





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
SCREAMING_SNAKE_CASE_      :			str		  =      '''stas/tiny-wmt19-en-ru'''

class 		snake_case_      (       UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		FSMTTokenizer
	__UpperCamelCase        =		False




	def       UpperCAmelCase (	self       :    Tuple      )					->   int:

			'''simple docstring'''




			super().setUp()

			# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
			__lowercase					=    [
			    'l',
			    'o',
			    'w',
			    'e',
			    'r',
			    's',
			    't',
			    'i',
			    'd',
			    'n',
			    'w</w>',
			    'r</w>',
			    't</w>',
			    'lo',
			    'low',
			    'er</w>',
			    'low</w>',
			    'lowest</w>',
			    'newer</w>',
			    'wider</w>',
			    '<unk>',
			]
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

			__lowercase					=    ['en', 'ru']
			__lowercase					=    {
			    'langs': self.langs,
			    'src_vocab_size': 10,
			    'tgt_vocab_size': 20,
			}

			__lowercase					=    os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['src_vocab_file']      )
			__lowercase					=    os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['tgt_vocab_file']      )
			__lowercase					=    os.path.join(self.tmpdirname				,			'tokenizer_config.json'      )
			__lowercase					=    os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['merges_file']      )
			with open(self.src_vocab_file				,			'w'      ) as fp:
					fp.write(json.dumps(__lowerCamelCase      )      )
			with open(self.tgt_vocab_file				,			'w'      ) as fp:
					fp.write(json.dumps(__lowerCamelCase      )      )
			with open(self.merges_file				,			'w'      ) as fp:
					fp.write('\n'.join(__lowerCamelCase      )      )
			with open(__lowerCamelCase				,			'w'      ) as fp:
					fp.write(json.dumps(__lowerCamelCase      )      )




	@cached_property
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'      )




	@cached_property
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    FSMTTokenizer.from_pretrained(__lowerCamelCase      )
			self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]				,			['en', 'ru']      )
			self.assertEqual(tokenizer.src_vocab_size				,			21      )
			self.assertEqual(tokenizer.tgt_vocab_size				,			21      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    FSMTTokenizer(self.langs				,			self.src_vocab_file				,			self.tgt_vocab_file				,			self.merges_file      )

			__lowercase					=    'lower'
			__lowercase					=    ['low', 'er</w>']
			__lowercase					=    tokenizer.tokenize(__lowerCamelCase      )
			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )

			__lowercase					=    tokens + ['<unk>']
			__lowercase					=    [14, 15, 20]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase      )				,			__lowerCamelCase      )




	@slow
	def       UpperCAmelCase (	self       :    Dict      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    self.tokenizer_ru_en

			__lowercase					=    tokenizer.encode('sequence builders'				,			add_special_tokens=__lowerCamelCase      )
			__lowercase					=    tokenizer.encode('multi-sequence build'				,			add_special_tokens=__lowerCamelCase      )

			__lowercase					=    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase      )
			__lowercase					=    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase				,			__lowerCamelCase      )

			assert encoded_sentence == text + [2]
			assert encoded_pair == text + [2] + text_a + [2]




	@slow
	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    self.tokenizer_en_ru
			__lowercase					=    self.tokenizer_ru_en

			__lowercase					=    [
			    [
			        'Here\'s a little song I wrote. Don\'t worry, be happy.',
			        [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
			    ],
			    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
			]

			# if data needs to be recreated or added, run:
			# import torch
			# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
			# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

			for src_text, tgt_input_ids in targets:
					__lowercase					=    tokenizer_enc.encode(__lowerCamelCase				,			return_tensors=__lowerCamelCase      )
					self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )

					# and decode backward, using the reversed languages model
					__lowercase					=    tokenizer_dec.decode(__lowerCamelCase				,			skip_special_tokens=__lowerCamelCase      )
					self.assertEqual(__lowerCamelCase				,			__lowerCamelCase      )




	@slow
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   int:

			'''simple docstring'''




			__lowercase					=    FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'				,			do_lower_case=__lowerCamelCase      )
			__lowercase					=    tokenizer.tokenize('USA is United States of America'      )
			__lowercase					=    ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )




	@unittest.skip('FSMTConfig.__init__  requires non-optional args'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			pass






	@unittest.skip('FSMTConfig.__init__  requires non-optional args'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass











import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    str      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			return (4, 14, 16)




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




			pass




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Any:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			super().test_output()




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    {
			    'block_out_channels': (32, 64, 128, 256),
			    'in_channels': 14,
			    'out_channels': 14,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1.0,
			    'out_block_type': 'OutConv1DBlock',
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='unet'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(loading_info['missing_keys']      )				,			0      )

			model.to(__lowerCamelCase      )
			__lowercase					=    model(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'				,			subfolder='unet'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    model.config.in_channels
			__lowercase					=    16
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample.permute(0				,			2				,			1      )

			__lowercase					=    output[0, -3:, -3:].flatten()
			# fmt: off
			__lowercase					=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			pass






	@slow
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    'harmonai/maestro-150k'
			__lowercase					=    UNetaDModel.from_pretrained(__lowerCamelCase				,			subfolder='unet'      )
			model.to(__lowerCamelCase      )

			__lowercase					=    65_536
			__lowercase					=    torch.sin(torch.arange(__lowerCamelCase      )[None, None, :].repeat(1				,			2				,			1      )      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([1]      ).to(__lowerCamelCase      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample

			__lowercase					=    output.abs().sum()
			__lowercase					=    output.abs().max()

			assert (output_sum - 224.0896).abs() < 0.5
			assert (output_max - 0.0607).abs() < 4E-4





class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    int      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Tuple      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 1)




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   int:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_init_args_and_inputs_for_common()
			__lowercase					=    self.model_class(**__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()

			with torch.no_grad():
					__lowercase					=    model(**__lowerCamelCase      )

					if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
							__lowercase					=    output.sample

			self.assertIsNotNone(__lowerCamelCase      )
			__lowercase					=    torch.Size((inputs_dict['sample'].shape[0], 1)      )
			self.assertEqual(output.shape				,			__lowerCamelCase				,			'Input and output shapes do not match'      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    'in_channels': 14,
			    'out_channels': 14,
			    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
			    'up_block_types': [],
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': [32, 64, 128, 256],
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'use_timestep_embedding': True,
			    'freq_shift': 1.0,
			    'flip_sin_to_cos': False,
			    'time_embedding_type': 'positional',
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(vf_loading_info['missing_keys']      )				,			0      )

			value_function.to(__lowerCamelCase      )
			__lowercase					=    value_function(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    value_function.config.in_channels
			__lowercase					=    14
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    value_function(__lowerCamelCase				,			__lowerCamelCase      ).sample

			# fmt: off
			__lowercase					=    torch.tensor([165.25] * seq_len      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )






	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			pass




