
"""simple docstring"""



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	(						_A      )    ->  List[Any]:





    """simple docstring"""

    return EnvironmentCommand()


def  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

    return EnvironmentCommand(args.accelerate_config_file      )







class 		a_						( _UpperCAmelCase	):







    @staticmethod
    def      _snake_case					(			__UpperCamelCase			:       ArgumentParser     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   parser.add_parser("""env"""     )
        download_parser.set_defaults(func=__UpperCamelCase     )
        download_parser.add_argument(
            """--accelerate-config_file"""     ,					default=__UpperCamelCase     ,					help="""The accelerate config file to use for the default values in the launching script."""     ,					)
        download_parser.set_defaults(func=__UpperCamelCase     )







    def __init__(			self			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					*__UpperCamelCase			:       Any     )		->None:






        '''simple docstring'''






        _UpperCAmelCase          =   accelerate_config_file







    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   """not installed"""
        if is_safetensors_available():
            import safetensors

            _UpperCAmelCase          =   safetensors.__version__
        elif importlib.util.find_spec("""safetensors"""     ) is not None:
            import safetensors

            _UpperCAmelCase          =   f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

        _UpperCAmelCase          =   """not installed"""
        _UpperCAmelCase          =   _UpperCAmelCase          =   """not found"""
        if is_accelerate_available():
            import accelerate
            from accelerate.commands.config import default_config_file, load_config_from_file

            _UpperCAmelCase          =   accelerate.__version__
            # Get the default from the config file.
            if self._accelerate_config_file is not None or os.path.isfile(__UpperCamelCase     ):
                _UpperCAmelCase          =   load_config_from_file(self._accelerate_config_file     ).to_dict()

            _UpperCAmelCase          =   (
                """\n""".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]     )
                if isinstance(__UpperCamelCase     ,					__UpperCamelCase     )
                else f"""\t{accelerate_config}"""
            )

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

            _UpperCAmelCase          =   torch.__version__
            _UpperCAmelCase          =   torch.cuda.is_available()

        _UpperCAmelCase          =   """not installed"""
        _UpperCAmelCase          =   """NA"""
        if is_tf_available():
            import tensorflow as tf

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

        _UpperCAmelCase          =   """not installed"""
        _UpperCAmelCase          =   """not installed"""
        _UpperCAmelCase          =   """not installed"""
        _UpperCAmelCase          =   """NA"""
        if is_flax_available():
            import flax
            import jax
            import jaxlib

            _UpperCAmelCase          =   flax.__version__
            _UpperCAmelCase          =   jax.__version__
            _UpperCAmelCase          =   jaxlib.__version__
            _UpperCAmelCase          =   jax.lib.xla_bridge.get_backend().platform

        _UpperCAmelCase          =   {
            """`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(__UpperCamelCase     )     )

        return info







    @staticmethod
    def      _snake_case					(			__UpperCamelCase			:       Union[str, Any]     )		->Dict:






        '''simple docstring'''






        return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )