
import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer





class 		__lowerCamelCase     (   tf.keras.layers.Layer		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case = None	,				__snake_case = None	,				__snake_case = None	,				__snake_case = "longest"	,				__snake_case = True	,				__snake_case = 5_1_2	,				__snake_case = None	,				__snake_case = True	,				__snake_case = True	,				__snake_case = True	,				**__snake_case	,				)						->     List[Any]:






								"""simple docstring"""



								super().__init__()
								if use_fast_bert_tokenizer:
									UpperCAmelCase:					str 						=     FastBertTokenizer(
									    __snake_case	,				token_out_type=tf.intaa	,				lower_case_nfd_strip_accents=__snake_case	,				**__snake_case							)
								else:
									UpperCAmelCase:					Optional[Any] 						=     tf.lookup.StaticVocabularyTable(
									    tf.lookup.KeyValueTensorInitializer(
									        keys=__snake_case	,				key_dtype=tf.string	,				values=tf.range(tf.size(__snake_case	,				out_type=tf.intaa							)	,				dtype=tf.intaa							)	,				value_dtype=tf.intaa	,				)	,				num_oov_buckets=1	,				)
									UpperCAmelCase:					List[Any] 						=     BertTokenizerLayer(
									    __snake_case	,				token_out_type=tf.intaa	,				lower_case=__snake_case	,				**__snake_case							)

								UpperCAmelCase:					Any 						=     vocab_list
								UpperCAmelCase:					Tuple 						=     do_lower_case
								UpperCAmelCase:					Tuple 						=     cls_token_id or vocab_list.index("[CLS]"							)
								UpperCAmelCase:					Tuple 						=     sep_token_id or vocab_list.index("[SEP]"							)
								UpperCAmelCase:					Optional[Any] 						=     pad_token_id or vocab_list.index("[PAD]"							)
								UpperCAmelCase:					str 						=     ShrinkLongestTrimmer(max_length - 3	,				axis=1							)  # Allow room for special tokens
								UpperCAmelCase:					Tuple 						=     max_length
								UpperCAmelCase:					List[str] 						=     padding
								UpperCAmelCase:					Any 						=     truncation
								UpperCAmelCase:					Optional[int] 						=     pad_to_multiple_of
								UpperCAmelCase:					int 						=     return_token_type_ids
								UpperCAmelCase:					Any 						=     return_attention_mask



							@classmethod
							def     A__ (     cls	,				__snake_case	,				**__snake_case							)						->     Optional[int]:  # noqa: F821






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     kwargs.pop("do_lower_case"	,				__snake_case							)
								UpperCAmelCase:					List[Any] 						=     tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								UpperCAmelCase:					Dict 						=     kwargs.pop("cls_token_id"	,				__snake_case							)
								UpperCAmelCase:					List[str] 						=     tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								UpperCAmelCase:					List[str] 						=     kwargs.pop("sep_token_id"	,				__snake_case							)
								UpperCAmelCase:					List[Any] 						=     tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								UpperCAmelCase:					List[str] 						=     kwargs.pop("pad_token_id"	,				__snake_case							)
								UpperCAmelCase:					Any 						=     tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								UpperCAmelCase:					List[str] 						=     tokenizer.get_vocab()
								UpperCAmelCase:					Tuple 						=     sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]	,				key=lambda __snake_case							: x[1]							)
								UpperCAmelCase:					List[str] 						=     [entry[0] for entry in vocab]
								return cls(
								    vocab_list=__snake_case	,				do_lower_case=__snake_case	,				cls_token_id=__snake_case	,				sep_token_id=__snake_case	,				pad_token_id=__snake_case	,				**__snake_case	,				)



							@classmethod
							def     A__ (     cls	,				__snake_case	,				*__snake_case	,				**__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								try:
									UpperCAmelCase:					int 						=     BertTokenizer.from_pretrained(__snake_case	,				*__snake_case	,				**__snake_case							)
								except:  # noqa: E722
									from .tokenization_bert_fast import BertTokenizerFast

									UpperCAmelCase:					Optional[Any] 						=     BertTokenizerFast.from_pretrained(__snake_case	,				*__snake_case	,				**__snake_case							)
								return cls.from_tokenizer(__snake_case	,				**__snake_case							)



							def     A__ (     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								if self.do_lower_case:
									UpperCAmelCase:					Optional[Any] 						=     case_fold_utfa(__snake_case							)
								UpperCAmelCase:					Optional[int] 						=     self.tf_tokenizer.tokenize(__snake_case							)
								return tokens.merge_dims(1	,				-1							)



							def     A__ (     self	,				__snake_case	,				__snake_case=None	,				__snake_case=None	,				__snake_case=None	,				__snake_case=None	,				__snake_case=None	,				__snake_case=None	,				__snake_case=None	,				)						->     List[str]:






								"""simple docstring"""



								if padding is None:
									UpperCAmelCase:					Optional[Any] 						=     self.padding
								if padding not in ("longest", "max_length"):
									raise ValueError("Padding must be either 'longest' or 'max_length'!"							)
								if max_length is not None and text_pair is not None:
									# Because we have to instantiate a Trimmer to do it properly
									raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"							)
								if max_length is None:
									UpperCAmelCase:					str 						=     self.max_length
								if truncation is None:
									UpperCAmelCase:					Any 						=     self.truncation
								if pad_to_multiple_of is None:
									UpperCAmelCase:					List[str] 						=     self.pad_to_multiple_of
								if return_token_type_ids is None:
									UpperCAmelCase:					Dict 						=     self.return_token_type_ids
								if return_attention_mask is None:
									UpperCAmelCase:					Dict 						=     self.return_attention_mask
								if not isinstance(__snake_case	,				tf.Tensor							):
									UpperCAmelCase:					Union[str, Any] 						=     tf.convert_to_tensor(__snake_case							)
								if text_pair is not None and not isinstance(__snake_case	,				tf.Tensor							):
									UpperCAmelCase:					Optional[Any] 						=     tf.convert_to_tensor(__snake_case							)
								if text_pair is not None:
									if text.shape.rank > 1:
										raise ValueError("text argument should not be multidimensional when a text pair is supplied!"							)
									if text_pair.shape.rank > 1:
										raise ValueError("text_pair should not be multidimensional!"							)
								if text.shape.rank == 2:
									UpperCAmelCase						,   UpperCAmelCase:					Tuple 						=     text[:, 0], text[:, 1]
								UpperCAmelCase:					str 						=     self.unpaired_tokenize(__snake_case							)
								if text_pair is None:  # Unpaired text
									if truncation:
										UpperCAmelCase:					Any 						=     text[:, : max_length - 2]  # Allow room for special tokens
									UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     combine_segments(
									    (text,)	,				start_of_sequence_id=self.cls_token_id	,				end_of_segment_id=self.sep_token_id							)
								else:  # Paired text
									UpperCAmelCase:					List[str] 						=     self.unpaired_tokenize(__snake_case							)
									if truncation:
										UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.paired_trimmer.trim([text, text_pair]							)
									UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     combine_segments(
									    (text, text_pair)	,				start_of_sequence_id=self.cls_token_id	,				end_of_segment_id=self.sep_token_id							)
								if padding == "longest":
									UpperCAmelCase:					Union[str, Any] 						=     input_ids.bounding_shape(axis=1							)
									if pad_to_multiple_of is not None:
										# No ceiling division in tensorflow, so we negate floordiv instead
										UpperCAmelCase:					Union[str, Any] 						=     pad_to_multiple_of * (-tf.math.floordiv(-pad_length	,				__snake_case							))
								else:
									UpperCAmelCase:					Any 						=     max_length

								UpperCAmelCase						,   UpperCAmelCase:					str 						=     pad_model_inputs(__snake_case	,				max_seq_length=__snake_case	,				pad_value=self.pad_token_id							)
								UpperCAmelCase:					Any 						=     {"input_ids": input_ids}
								if return_attention_mask:
									UpperCAmelCase:					List[Any] 						=     attention_mask
								if return_token_type_ids:
									UpperCAmelCase						,   UpperCAmelCase:					int 						=     pad_model_inputs(
									    __snake_case	,				max_seq_length=__snake_case	,				pad_value=self.pad_token_id							)
									UpperCAmelCase:					str 						=     token_type_ids
								return output




							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								return {
								    "vocab_list": self.vocab_list,
								    "do_lower_case": self.do_lower_case,
								    "cls_token_id": self.cls_token_id,
								    "sep_token_id": self.sep_token_id,
								    "pad_token_id": self.pad_token_id,
								}


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 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							UNetaDModel
							lowerCamelCase__:				Any      =							'''sample'''



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     4
								UpperCAmelCase:					Optional[Any] 						=     1_4
								UpperCAmelCase:					str 						=     1_6

								UpperCAmelCase:					Optional[int] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

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



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return (4, 1_4, 1_6)



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






								"""simple docstring"""



								return (4, 1_4, 1_6)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								pass



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






								"""simple docstring"""



								pass



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






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								super().test_outputs_equivalence()



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






								"""simple docstring"""



								super().test_from_save_pretrained()



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






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



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






								"""simple docstring"""



								super().test_model_from_pretrained()



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






								"""simple docstring"""



								super().test_output()



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "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",
								}
								UpperCAmelCase:					Tuple 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="unet"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(loading_info["missing_keys"]							)	,				0							)

								model.to(__snake_case							)
								UpperCAmelCase:					Tuple 						=     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     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     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							)

								UpperCAmelCase:					int 						=     model.config.in_channels
								UpperCAmelCase:					List[str] 						=     1_6
								UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					List[str] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(__snake_case	,				__snake_case							).sample.permute(0	,				2	,				1							)

								UpperCAmelCase:					str 						=     output[0, -3:, -3:].flatten()
								# fmt: off
								UpperCAmelCase:					Dict 						=     torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     "harmonai/maestro-150k"
								UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(__snake_case	,				subfolder="unet"							)
								model.to(__snake_case							)

								UpperCAmelCase:					Any 						=     6_5_5_3_6
								UpperCAmelCase:					str 						=     torch.sin(torch.arange(__snake_case							)[None, None, :].repeat(1	,				2	,				1							)							).to(__snake_case							)
								UpperCAmelCase:					Dict 						=     torch.tensor([1]							).to(__snake_case							)

								with torch.no_grad():
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				__snake_case							).sample

								UpperCAmelCase:					List[Any] 						=     output.abs().sum()
								UpperCAmelCase:					Dict 						=     output.abs().max()

								assert (output_sum - 2_24.08_96).abs() < 0.5
								assert (output_max - 0.06_07).abs() < 4e-4





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							UNetaDModel
							lowerCamelCase__:				int      =							'''sample'''



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     4
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					Optional[int] 						=     1_6

								UpperCAmelCase:					List[str] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

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



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






								"""simple docstring"""



								return (4, 1_4, 1_6)



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






								"""simple docstring"""



								return (4, 1_4, 1)



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






								"""simple docstring"""



								super().test_determinism()



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






								"""simple docstring"""



								super().test_outputs_equivalence()



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






								"""simple docstring"""



								super().test_from_save_pretrained()



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






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



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






								"""simple docstring"""



								super().test_model_from_pretrained()



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






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase:					Tuple 						=     self.model_class(**__snake_case							)
								model.to(__snake_case							)
								model.eval()

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(**__snake_case							)

									if isinstance(__snake_case	,				__snake_case							):
										UpperCAmelCase:					List[str] 						=     output.sample

								self.assertIsNotNone(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     torch.Size((inputs_dict["sample"].shape[0], 1)							)
								self.assertEqual(output.shape	,				__snake_case	,				"Input and output shapes do not match"							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
								    "up_block_types": [],
								    "out_block_type": "ValueFunction",
								    "mid_block_type": "ValueFunctionMidBlock1D",
								    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
								    "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",
								}
								UpperCAmelCase:					Union[str, Any] 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(vf_loading_info["missing_keys"]							)	,				0							)

								value_function.to(__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     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     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					str 						=     value_function.config.in_channels
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					int 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					Union[str, Any] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Dict 						=     value_function(__snake_case	,				__snake_case							).sample

								# fmt: off
								UpperCAmelCase:					Tuple 						=     torch.tensor([1_65.25] * seq_len							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)




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






								"""simple docstring"""



								pass
