



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






    return EnvironmentCommand()




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  int:



    """simple docstring"""






    return EnvironmentCommand(args.accelerate_config_file		)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @staticmethod
    def 			_lowerCAmelCase					(       lowerCamelCase__		):
        A :       Tuple          =   parser.add_parser("""env"""		)
        download_parser.set_defaults(func=lowerCamelCase__		)
        download_parser.add_argument(
            """--accelerate-config_file""",		default=lowerCamelCase__,		help="""The accelerate config file to use for the default values in the launching script.""",		)
        download_parser.set_defaults(func=lowerCamelCase__		)






    def __init__(       self,		lowerCamelCase__,		*lowerCamelCase__		):
        A :       List[Any]          =   accelerate_config_file






    def 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   """not installed"""
        if is_safetensors_available():
            import safetensors

            A :       Union[str, Any]          =   safetensors.__version__
        elif importlib.util.find_spec("""safetensors"""		) is not None:
            import safetensors

            A :       str          =   f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

        A :       Dict          =   """not installed"""
        A :       Optional[Any]          =   """not found"""
        if is_accelerate_available():
            import accelerate
            from accelerate.commands.config import default_config_file, load_config_from_file

            A :       Union[str, Any]          =   accelerate.__version__
            # Get the default from the config file.
            if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase__		):
                A :       List[Any]          =   load_config_from_file(self._accelerate_config_file		).to_dict()

            A :       Optional[int]          =   (
                """\n""".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]		)
                if isinstance(lowerCamelCase__,		lowerCamelCase__		)
                else f'''\t{accelerate_config}'''
            )

        A :       Tuple          =   """not installed"""
        A :       Tuple          =   """NA"""
        if is_torch_available():
            import torch

            A :       List[str]          =   torch.__version__
            A :       Optional[int]          =   torch.cuda.is_available()

        A :       Dict          =   """not installed"""
        A :       Optional[int]          =   """NA"""
        if is_tf_available():
            import tensorflow as tf

            A :       Any          =   tf.__version__
            try:
                # deprecated in v2.1
                A :       Tuple          =   tf.test.is_gpu_available()
            except AttributeError:
                # returns list of devices, convert to bool
                A :       List[Any]          =   bool(tf.config.list_physical_devices("""GPU"""		)		)

        A :       int          =   """not installed"""
        A :       List[str]          =   """not installed"""
        A :       Dict          =   """not installed"""
        A :       List[str]          =   """NA"""
        if is_flax_available():
            import flax
            import jax
            import jaxlib

            A :       Optional[int]          =   flax.__version__
            A :       Tuple          =   jax.__version__
            A :       int          =   jaxlib.__version__
            A :       List[Any]          =   jax.lib.xla_bridge.get_backend().platform

        A :       Dict          =   {
            """`transformers` version""": version,
            """Platform""": platform.platform(),
            """Python version""": platform.python_version(),
            """Huggingface_hub version""": huggingface_hub.__version__,
            """Safetensors version""": f'''{safetensors_version}''',
            """Accelerate version""": f'''{accelerate_version}''',
            """Accelerate config""": f'''{accelerate_config_str}''',
            """PyTorch version (GPU?)""": f'''{pt_version} ({pt_cuda_available})''',
            """Tensorflow version (GPU?)""": f'''{tf_version} ({tf_cuda_available})''',
            """Flax version (CPU?/GPU?/TPU?)""": f'''{flax_version} ({jax_backend})''',
            """Jax version""": f'''{jax_version}''',
            """JaxLib version""": f'''{jaxlib_version}''',
            """Using GPU in script?""": """<fill in>""",
            """Using distributed or parallel set-up in script?""": """<fill in>""",
        }

        print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""		)
        print(self.format_dict(lowerCamelCase__		)		)

        return info






    @staticmethod
    def 			_lowerCAmelCase					(       lowerCamelCase__		):
        return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]		) + "\n"







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


