



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


SCREAMING_SNAKE_CASE_:List[Any]             =						get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")






@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[Any]					=      PegasusTokenizer
    __lowerCamelCase			: List[Any]					=      PegasusTokenizerFast
    __lowerCamelCase			: List[str]					=      True
    __lowerCamelCase			: Optional[Any]					=      True






    def 			_lowerCAmelCase					(       self		):
        super().setUp()

        # We have a SentencePiece fixture for testing
        A :       Optional[int]          =   PegasusTokenizer(lowerCamelCase__		)
        tokenizer.save_pretrained(self.tmpdirname		)






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






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        return PegasusTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return ("This is a test", "This is a test")






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   """</s>"""
        A :       str          =   1

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






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   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(lowerCamelCase__		),		1103		)






    def 			_lowerCAmelCase					(       self		):
        self.assertEqual(self.get_tokenizer().vocab_size,		1103		)






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
        A :       Optional[Any]          =   self.tokenizer_class.from_pretrained(self.tmpdirname		)
        A :       str          =   (
            """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>"""
        )
        A :       Dict          =   rust_tokenizer([raw_input_str],		return_tensors=lowerCamelCase__,		add_special_tokens=lowerCamelCase__		).input_ids[0]
        A :       Tuple          =   py_tokenizer([raw_input_str],		return_tensors=lowerCamelCase__,		add_special_tokens=lowerCamelCase__		).input_ids[0]
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        A :       List[Any]          =   """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
        A :       Optional[int]          =   [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
        A :       List[Any]          =   tokenizer([raw_input_str],		return_tensors=lowerCamelCase__		).input_ids[0]
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 9_6103
        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 == 1024
        A :       Dict          =   """To ensure a smooth flow of bank resolutions."""
        A :       Tuple          =   [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
        A :       Any          =   tokenizer([raw_input_str],		return_tensors=lowerCamelCase__		).input_ids[0]
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






    @require_torch
    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   ["""This is going to be way too long.""" * 150, """short example"""]
        A :       Dict          =   ["""not super long but more than 5 tokens""", """tiny"""]
        A :       Any          =   self._large_tokenizer(lowerCamelCase__,		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		return_tensors="""pt"""		)
        A :       List[Any]          =   self._large_tokenizer(
            text_target=lowerCamelCase__,		max_length=5,		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		return_tensors="""pt"""		)

        assert batch.input_ids.shape == (2, 1024)
        assert batch.attention_mask.shape == (2, 1024)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCamelCase__		) == 2  # input_ids, attention_mask.






    @slow
    def 			_lowerCAmelCase					(       self		):
        # fmt: off
        A :       Optional[int]          =   {"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 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, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 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=lowerCamelCase__,		model_name="""google/bigbird-pegasus-large-arxiv""",		revision="""ba85d0851d708441f91440d509690f1ab6353415""",		)






@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      PegasusTokenizer
    __lowerCamelCase			: Optional[int]					=      PegasusTokenizerFast
    __lowerCamelCase			: str					=      True
    __lowerCamelCase			: Optional[int]					=      True






    def 			_lowerCAmelCase					(       self		):
        super().setUp()

        # We have a SentencePiece fixture for testing
        A :       List[Any]          =   PegasusTokenizer(lowerCamelCase__,		offset=0,		mask_token_sent=lowerCamelCase__,		mask_token="""[MASK]"""		)
        tokenizer.save_pretrained(self.tmpdirname		)






    @cached_property
    def 			_lowerCAmelCase					(       self		):
        return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""		)






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        return PegasusTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return ("This is a test", "This is a test")






    def 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
        A :       Any          =   self.tokenizer_class.from_pretrained(self.tmpdirname		)
        A :       str          =   (
            """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
            """ <pad> <pad> <pad>"""
        )
        A :       Optional[int]          =   rust_tokenizer([raw_input_str],		return_tensors=lowerCamelCase__,		add_special_tokens=lowerCamelCase__		).input_ids[0]
        A :       str          =   py_tokenizer([raw_input_str],		return_tensors=lowerCamelCase__,		add_special_tokens=lowerCamelCase__		).input_ids[0]
        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)






    @require_torch
    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   ["""This is going to be way too long.""" * 1000, """short example"""]
        A :       int          =   ["""not super long but more than 5 tokens""", """tiny"""]
        A :       str          =   self._large_tokenizer(lowerCamelCase__,		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		return_tensors="""pt"""		)
        A :       str          =   self._large_tokenizer(
            text_target=lowerCamelCase__,		max_length=5,		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		return_tensors="""pt"""		)

        assert batch.input_ids.shape == (2, 4096)
        assert batch.attention_mask.shape == (2, 4096)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCamelCase__		) == 2  # input_ids, attention_mask.






    def 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   (
            """This is an example string that is used to test the original TF implementation against the HF"""
            """ implementation"""
        )

        A :       Tuple          =   self._large_tokenizer(lowerCamelCase__		).input_ids

        self.assertListEqual(
            lowerCamelCase__,		[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1],		)







from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings






@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = "layer_norm",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       Dict          =   only_cross_attention

        A :       Tuple          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
        A :       Union[str, Any]          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
            raise ValueError(
                f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''		)

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if self.use_ada_layer_norm:
            A :       Dict          =   AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A :       List[str]          =   AdaLayerNormZero(lowerCamelCase__,		lowerCamelCase__		)
        else:
            A :       Tuple          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       Any          =   Attention(
            query_dim=lowerCamelCase__,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if only_cross_attention else None,		upcast_attention=lowerCamelCase__,		)

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            A :       int          =   (
                AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
                if self.use_ada_layer_norm
                else nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
            )
            A :       Dict          =   Attention(
                query_dim=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if not double_self_attention else None,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		upcast_attention=lowerCamelCase__,		)  # is self-attn if encoder_hidden_states is none
        else:
            A :       Dict          =   None
            A :       Dict          =   None

        # 3. Feed-forward
        A :       Optional[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       int          =   FeedForward(lowerCamelCase__,		dropout=lowerCamelCase__,		activation_fn=lowerCamelCase__,		final_dropout=lowerCamelCase__		)

        # let chunk size default to None
        A :       Optional[Any]          =   None
        A :       int          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        # Sets chunk feed-forward
        A :       List[str]          =   chunk_size
        A :       int          =   dim






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 1. Self-Attention
        if self.use_ada_layer_norm:
            A :       Optional[int]          =   self.norma(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A     ,							A     ,							A     ,							A     ,							A :       Tuple          =   self.norma(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=hidden_states.dtype		)
        else:
            A :       Tuple          =   self.norma(lowerCamelCase__		)

        A :       Dict          =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

        A :       str          =   self.attna(
            lowerCamelCase__,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        if self.use_ada_layer_norm_zero:
            A :       List[Any]          =   gate_msa.unsqueeze(1		) * attn_output
        A :       str          =   attn_output + hidden_states

        # 2. Cross-Attention
        if self.attna is not None:
            A :       Optional[int]          =   (
                self.norma(lowerCamelCase__,		lowerCamelCase__		) if self.use_ada_layer_norm else self.norma(lowerCamelCase__		)
            )

            A :       Optional[Any]          =   self.attna(
                lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
            A :       Dict          =   attn_output + hidden_states

        # 3. Feed-forward
        A :       str          =   self.norma(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Tuple          =   norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                raise ValueError(
                    f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''		)

            A :       Any          =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            A :       Optional[Any]          =   torch.cat(
                [self.ff(lowerCamelCase__		) for hid_slice in norm_hidden_states.chunk(lowerCamelCase__,		dim=self._chunk_dim		)],		dim=self._chunk_dim,		)
        else:
            A :       Dict          =   self.ff(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Optional[Any]          =   gate_mlp.unsqueeze(1		) * ff_output

        A :       Optional[int]          =   ff_output + hidden_states

        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 4,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       str          =   int(dim * mult		)
        A :       Optional[Any]          =   dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
            A :       Dict          =   GELU(lowerCamelCase__,		lowerCamelCase__		)
        if activation_fn == "gelu-approximate":
            A :       Optional[int]          =   GELU(lowerCamelCase__,		lowerCamelCase__,		approximate="""tanh"""		)
        elif activation_fn == "geglu":
            A :       Any          =   GEGLU(lowerCamelCase__,		lowerCamelCase__		)
        elif activation_fn == "geglu-approximate":
            A :       Optional[Any]          =   ApproximateGELU(lowerCamelCase__,		lowerCamelCase__		)

        A :       Dict          =   nn.ModuleList([]		)
        # project in
        self.net.append(lowerCamelCase__		)
        # project dropout
        self.net.append(nn.Dropout(lowerCamelCase__		)		)
        # project out
        self.net.append(nn.Linear(lowerCamelCase__,		lowerCamelCase__		)		)
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
            self.net.append(nn.Dropout(lowerCamelCase__		)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        for module in self.net:
            A :       int          =   module(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = "none"		):
        super().__init__()
        A :       Optional[int]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)
        A :       int          =   approximate






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__,		approximate=self.approximate		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		),		approximate=self.approximate		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        A :       Union[str, Any]          =   self.gelu(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		dim_out * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A     ,							A :       Any          =   self.proj(lowerCamelCase__		).chunk(2,		dim=-1		)
        return hidden_states * self.gelu(lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        return x * torch.sigmoid(1.702 * x		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Dict          =   nn.Embedding(lowerCamelCase__,		lowerCamelCase__		)
        A :       Tuple          =   nn.SiLU()
        A :       Tuple          =   nn.Linear(lowerCamelCase__,		embedding_dim * 2		)
        A :       List[str]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        A :       int          =   self.linear(self.silu(self.emb(lowerCamelCase__		)		)		)
        A     ,							A :       Optional[int]          =   torch.chunk(lowerCamelCase__,		2		)
        A :       Tuple          =   self.norm(lowerCamelCase__		) * (1 + scale) + shift
        return x






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()

        A :       Union[str, Any]          =   CombinedTimestepLabelEmbeddings(lowerCamelCase__,		lowerCamelCase__		)

        A :       List[Any]          =   nn.SiLU()
        A :       str          =   nn.Linear(lowerCamelCase__,		6 * embedding_dim,		bias=lowerCamelCase__		)
        A :       List[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__,		eps=1e-6		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A :       Tuple          =   self.linear(self.silu(self.emb(lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=lowerCamelCase__		)		)		)
        A     ,							A     ,							A     ,							A     ,							A     ,							A :       Optional[Any]          =   emb.chunk(6,		dim=1		)
        A :       List[str]          =   self.norm(lowerCamelCase__		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 1e-5		):
        super().__init__()
        A :       int          =   num_groups
        A :       Dict          =   eps

        if act_fn is None:
            A :       Union[str, Any]          =   None
        else:
            A :       Optional[int]          =   get_activation(lowerCamelCase__		)

        A :       Dict          =   nn.Linear(lowerCamelCase__,		out_dim * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self.act:
            A :       Dict          =   self.act(lowerCamelCase__		)
        A :       Optional[int]          =   self.linear(lowerCamelCase__		)
        A :       int          =   emb[:, :, None, None]
        A     ,							A :       Tuple          =   emb.chunk(2,		dim=1		)

        A :       Any          =   F.group_norm(lowerCamelCase__,		self.num_groups,		eps=self.eps		)
        A :       List[str]          =   x * (1 + scale) + shift
        return x


