(0    )
						snake_case				=			AutoencoderKL(
						    sample_size=(1_28, 64)    ,   in_channels=1    ,   out_channels=1    ,   latent_channels=1    ,   layers_per_block=2    ,   block_out_channels=(1_28, 1_28)    ,   down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')    ,   up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')    ,   )
						snake_case				=			UNetaDModel(
						    sample_size=(64, 32)    ,   in_channels=1    ,   out_channels=1    ,   layers_per_block=2    ,   block_out_channels=(1_28, 1_28)    ,   down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')    ,   up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')    ,   )
						return vqvae, unet







					@slow
					def     UpperCamelCase			( self    ) ->	Tuple:
						snake_case				=			"cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case				=			Mel(
						    x_res=self.dummy_unet.config.sample_size[1]    ,   y_res=self.dummy_unet.config.sample_size[0]    ,   )

						snake_case				=			DDPMScheduler()
						snake_case				=			AudioDiffusionPipeline(vqvae=__a    ,   unet=self.dummy_unet    ,   mel=__a    ,   scheduler=__a    )
						snake_case				=			pipe.to(__a    )
						pipe.set_progress_bar_config(disable=__a    )

						snake_case				=			torch.Generator(device=__a    ).manual_seed(42    )
						snake_case				=			pipe(generator=__a    ,   steps=4    )
						snake_case				=			output.audios[0]
						snake_case				=			output.images[0]

						snake_case				=			torch.Generator(device=__a    ).manual_seed(42    )
						snake_case				=			pipe(generator=__a    ,   steps=4    ,   return_dict=__a    )
						snake_case				=			output[0][0]

						assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
						assert (
						    image.height == self.dummy_unet.config.sample_size[0]
						    and image.width == self.dummy_unet.config.sample_size[1]
						)
						snake_case				=			np.frombuffer(image.tobytes()    ,   dtype='''uint8'''    )[:10]
						snake_case				=			np.frombuffer(image_from_tuple.tobytes()    ,   dtype='''uint8'''    )[:10]
						snake_case				=			np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() == 0

						snake_case				=			Mel(
						    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]    ,   y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]    ,   )

						snake_case				=			DDIMScheduler()
						snake_case				=			self.dummy_vqvae_and_unet
						snake_case				=			AudioDiffusionPipeline(
						    vqvae=self.dummy_vqvae_and_unet[0]    ,   unet=dummy_vqvae_and_unet[1]    ,   mel=__a    ,   scheduler=__a    )
						snake_case				=			pipe.to(__a    )
						pipe.set_progress_bar_config(disable=__a    )

						np.random.seed(0    )
						snake_case				=			np.random.uniform(-1    ,   1    ,   ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)    )
						snake_case				=			torch.Generator(device=__a    ).manual_seed(42    )
						snake_case				=			pipe(raw_audio=__a    ,   generator=__a    ,   start_step=5    ,   steps=10    )
						snake_case				=			output.images[0]

						assert (
						    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
						    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
						)
						snake_case				=			np.frombuffer(image.tobytes()    ,   dtype='''uint8'''    )[:10]
						snake_case				=			np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0

						snake_case				=			self.dummy_unet_condition
						snake_case				=			AudioDiffusionPipeline(
						    vqvae=self.dummy_vqvae_and_unet[0]    ,   unet=__a    ,   mel=__a    ,   scheduler=__a    )
						snake_case				=			pipe.to(__a    )
						pipe.set_progress_bar_config(disable=__a    )

						np.random.seed(0    )
						snake_case				=			torch.rand((1, 1, 10)    )
						snake_case				=			pipe(generator=__a    ,   encoding=__a    )
						snake_case				=			output.images[0]
						snake_case				=			np.frombuffer(image.tobytes()    ,   dtype='''uint8'''    )[:10]
						snake_case				=			np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0




@slow
@require_torch_gpu
class 			_lowercase						(       unittest.TestCase					):







					def     UpperCamelCase			( self    ) ->	Optional[int]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					def     UpperCamelCase			( self    ) ->	str:
						snake_case				=			torch_device

						snake_case				=			DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''    )
						snake_case				=			pipe.to(__a    )
						pipe.set_progress_bar_config(disable=__a    )

						snake_case				=			torch.Generator(device=__a    ).manual_seed(42    )
						snake_case				=			pipe(generator=__a    )
						snake_case				=			output.audios[0]
						snake_case				=			output.images[0]

						assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
						assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
						snake_case				=			np.frombuffer(image.tobytes()    ,   dtype='''uint8'''    )[:10]
						snake_case				=			np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0








'''simple docstring'''







import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					CodeGenTokenizer
     _UpperCAmelCase										=					CodeGenTokenizerFast
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					{'''add_prefix_space''': True}
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	Tuple:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      snake_case				=			[
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
          '''<|endoftext|>''',
      ]
      snake_case				=			dict(zip(A__    ,   range(len(A__    )    )    )    )
      snake_case				=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      snake_case				=			{'''unk_token''': '''<unk>'''}

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







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:
      kwargs.update(self.special_tokens_map    )
      return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:
      kwargs.update(self.special_tokens_map    )
      return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
      snake_case				=			'''lower newer'''
      snake_case				=			'''lower newer'''
      return input_text, output_text







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			CodeGenTokenizer(self.vocab_file    ,   self.merges_file    ,   **self.special_tokens_map    )
      snake_case				=			'''lower newer'''
      snake_case				=			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      snake_case				=			tokenizer.tokenize(A__    ,   add_prefix_space=A__    )
      self.assertListEqual(A__    ,   A__    )

      snake_case				=			tokens + [tokenizer.unk_token]
      snake_case				=			[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A__    )    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      if not self.test_rust_tokenizer:
       return

      snake_case				=			self.get_tokenizer()
      snake_case				=			self.get_rust_tokenizer(add_prefix_space=A__    )

      snake_case				=			'''lower newer'''

      # Testing tokenization
      snake_case				=			tokenizer.tokenize(A__    ,   add_prefix_space=A__    )
      snake_case				=			rust_tokenizer.tokenize(A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing conversion to ids without special tokens
      snake_case				=			tokenizer.encode(A__    ,   add_special_tokens=A__    ,   add_prefix_space=A__    )
      snake_case				=			rust_tokenizer.encode(A__    ,   add_special_tokens=A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing conversion to ids with special tokens
      snake_case				=			self.get_rust_tokenizer(add_prefix_space=A__    )
      snake_case				=			tokenizer.encode(A__    ,   add_prefix_space=A__    )
      snake_case				=			rust_tokenizer.encode(A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing the unknown token
      snake_case				=			tokens + [rust_tokenizer.unk_token]
      snake_case				=			[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__    )    ,   A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	List[str]:
      # It's very difficult to mix/test pretokenization with byte-level
      # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
      pass







     def     UpperCamelCase			( self    ,   A__=15    ) ->	Tuple:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
        snake_case				=			self.rust_tokenizer_class.from_pretrained(A__    ,   **A__    )

        # Simple input
        snake_case				=			'''This is a simple input'''
        snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']
        snake_case				=			('''This is a simple input''', '''This is a pair''')
        snake_case				=			[
            ('''This is a simple input 1''', '''This is a simple input 2'''),
            ('''This is a simple pair 1''', '''This is a simple pair 2'''),
        ]

        # Simple input tests
        self.assertRaises(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

        # Simple input
        self.assertRaises(A__    ,   tokenizer_r.encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

        # Simple input
        self.assertRaises(
            A__    ,   tokenizer_r.batch_encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    ,   )

        # Pair input
        self.assertRaises(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

        # Pair input
        self.assertRaises(A__    ,   tokenizer_r.encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

        # Pair input
        self.assertRaises(
            A__    ,   tokenizer_r.batch_encode_plus    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    ,   )







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   pad_token='''<pad>'''    )

      # Simple input
      snake_case				=			'''This is a simple input'''
      snake_case				=			['''This is a simple input looooooooong''', '''This is a simple input''']
      snake_case				=			('''This is a simple input''', '''This is a pair''')
      snake_case				=			[
          ('''This is a simple input loooooong''', '''This is a simple input'''),
          ('''This is a simple pair loooooong''', '''This is a simple pair'''),
      ]

      snake_case				=			tokenizer.pad_token_id

      snake_case				=			tokenizer(A__    ,   padding='''max_length'''    ,   max_length=30    ,   return_tensors='''np'''    )
      snake_case				=			tokenizer(A__    ,   padding=A__    ,   truncate=A__    ,   return_tensors='''np'''    )
      snake_case				=			tokenizer(*A__    ,   padding='''max_length'''    ,   max_length=60    ,   return_tensors='''np'''    )
      snake_case				=			tokenizer(A__    ,   padding=A__    ,   truncate=A__    ,   return_tensors='''np'''    )

      # s
      # test single string max_length padding
      self.assertEqual(out_s['''input_ids'''].shape[-1]    ,   30    )
      self.assertTrue(pad_token_id in out_s['''input_ids''']    )
      self.assertTrue(0 in out_s['''attention_mask''']    )

      # s2
      # test automatic padding
      self.assertEqual(out_sa['''input_ids'''].shape[-1]    ,   33    )
      # long slice doesn't have padding
      self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]    )
      self.assertFalse(0 in out_sa['''attention_mask'''][0]    )
      # short slice does have padding
      self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]    )
      self.assertTrue(0 in out_sa['''attention_mask'''][1]    )

      # p
      # test single pair max_length padding
      self.assertEqual(out_p['''input_ids'''].shape[-1]    ,   60    )
      self.assertTrue(pad_token_id in out_p['''input_ids''']    )
      self.assertTrue(0 in out_p['''attention_mask''']    )

      # p2
      # test automatic padding pair
      self.assertEqual(out_pa['''input_ids'''].shape[-1]    ,   52    )
      # long slice pair doesn't have padding
      self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]    )
      self.assertFalse(0 in out_pa['''attention_mask'''][0]    )
      # short slice pair does have padding
      self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]    )
      self.assertTrue(0 in out_pa['''attention_mask'''][1]    )







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			'''$$$'''
      snake_case				=			CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   bos_token=A__    ,   add_bos_token=A__    )

      snake_case				=			'''This is a simple input'''
      snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']

      snake_case				=			tokenizer.bos_token_id

      snake_case				=			tokenizer(A__    )
      snake_case				=			tokenizer(A__    )

      self.assertEqual(out_s.input_ids[0]    ,   A__    )
      self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

      snake_case				=			tokenizer.decode(out_s.input_ids    )
      snake_case				=			tokenizer.batch_decode(out_sa.input_ids    )

      self.assertEqual(decode_s.split()[0]    ,   A__    )
      self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''    )

      snake_case				=			'''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
      snake_case				=			'''\nif len_a > len_b:      result = a\nelse:      result = b'''

      snake_case				=			tokenizer.encode(A__    )
      snake_case				=			['''^#''', re.escape('''<|endoftext|>'''    ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
      snake_case				=			tokenizer.decode(A__    ,   truncate_before_pattern=A__    )
      self.assertEqual(A__    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      pass

