
import argparse
import os
import re


_A     =  "src/diffusers"

# Pattern that looks at the indentation in a line.
_A     =  re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_A     =  re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_A     =  re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
_A     =  re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_A     =  re.compile(R"\[([^\]]+)\]")






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			_re_indent.search(__lowerCAmelCase       )
			return "" if search is None else search.groups()[0]






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Union[str, Any]=""					,							__lowerCAmelCase						:						List[str]=None					,							__lowerCAmelCase						:						Any=None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			0
			lowerCAmelCase_      			=			code.split("\n"       )
			if start_prompt is not None:
						while not lines[index].startswith(__lowerCAmelCase       ):
									index += 1
						lowerCAmelCase_      			=			["\n".join(lines[:index]       )]
			else:
						lowerCAmelCase_      			=			[]

			# We split into blocks until we get to the `end_prompt` (or the end of the block).
			lowerCAmelCase_      			=			[lines[index]]
			index += 1
			while index < len(__lowerCAmelCase       ) and (end_prompt is None or not lines[index].startswith(__lowerCAmelCase       )):
						if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
									if len(__lowerCAmelCase       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + " "       ):
												current_block.append(lines[index]       )
												blocks.append("\n".join(__lowerCAmelCase       )       )
												if index < len(__lowerCAmelCase       ) - 1:
															lowerCAmelCase_      			=			[lines[index + 1]]
															index += 1
												else:
															lowerCAmelCase_      			=			[]
									else:
												blocks.append("\n".join(__lowerCAmelCase       )       )
												lowerCAmelCase_      			=			[lines[index]]
						else:
									current_block.append(lines[index]       )
						index += 1

			# Adds current block if it's nonempty.
			if len(__lowerCAmelCase       ) > 0:
						blocks.append("\n".join(__lowerCAmelCase       )       )

			# Add final block after end_prompt if provided.
			if end_prompt is not None and index < len(__lowerCAmelCase       ):
						blocks.append("\n".join(lines[index:]       )       )

			return blocks






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			def _inner(__lowerCAmelCase						:						List[str]       ):
						return key(__lowerCAmelCase       ).lower().replace("_"					,							""       )

			return _inner






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[Any]=None       ):




			"""simple docstring"""


			def noop(__lowerCAmelCase						:						str       ):
						return x

			if key is None:
						lowerCAmelCase_      			=			noop
			# Constants are all uppercase, they go first.
			lowerCAmelCase_      			=			[obj for obj in objects if key(__lowerCAmelCase       ).isupper()]
			# Classes are not all uppercase but start with a capital, they go second.
			lowerCAmelCase_      			=			[obj for obj in objects if key(__lowerCAmelCase       )[0].isupper() and not key(__lowerCAmelCase       ).isupper()]
			# Functions begin with a lowercase, they go last.
			lowerCAmelCase_      			=			[obj for obj in objects if not key(__lowerCAmelCase       )[0].isupper()]

			lowerCAmelCase_      			=			ignore_underscore(__lowerCAmelCase       )
			return sorted(__lowerCAmelCase					,							key=__lowerCAmelCase       ) + sorted(__lowerCAmelCase					,							key=__lowerCAmelCase       ) + sorted(__lowerCAmelCase					,							key=__lowerCAmelCase       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			def _replace(__lowerCAmelCase						:						Dict       ):
						lowerCAmelCase_      			=			match.groups()[0]
						if "," not in imports:
									return F"""[{imports}]"""
						lowerCAmelCase_      			=			[part.strip().replace("\""					,							""       ) for part in imports.split(","       )]
						# We will have a final empty element if the line finished with a comma.
						if len(keys[-1]       ) == 0:
									lowerCAmelCase_      			=			keys[:-1]
						return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__lowerCAmelCase       )]       ) + "]"

			lowerCAmelCase_      			=			import_statement.split("\n"       )
			if len(__lowerCAmelCase       ) > 3:
						# Here we have to sort internal imports that are on several lines (one per name):
						# key: [
						#     "object1",
						#     "object2",
						#     ...
						# ]

						# We may have to ignore one or two lines on each side.
						lowerCAmelCase_      			=			2 if lines[1].strip() == "[" else 1
						lowerCAmelCase_      			=			[(i, _re_strip_line.search(__lowerCAmelCase       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
						lowerCAmelCase_      			=			sort_objects(__lowerCAmelCase					,							key=lambda __lowerCAmelCase       : x[1]       )
						lowerCAmelCase_      			=			[lines[x[0] + idx] for x in sorted_indices]
						return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
			elif len(__lowerCAmelCase       ) == 3:
						# Here we have to sort internal imports that are on one separate line:
						# key: [
						#     "object1", "object2", ...
						# ]
						if _re_bracket_content.search(lines[1]       ) is not None:
									lowerCAmelCase_      			=			_re_bracket_content.sub(_replace					,							lines[1]       )
						else:
									lowerCAmelCase_      			=			[part.strip().replace("\""					,							""       ) for part in lines[1].split(","       )]
									# We will have a final empty element if the line finished with a comma.
									if len(keys[-1]       ) == 0:
												lowerCAmelCase_      			=			keys[:-1]
									lowerCAmelCase_      			=			get_indent(lines[1]       ) + ", ".join([F"""\"{k}\"""" for k in sort_objects(__lowerCAmelCase       )]       )
						return "\n".join(__lowerCAmelCase       )
			else:
						# Finally we have to deal with imports fitting on one line
						lowerCAmelCase_      			=			_re_bracket_content.sub(_replace					,							__lowerCAmelCase       )
						return import_statement






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Optional[Any]=True       ):




			"""simple docstring"""


			with open(__lowerCAmelCase					,							"r"       ) as f:
						lowerCAmelCase_      			=			f.read()

			if "_import_structure" not in code:
						return

			# Blocks of indent level 0
			lowerCAmelCase_      			=			split_code_in_indented_blocks(
			    __lowerCAmelCase					,							start_prompt="_import_structure = {"					,							end_prompt="if TYPE_CHECKING:"       )

			# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
			for block_idx in range(1					,							len(__lowerCAmelCase       ) - 1       ):
						# Check if the block contains some `_import_structure`s thingy to sort.
						lowerCAmelCase_      			=			main_blocks[block_idx]
						lowerCAmelCase_      			=			block.split("\n"       )

						# Get to the start of the imports.
						lowerCAmelCase_      			=			0
						while line_idx < len(__lowerCAmelCase       ) and "_import_structure" not in block_lines[line_idx]:
									# Skip dummy import blocks
									if "import dummy" in block_lines[line_idx]:
												lowerCAmelCase_      			=			len(__lowerCAmelCase       )
									else:
												line_idx += 1
						if line_idx >= len(__lowerCAmelCase       ):
									continue

						# Ignore beginning and last line: they don't contain anything.
						lowerCAmelCase_      			=			"\n".join(block_lines[line_idx:-1]       )
						lowerCAmelCase_      			=			get_indent(block_lines[1]       )
						# Slit the internal block into blocks of indent level 1.
						lowerCAmelCase_      			=			split_code_in_indented_blocks(__lowerCAmelCase					,							indent_level=__lowerCAmelCase       )
						# We have two categories of import key: list or _import_structure[key].append/extend
						lowerCAmelCase_      			=			_re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
						# Grab the keys, but there is a trap: some lines are empty or just comments.
						lowerCAmelCase_      			=			[(pattern.search(__lowerCAmelCase       ).groups()[0] if pattern.search(__lowerCAmelCase       ) is not None else None) for b in internal_blocks]
						# We only sort the lines with a key.
						lowerCAmelCase_      			=			[(i, key) for i, key in enumerate(__lowerCAmelCase       ) if key is not None]
						lowerCAmelCase_      			=			[x[0] for x in sorted(__lowerCAmelCase					,							key=lambda __lowerCAmelCase       : x[1]       )]

						# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
						lowerCAmelCase_      			=			0
						lowerCAmelCase_      			=			[]
						for i in range(len(__lowerCAmelCase       )       ):
									if keys[i] is None:
												reordered_blocks.append(internal_blocks[i]       )
									else:
												lowerCAmelCase_      			=			sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
												reordered_blocks.append(__lowerCAmelCase       )
												count += 1

        # And we put our main block back together with its first and last line.
						lowerCAmelCase_      			=			"\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]       )

			if code != "\n".join(__lowerCAmelCase       ):
						if check_only:
									return True
						else:
									print(F"""Overwriting {file}."""       )
									with open(__lowerCAmelCase					,							"w"       ) as f:
												f.write("\n".join(__lowerCAmelCase       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Any=True       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			for root, _, files in os.walk(__lowerCAmelCase       ):
						if "__init__.py" in files:
									lowerCAmelCase_      			=			sort_imports(os.path.join(__lowerCAmelCase					,							"__init__.py"       )					,							check_only=__lowerCAmelCase       )
									if result:
												lowerCAmelCase_      			=			[os.path.join(__lowerCAmelCase					,							"__init__.py"       )]
			if len(__lowerCAmelCase       ) > 0:
						raise ValueError(F"""Would overwrite {len(__lowerCAmelCase       )} files, run `make style`."""       )


if __name__ == "__main__":
			_A     =  argparse.ArgumentParser()
			parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
			_A     =  parser.parse_args()

			sort_imports_in_all_inits(check_only=args.check_only)





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			_lowerCAmelCase      (			__a      ,   __a ):




				@register_to_config
				def __init__(      self       ,       _UpperCamelCase = 128       ,       _UpperCamelCase = 256       ,       _UpperCamelCase = 2000.0       ,       _UpperCamelCase = 768       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 64       ,       _UpperCamelCase = 2_048       ,       _UpperCamelCase = 0.1       ,       )       ->   str:
							super().__init__()

							lowerCAmelCase_      			=			nn.Sequential(
							    nn.Linear(_UpperCamelCase       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       nn.Linear(d_model * 4       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       )

							lowerCAmelCase_      			=			nn.Embedding(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			False

							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.ModuleList()
							for lyr_num in range(_UpperCamelCase  ):
										# FiLM conditional T5 decoder
										lowerCAmelCase_      			=			DecoderLayer(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
										self.decoders.append(_UpperCamelCase  )

							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			torch.mul(query_input.unsqueeze(-1  )       ,       key_input.unsqueeze(-2  )  )
							return mask.unsqueeze(-3  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Any:
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							lowerCAmelCase_      			=			get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time       ,       embedding_dim=self.config.d_model       ,       max_period=self.config.max_decoder_noise_time       ,       ).to(dtype=self.dtype  )

							lowerCAmelCase_      			=			self.conditioning_emb(_UpperCamelCase  ).unsqueeze(1  )

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							lowerCAmelCase_      			=			decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							lowerCAmelCase_      			=			torch.broadcast_to(
							    torch.arange(_UpperCamelCase       ,       device=decoder_input_tokens.device  )       ,       (batch, seq_length)       ,       )

							lowerCAmelCase_      			=			self.position_encoding(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.continuous_inputs_projection(_UpperCamelCase  )
							inputs += position_encodings
							lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

							# decoder: No padding present.
							lowerCAmelCase_      			=			torch.ones(
							    decoder_input_tokens.shape[:2]       ,       device=decoder_input_tokens.device       ,       dtype=inputs.dtype  )

							# Translate encoding masks to encoder-decoder masks.
							lowerCAmelCase_      			=			[(x, self.encoder_decoder_mask(_UpperCamelCase       ,       _UpperCamelCase  )) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							lowerCAmelCase_      			=			torch.cat([x[0] for x in encodings_and_encdec_masks]       ,       dim=1  )
							lowerCAmelCase_      			=			torch.cat([x[1] for x in encodings_and_encdec_masks]       ,       dim=-1  )

							for lyr in self.decoders:
										lowerCAmelCase_      			=			lyr(
										    _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       encoder_attention_mask=_UpperCamelCase       ,       )[0]

							lowerCAmelCase_      			=			self.decoder_norm(_UpperCamelCase  )
							lowerCAmelCase_      			=			self.post_dropout(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.spec_out(_UpperCamelCase  )
							return spec_out





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   Dict:
							super().__init__()
							lowerCAmelCase_      			=			nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )  )

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase       ,       )  )

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase  )  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Any:
							lowerCAmelCase_      			=			self.layer[0](
							    _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

							if encoder_hidden_states is not None:
										lowerCAmelCase_      			=			torch.where(encoder_attention_mask > 0       ,       0       ,       -1e1_0  ).to(
										    encoder_hidden_states.dtype  )

										lowerCAmelCase_      			=			self.layer[1](
										    _UpperCamelCase       ,       key_value_states=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

							# Apply Film Conditional Feed Forward layer
							lowerCAmelCase_      			=			self.layer[-1](_UpperCamelCase       ,       _UpperCamelCase  )

							return (hidden_states,)





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
							super().__init__()
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )
							lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
							lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Dict:
							# pre_self_attention_layer_norm
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )

							if conditioning_emb is not None:
										lowerCAmelCase_      			=			self.FiLMLayer(_UpperCamelCase       ,       _UpperCamelCase  )

							# Self-attention block
							lowerCAmelCase_      			=			self.attention(_UpperCamelCase  )

							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )

							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							super().__init__()
							lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   int:
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
							lowerCAmelCase_      			=			self.attention(
							    _UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       attention_mask=attention_mask.squeeze(1  )       ,       )
							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
							return layer_output





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							super().__init__()
							lowerCAmelCase_      			=			TaDenseGatedActDense(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Tuple:
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
							if conditioning_emb is not None:
										lowerCAmelCase_      			=			self.film(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			self.DenseReluDense(_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							super().__init__()
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )
							lowerCAmelCase_      			=			NewGELUActivation()




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.act(self.wi_a(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			self.wi_a(_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_gelu * hidden_linear
							lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.wo(_UpperCamelCase  )
							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   int:
							super().__init__()
							lowerCAmelCase_      			=			nn.Parameter(torch.ones(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			eps




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
							# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
							# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
							# half-precision inputs is done in fp32

							lowerCAmelCase_      			=			hidden_states.to(torch.floataa  ).pow(2  ).mean(-1       ,       keepdim=_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
										lowerCAmelCase_      			=			hidden_states.to(self.weight.dtype  )

							return self.weight * hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def   __a		(      self       ,       _UpperCamelCase  )       ->   torch.Tensor:
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044715 * torch.pow(_UpperCamelCase       ,       3.0  ))  ))





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   str:
							super().__init__()
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       out_features * 2       ,       bias=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.scale_bias(_UpperCamelCase  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			torch.chunk(_UpperCamelCase       ,       2       ,       -1  )
							lowerCAmelCase_      			=			x * (1 + scale) + shift
							return x



