
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A   =     get_tests_dir("fixtures/test_sentencepiece_no_bos.model")





@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase					(	__snake_case					,      unittest.TestCase       ):
				_lowercase					=PegasusTokenizer
				_lowercase					=PegasusTokenizerFast
				_lowercase					=True
				_lowercase					=True




				def      __a						(					self     )				->      Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase_						=	PegasusTokenizer(_UpperCamelCase     )
							tokenizer.save_pretrained(self.tmpdirname     )




				@cached_property
				def      __a						(					self     )				->      str:
							return PegasusTokenizer.from_pretrained("google/pegasus-large"     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      PegasusTokenizer:
							return PegasusTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							return ("This is a test", "This is a test")




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	'''</s>'''
							lowerCAmelCase_						=	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase     )				,				_UpperCamelCase     )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase     )				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	list(self.get_tokenizer().get_vocab().keys()     )

							self.assertEqual(vocab_keys[0]				,				"<pad>"     )
							self.assertEqual(vocab_keys[1]				,				"</s>"     )
							self.assertEqual(vocab_keys[-1]				,				"v"     )
							self.assertEqual(len(_UpperCamelCase     )				,				1_103     )




				def      __a						(					self     )				->      Optional[int]:
							self.assertEqual(self.get_tokenizer().vocab_size				,				1_103     )




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase_						=	(
							    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
							    ''' </s> <pad> <pad> <pad>'''
							)
							lowerCAmelCase_						=	rust_tokenizer([raw_input_str]				,				return_tensors=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     ).input_ids[0]
							lowerCAmelCase_						=	py_tokenizer([raw_input_str]				,				return_tensors=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     ).input_ids[0]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							lowerCAmelCase_						=	'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
							lowerCAmelCase_						=	[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
							lowerCAmelCase_						=	tokenizer([raw_input_str]				,				return_tensors=_UpperCamelCase     ).input_ids[0]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 96_103
							assert tokenizer.pad_token_id == 0
							assert tokenizer.eos_token_id == 1
							assert tokenizer.offset == 103
							assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
							assert tokenizer.unk_token == "<unk>"
							assert tokenizer.model_max_length == 1_024
							lowerCAmelCase_						=	'''To ensure a smooth flow of bank resolutions.'''
							lowerCAmelCase_						=	[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
							lowerCAmelCase_						=	tokenizer([raw_input_str]				,				return_tensors=_UpperCamelCase     ).input_ids[0]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]     ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




				@require_torch
				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	['''This is going to be way too long.''' * 150, '''short example''']
							lowerCAmelCase_						=	['''not super long but more than 5 tokens''', '''tiny''']
							lowerCAmelCase_						=	self._large_tokenizer(_UpperCamelCase				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				return_tensors="pt"     )
							lowerCAmelCase_						=	self._large_tokenizer(
							    text_target=_UpperCamelCase				,				max_length=5				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				return_tensors="pt"     )

							assert batch.input_ids.shape == (2, 1_024)
							assert batch.attention_mask.shape == (2, 1_024)
							assert targets["input_ids"].shape == (2, 5)
							assert len(_UpperCamelCase     ) == 2  # input_ids, attention_mask.




				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	{'''input_ids''': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCamelCase				,				model_name="google/bigbird-pegasus-large-arxiv"				,				revision="ba85d0851d708441f91440d509690f1ab6353415"				,				)





@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase					(	__snake_case					,      unittest.TestCase       ):
				_lowercase					=PegasusTokenizer
				_lowercase					=PegasusTokenizerFast
				_lowercase					=True
				_lowercase					=True




				def      __a						(					self     )				->      Optional[int]:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase_						=	PegasusTokenizer(_UpperCamelCase				,				offset=0				,				mask_token_sent=_UpperCamelCase				,				mask_token="[MASK]"     )
							tokenizer.save_pretrained(self.tmpdirname     )




				@cached_property
				def      __a						(					self     )				->      Union[str, Any]:
							return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      PegasusTokenizer:
							return PegasusTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      Union[str, Any]:
							return ("This is a test", "This is a test")




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase_						=	(
							    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
							    ''' <pad> <pad> <pad>'''
							)
							lowerCAmelCase_						=	rust_tokenizer([raw_input_str]				,				return_tensors=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     ).input_ids[0]
							lowerCAmelCase_						=	py_tokenizer([raw_input_str]				,				return_tensors=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     ).input_ids[0]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				@require_torch
				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	['''This is going to be way too long.''' * 1_000, '''short example''']
							lowerCAmelCase_						=	['''not super long but more than 5 tokens''', '''tiny''']
							lowerCAmelCase_						=	self._large_tokenizer(_UpperCamelCase				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				return_tensors="pt"     )
							lowerCAmelCase_						=	self._large_tokenizer(
							    text_target=_UpperCamelCase				,				max_length=5				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase				,				return_tensors="pt"     )

							assert batch.input_ids.shape == (2, 4_096)
							assert batch.attention_mask.shape == (2, 4_096)
							assert targets["input_ids"].shape == (2, 5)
							assert len(_UpperCamelCase     ) == 2  # input_ids, attention_mask.




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	(
							    '''This is an example string that is used to test the original TF implementation against the HF'''
							    ''' implementation'''
							)

							lowerCAmelCase_						=	self._large_tokenizer(_UpperCamelCase     ).input_ids

							self.assertListEqual(
							    _UpperCamelCase				,				[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				,				)





'''simple docstring'''




import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Union[str, Any]					=						[]
         A       :   Union[str, Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Any					=						self.in_channels if i == 0 else self.out_channels

             A       :   Optional[Any]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   Optional[int]					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						resnets
         A       :   Union[str, Any]					=						attentions

         if self.add_downsample:
             A       :   int					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						()

         for resnet, attn in zip(self.resnets						,			self.attentions	):
             A       :   int					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[Any]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





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






         """simple docstring"""






         A       :   Optional[Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Optional[Any]					=						self.in_channels if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						resnets

         if self.add_downsample:
             A       :   Dict					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[Any]:






         """simple docstring"""






         A       :   str					=						()

         for resnet in self.resnets:
             A       :   Optional[int]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[int]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						[]
         A       :   Optional[int]					=						[]

         for i in range(self.num_layers	):
             A       :   str					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   Dict					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						resnets
         A       :   Optional[Any]					=						attentions

         if self.add_upsample:
             A       :   Optional[int]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[int]:






         """simple docstring"""






         for resnet, attn in zip(self.resnets						,			self.attentions	):
             # pop res hidden states
             A       :   List[str]					=						res_hidden_states_tuple[-1]
             A       :   int					=						res_hidden_states_tuple[:-1]
             A       :   List[str]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Tuple					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   Dict					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   int					=						[]

         for i in range(self.num_layers	):
             A       :   List[Any]					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   List[str]					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   str					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						resnets

         if self.add_upsample:
             A       :   Optional[Any]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Tuple:






         """simple docstring"""






         for resnet in self.resnets:
             # pop res hidden states
             A       :   Optional[int]					=						res_hidden_states_tuple[-1]
             A       :   Optional[Any]					=						res_hidden_states_tuple[:-1]
             A       :   List[Any]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Optional[Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   List[str]					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





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






         """simple docstring"""






         A       :   str					=						[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
         ]

         A       :   List[Any]					=						[]

         for _ in range(self.num_layers	):
             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.in_channels						,			n_heads=self.num_attention_heads						,			d_head=self.in_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

             A       :   Union[str, Any]					=						FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						resnets
         A       :   List[str]					=						attentions





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Dict:






         """simple docstring"""






         A       :   Optional[Any]					=						self.resnets[0](SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         for attn, resnet in zip(self.attentions						,			self.resnets[1:]	):
             A       :   Optional[int]					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         return hidden_states




