



"""simple docstring"""


import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def 			_lowerCAmelCase				(       lowercase_						):
      return EnvironmentCommand()







class 				A_      (					__a    ):



      """simple docstring"""






      @staticmethod
      def      UpperCAmelCase__					( lowercase_			:ArgumentParser )    ->      Dict:
            UpperCAmelCase					=			parser.add_parser('env' )
            download_parser.set_defaults(func=UpperCamelCase__ )






      def      UpperCAmelCase__					( self			:Optional[int] )    ->      Tuple:
            UpperCAmelCase					=			huggingface_hub.__version__

            UpperCAmelCase					=			'not installed'
            UpperCAmelCase					=			'NA'
            if is_torch_available():
                  import torch

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

            UpperCAmelCase					=			'not installed'
            if is_transformers_available():
                  import transformers

                  UpperCAmelCase					=			transformers.__version__

            UpperCAmelCase					=			'not installed'
            if is_accelerate_available():
                  import accelerate

                  UpperCAmelCase					=			accelerate.__version__

            UpperCAmelCase					=			'not installed'
            if is_xformers_available():
                  import xformers

                  UpperCAmelCase					=			xformers.__version__

            UpperCAmelCase					=			{
                '`diffusers` version': version,
                'Platform': platform.platform(),
                'Python version': platform.python_version(),
                'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
                'Huggingface_hub version': hub_version,
                'Transformers version': transformers_version,
                'Accelerate version': accelerate_version,
                'xFormers version': xformers_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      UpperCAmelCase__					( lowercase_			:Dict )    ->      Optional[Any]:
            return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"










"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase					=			pt_tensor.transpose(2		,     3		,     1		,     0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase					=			flax_model.init_weights(PRNGKey(lowercase_						)						)

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





