






'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					RobertaTokenizer
			snake_case__	=					RobertaTokenizerFast
			snake_case__	=					True
			snake_case__	=					{"cls_token": "<s>"}






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							super().setUp()

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    **__SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Union[str, Any]:
							kwargs.update(self.special_tokens_map       )
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Dict       )      ->      List[Any]:
							kwargs.update(self.special_tokens_map       )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      List[Any]:
							a_		:	List[str]									=   '''lower newer'''
							a_		:	Optional[Any]									=   '''lower newer'''
							return input_text, output_text






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      int:
							a_		:	int									=   self.tokenizer_class(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map       )
							a_		:	str									=   '''lower newer'''
							a_		:	Optional[int]									=   ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							a_		:	Optional[int]									=   tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )  # , add_prefix_space=True)
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   tokens + [tokenizer.unk_token]
							a_		:	Optional[int]									=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Optional[int]:
							a_		:	int									=   self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('''Hello world!'''	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )	,    [0, 3_1414, 232, 328, 2]       )
							self.assertListEqual(
							    tokenizer.encode('''Hello world! cécé herlolip 418'''	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )	,    [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]	,    )






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	int									=   self.tokenizer_class.from_pretrained('''roberta-base'''       )

							a_		:	Optional[int]									=   tokenizer.encode('''sequence builders'''	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.encode('''multi-sequence build'''	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )

							a_		:	List[str]									=   tokenizer.encode(
							    '''sequence builders'''	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   tokenizer.encode(
							    '''sequence builders'''	,    '''multi-sequence build'''	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE       )
							a_		:	List[Any]									=   tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Any:
							a_		:	int									=   self.get_tokenizer()

							a_		:	List[Any]									=   '''Encode this sequence.'''
							a_		:	Optional[int]									=   tokenizer.byte_encoder[''' '''.encode('''utf-8'''       )[0]]

							# Testing encoder arguments
							a_		:	str									=   tokenizer.encode(__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							a_		:	str									=   tokenizer.encode(__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
							self.assertEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}       )
							a_		:	int									=   tokenizer.encode(__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							# Testing spaces after special tokens
							a_		:	Optional[int]									=   '''<mask>'''
							tokenizer.add_special_tokens(
							    {'''mask_token''': AddedToken(__SCREAMING_SNAKE_CASE	,    lstrip=__SCREAMING_SNAKE_CASE	,    rstrip=__SCREAMING_SNAKE_CASE       )}       )  # mask token has a left space
							a_		:	List[Any]									=   tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''Encode <mask> sequence'''
							a_		:	Optional[int]									=   '''Encode <mask>sequence'''

							a_		:	Union[str, Any]									=   tokenizer.encode(__SCREAMING_SNAKE_CASE       )
							a_		:	str									=   encoded.index(__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
							self.assertEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   tokenizer.encode(__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   encoded.index(__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Optional[int]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
															a_		:	Tuple									=   self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[Any]									=   self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
															a_		:	Union[str, Any]									=   '''A, <mask> AllenNLP sentence.'''
															a_		:	Optional[int]									=   tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    return_token_type_ids=__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[Any]									=   tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    return_token_type_ids=__SCREAMING_SNAKE_CASE       )

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r['''token_type_ids''']       )	,    sum(tokens_p['''token_type_ids''']       )       )

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r['''attention_mask''']       ) / len(tokens_r['''attention_mask''']       )	,    sum(tokens_p['''attention_mask''']       ) / len(tokens_p['''attention_mask''']       )	,    )

															a_		:	List[Any]									=   tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
															a_		:	Optional[Any]									=   tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']       )

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p['''input_ids''']	,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )
															self.assertSequenceEqual(tokens_r['''input_ids''']	,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )

															self.assertSequenceEqual(
															    __SCREAMING_SNAKE_CASE	,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )
															self.assertSequenceEqual(
															    __SCREAMING_SNAKE_CASE	,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Union[str, Any]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]	,    repeat=2       ):
											a_		:	int									=   self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )

											a_		:	List[str]									=   json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
											a_		:	Any									=   json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

											self.assertEqual(pre_tokenizer_state['''add_prefix_space''']	,    __SCREAMING_SNAKE_CASE       )

											self.assertEqual(post_processor_state['''add_prefix_space''']	,    __SCREAMING_SNAKE_CASE       )
											self.assertEqual(post_processor_state['''trim_offsets''']	,    __SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Union[str, Any]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
															a_		:	Any									=   '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
															a_		:	Optional[int]									=   f'{text_of_1_token} {text_of_1_token}'

															a_		:	List[str]									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	Dict									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (0, len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (len(__SCREAMING_SNAKE_CASE       ) + 1, len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )

															a_		:	str									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[int]									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (0, len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (len(__SCREAMING_SNAKE_CASE       ) + 1, len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )

															a_		:	List[Any]									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[Any]									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (0, len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (len(__SCREAMING_SNAKE_CASE       ), len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )

															a_		:	Optional[int]									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	int									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (0, len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (len(__SCREAMING_SNAKE_CASE       ), len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )

															a_		:	Union[str, Any]									=   f' {text}'

															# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
															#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
															# )
															# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
															# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
															# self.assertEqual(
															#     encoding.offset_mapping[1],
															#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
															# )

															a_		:	Any									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[int]									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (1, 1 + len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (1 + len(__SCREAMING_SNAKE_CASE       ) + 1, 1 + len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )

															a_		:	Tuple									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[int]									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (1 + len(__SCREAMING_SNAKE_CASE       ), 1 + len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )

															a_		:	int									=   self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE	,    use_fast=__SCREAMING_SNAKE_CASE	,    add_prefix_space=__SCREAMING_SNAKE_CASE	,    trim_offsets=__SCREAMING_SNAKE_CASE       )
															a_		:	Optional[Any]									=   tokenizer_r(__SCREAMING_SNAKE_CASE	,    return_offsets_mapping=__SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE       )
															self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(__SCREAMING_SNAKE_CASE       ))       )
															self.assertEqual(
															    encoding.offset_mapping[1]	,    (1 + len(__SCREAMING_SNAKE_CASE       ), 1 + len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))	,    )












'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase									=   {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 		_UpperCAmelCase (				__A				: Tuple   ):
				if isinstance(__A    ,		__A   ):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('''boolean value expected'''   )





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Tuple    ,		__A				: Optional[int]    ,		__A				: int    ,		__A				: Optional[Any]=False   ):
				a_		:	str									=   checkpoint[f'{old_prefix}.in_layers.0.weight']
				a_		:	Dict									=   checkpoint[f'{old_prefix}.in_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.in_layers.2.weight']
				a_		:	Any									=   checkpoint[f'{old_prefix}.in_layers.2.bias']
				a_		:	Any									=   checkpoint[f'{old_prefix}.emb_layers.1.weight']
				a_		:	int									=   checkpoint[f'{old_prefix}.emb_layers.1.bias']
				a_		:	Union[str, Any]									=   checkpoint[f'{old_prefix}.out_layers.0.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.weight']
				a_		:	List[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.bias']

				if has_skip:
								a_		:	int									=   checkpoint[f'{old_prefix}.skip_connection.weight']
								a_		:	List[Any]									=   checkpoint[f'{old_prefix}.skip_connection.bias']

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]    ,		__A				: List[str]=None   ):
				a_					,      a_					,      a_		:	Any									=   checkpoint[f'{old_prefix}.qkv.weight'].chunk(3    ,		dim=0   )
				a_					,      a_					,      a_		:	List[Any]									=   checkpoint[f'{old_prefix}.qkv.bias'].chunk(3    ,		dim=0   )

				a_		:	List[str]									=   checkpoint[f'{old_prefix}.norm.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.norm.bias']

				a_		:	Any									=   weight_q.squeeze(-1   ).squeeze(-1   )
				a_		:	int									=   bias_q.squeeze(-1   ).squeeze(-1   )
				a_		:	Any									=   weight_k.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[int]									=   bias_k.squeeze(-1   ).squeeze(-1   )
				a_		:	str									=   weight_v.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[Any]									=   bias_v.squeeze(-1   ).squeeze(-1   )

				a_		:	Union[str, Any]									=   (
				    checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1   ).squeeze(-1   )
				)
				a_		:	Any									=   checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1   ).squeeze(-1   )

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str   ):
				a_		:	int									=   torch.load(__A    ,		map_location='''cpu'''   )
				a_		:	Union[str, Any]									=   {}

				a_		:	int									=   checkpoint['''time_embed.0.weight''']
				a_		:	Tuple									=   checkpoint['''time_embed.0.bias''']
				a_		:	List[Any]									=   checkpoint['''time_embed.2.weight''']
				a_		:	Union[str, Any]									=   checkpoint['''time_embed.2.bias''']

				if unet_config["num_class_embeds"] is not None:
								a_		:	List[Any]									=   checkpoint['''label_emb.weight''']

				a_		:	Optional[int]									=   checkpoint['''input_blocks.0.0.weight''']
				a_		:	int									=   checkpoint['''input_blocks.0.0.bias''']

				a_		:	Optional[int]									=   unet_config['''down_block_types''']
				a_		:	Union[str, Any]									=   unet_config['''layers_per_block''']
				a_		:	Optional[Any]									=   unet_config['''attention_head_dim''']
				a_		:	str									=   unet_config['''block_out_channels''']
				a_		:	Any									=   1
				a_		:	Union[str, Any]									=   channels_list[0]

				for i, layer_type in enumerate(__A   ):
								a_		:	Any									=   channels_list[i]
								a_		:	Tuple									=   current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	int									=   f'input_blocks.{current_layer}.0'
																a_		:	List[Any]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	List[str]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
																a_		:	Optional[int]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	Any									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	Optional[int]									=   f'down_blocks.{i}.attentions.{j}'
																a_		:	List[Any]									=   f'input_blocks.{current_layer}.1'
																a_		:	int									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

								if i != len(__A   ) - 1:
												a_		:	Dict									=   f'down_blocks.{i}.downsamplers.0'
												a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
												a_		:	Union[str, Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
												current_layer += 1

								a_		:	Any									=   current_channels

				# hardcoded the mid-block for now
				a_		:	Tuple									=   '''mid_block.resnets.0'''
				a_		:	int									=   '''middle_block.0'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
				a_		:	List[Any]									=   '''mid_block.attentions.0'''
				a_		:	Optional[int]									=   '''middle_block.1'''
				a_		:	Optional[int]									=   convert_attention(__A    ,		__A    ,		__A    ,		__A    ,		__A   )
				a_		:	Any									=   '''mid_block.resnets.1'''
				a_		:	Tuple									=   '''middle_block.2'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	str									=   0
				a_		:	Optional[int]									=   unet_config['''up_block_types''']

				for i, layer_type in enumerate(__A   ):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Tuple									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'output_blocks.{current_layer}.0'
																a_		:	Optional[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	int									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Union[str, Any]									=   f'output_blocks.{current_layer-1}.1'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Optional[Any]									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	str									=   f'output_blocks.{current_layer}.0'
																a_		:	List[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	List[Any]									=   f'up_blocks.{i}.attentions.{j}'
																a_		:	List[str]									=   f'output_blocks.{current_layer}.1'
																a_		:	str									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	Dict									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Any									=   f'output_blocks.{current_layer-1}.2'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	Dict									=   checkpoint['''out.0.weight''']
				a_		:	int									=   checkpoint['''out.0.bias''']
				a_		:	int									=   checkpoint['''out.2.weight''']
				a_		:	str									=   checkpoint['''out.2.bias''']

				return new_checkpoint


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()

		parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
		parser.add_argument(
		    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
		)
		parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

		__lowerCAmelCase									=   parser.parse_args()
		__lowerCAmelCase									=   strabool(args.class_cond)

		__lowerCAmelCase									=   os.path.basename(args.unet_path)
		print(F"""Checkpoint: {ckpt_name}""")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				__lowerCAmelCase									=   TEST_UNET_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		if not args.class_cond:
				__lowerCAmelCase									=   None

		__lowerCAmelCase									=   con_pt_to_diffuser(args.unet_path, unet_config)

		__lowerCAmelCase									=   UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__lowerCAmelCase									=   CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		__lowerCAmelCase									=   CMStochasticIterativeScheduler(**scheduler_config)

		__lowerCAmelCase									=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)




