

'''simple docstring'''







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__lowerCamelCase		       =    (
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


__lowerCamelCase		       =    logging.get_logger(__name__)  # pylint: disable=invalid-name
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      """https://pypi.org/pypi/diffusers/json"""
       A_						=      json.loads(request.urlopen(UpperCAmelCase__				).read()				)["""releases"""].keys()
       return sorted(UpperCAmelCase__,       key=lambda UpperCAmelCase__				: version.Version(UpperCAmelCase__				)				)
def       UpperCAmelCase__    (      )			->			Tuple:
       # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
       if HF_MODULES_CACHE in sys.path:
              return

       sys.path.append(UpperCAmelCase__				)
       os.makedirs(UpperCAmelCase__,       exist_ok=UpperCAmelCase__				)
       A_						=      Path(UpperCAmelCase__				) / """__init__.py"""
       if not init_path.exists():
              init_path.touch()
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Union[str, Any]:
       init_hf_modules()
       A_						=      Path(UpperCAmelCase__				) / name
       # If the parent module does not exist yet, recursively create it.
       if not dynamic_module_path.parent.exists():
              create_dynamic_module(dynamic_module_path.parent				)
       os.makedirs(UpperCAmelCase__,       exist_ok=UpperCAmelCase__				)
       A_						=      dynamic_module_path / """__init__.py"""
       if not init_path.exists():
              init_path.touch()
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
       with open(UpperCAmelCase__,       """r""",       encoding="""utf-8"""				) as f:
              A_						=      f.read()

       # Imports of the form `import .xxx`
       A_						=      re.findall("""^\s*import\s+\.(\S+)\s*$""",       UpperCAmelCase__,       flags=re.MULTILINE				)
       # Imports of the form `from .xxx import yyy`
       relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import""",       UpperCAmelCase__,       flags=re.MULTILINE				)
       # Unique-ify
       return list(set(UpperCAmelCase__				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Any:
       A_						=      False
       A_						=      [module_file]
       A_						=      []

       # Let's recurse through all relative imports
       while not no_change:
              A_						=      []
              for f in files_to_check:
                     new_imports.extend(get_relative_imports(UpperCAmelCase__				)				)

              A_						=      Path(UpperCAmelCase__				).parent
              A_						=      [str(module_path / m				) for m in new_imports]
              A_						=      [f for f in new_import_files if f not in all_relative_imports]
              A_						=      [F'''{f}.py''' for f in new_import_files]

              A_						=      len(UpperCAmelCase__				) == 0
              all_relative_imports.extend(UpperCAmelCase__				)

       return all_relative_imports
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Any:
       with open(UpperCAmelCase__,       """r""",       encoding="""utf-8"""				) as f:
              A_						=      f.read()

       # Imports of the form `import xxx`
       A_						=      re.findall("""^\s*import\s+(\S+)\s*$""",       UpperCAmelCase__,       flags=re.MULTILINE				)
       # Imports of the form `from xxx import yyy`
       imports += re.findall("""^\s*from\s+(\S+)\s+import""",       UpperCAmelCase__,       flags=re.MULTILINE				)
       # Only keep the top-level module
       A_						=      [imp.split("""."""				)[0] for imp in imports if not imp.startswith("""."""				)]

       # Unique-ify and test we got them all
       A_						=      list(set(UpperCAmelCase__				)				)
       A_						=      []
       for imp in imports:
              try:
                     importlib.import_module(UpperCAmelCase__				)
              except ImportError:
                     missing_packages.append(UpperCAmelCase__				)

       if len(UpperCAmelCase__				) > 0:
              raise ImportError(
                  """This modeling file requires the following packages that were not found in your environment: """
                  F'''{", ".join(UpperCAmelCase__				)}. Run `pip install {" ".join(UpperCAmelCase__				)}`'''				)

       return get_relative_imports(UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			List[str]:
       A_						=      module_path.replace(os.path.sep,       """."""				)
       A_						=      importlib.import_module(UpperCAmelCase__				)

       if class_name is None:
              return find_pipeline_class(UpperCAmelCase__				)
       return getattr(UpperCAmelCase__,       UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
       from ..pipelines import DiffusionPipeline

       A_						=      dict(inspect.getmembers(UpperCAmelCase__,       inspect.isclass				)				)

       A_						=      None
       for cls_name, cls in cls_members.items():
              if (
                  cls_name != DiffusionPipeline.__name__
                  and issubclass(cls,       UpperCAmelCase__				)
                  and cls.__module__.split("""."""				)[0] != "diffusers"
              ):
                     if pipeline_class is not None:
                            raise ValueError(
                                F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                                F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                                F''' {loaded_module}.'''				)
                     A_						=      cls

       return pipeline_class
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = None,       UpperCAmelCase__ = False,       UpperCAmelCase__ = False,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = False,       )			->			List[str]:
       A_						=      str(UpperCAmelCase__				)

       A_						=      os.path.join(UpperCAmelCase__,       UpperCAmelCase__				)

       if os.path.isfile(UpperCAmelCase__				):
              A_						=      module_file_or_url
              A_						=      """local"""
       elif pretrained_model_name_or_path.count("""/"""				) == 0:
              A_						=      get_diffusers_versions()
              # cut ".dev0"
              A_						=      """v""" + """.""".join(__version__.split("""."""				)[:3]				)

              # retrieve github version that matches
              if revision is None:
                     A_						=      latest_version if latest_version[1:] in available_versions else """main"""
                     logger.info(F'''Defaulting to latest_version: {revision}.'''				)
              elif revision in available_versions:
                     A_						=      F'''v{revision}'''
              elif revision == "main":
                     A_						=      revision
              else:
                     raise ValueError(
                         F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
                         F''' {", ".join(available_versions + ["main"]				)}.'''				)

              # community pipeline on GitHub
              A_						=      COMMUNITY_PIPELINES_URL.format(revision=UpperCAmelCase__,       pipeline=UpperCAmelCase__				)
              try:
                     A_						=      cached_download(
                         UpperCAmelCase__,       cache_dir=UpperCAmelCase__,       force_download=UpperCAmelCase__,       proxies=UpperCAmelCase__,       resume_download=UpperCAmelCase__,       local_files_only=UpperCAmelCase__,       use_auth_token=UpperCAmelCase__,       )
                     A_						=      """git"""
                     A_						=      pretrained_model_name_or_path + """.py"""
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''				)
                     raise
       else:
              try:
                     # Load from URL or cache if already cached
                     A_						=      hf_hub_download(
                         UpperCAmelCase__,       UpperCAmelCase__,       cache_dir=UpperCAmelCase__,       force_download=UpperCAmelCase__,       proxies=UpperCAmelCase__,       resume_download=UpperCAmelCase__,       local_files_only=UpperCAmelCase__,       use_auth_token=UpperCAmelCase__,       )
                     A_						=      os.path.join("""local""",       """--""".join(pretrained_model_name_or_path.split("""/"""				)				)				)
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''				)
                     raise

    # Check we have all the requirements in our environment
       A_						=      check_imports(UpperCAmelCase__				)

       # Now we move the module inside our cached dynamic modules.
       A_						=      DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
       create_dynamic_module(UpperCAmelCase__				)
       A_						=      Path(UpperCAmelCase__				) / full_submodule
       if submodule == "local" or submodule == "git":
              # We always copy local files (we could hash the file to see if there was a change, and give them the name of
              # that hash, to only copy when there is a modification but it seems overkill for now).
              # The only reason we do the copy is to avoid putting too many folders in sys.path.
              shutil.copy(UpperCAmelCase__,       submodule_path / module_file				)
              for module_needed in modules_needed:
                     A_						=      F'''{module_needed}.py'''
                     shutil.copy(os.path.join(UpperCAmelCase__,       UpperCAmelCase__				),       submodule_path / module_needed				)
       else:
              # Get the commit hash
              # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     A_						=      use_auth_token
              elif use_auth_token is True:
                     A_						=      HfFolder.get_token()
              else:
                     A_						=      None

              A_						=      model_info(UpperCAmelCase__,       revision=UpperCAmelCase__,       token=UpperCAmelCase__				).sha

              # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
              # benefit of versioning.
              A_						=      submodule_path / commit_hash
              A_						=      full_submodule + os.path.sep + commit_hash
              create_dynamic_module(UpperCAmelCase__				)

              if not (submodule_path / module_file).exists():
                     shutil.copy(UpperCAmelCase__,       submodule_path / module_file				)
              # Make sure we also have every file with relative
              for module_needed in modules_needed:
                     if not (submodule_path / module_needed).exists():
                            get_cached_module_file(
                                UpperCAmelCase__,       F'''{module_needed}.py''',       cache_dir=UpperCAmelCase__,       force_download=UpperCAmelCase__,       resume_download=UpperCAmelCase__,       proxies=UpperCAmelCase__,       use_auth_token=UpperCAmelCase__,       revision=UpperCAmelCase__,       local_files_only=UpperCAmelCase__,       )
       return os.path.join(UpperCAmelCase__,       UpperCAmelCase__				)







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = False,       UpperCAmelCase__ = False,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = False,       **UpperCAmelCase__,       )			->			List[str]:
       A_						=      get_cached_module_file(
           UpperCAmelCase__,       UpperCAmelCase__,       cache_dir=UpperCAmelCase__,       force_download=UpperCAmelCase__,       resume_download=UpperCAmelCase__,       proxies=UpperCAmelCase__,       use_auth_token=UpperCAmelCase__,       revision=UpperCAmelCase__,       local_files_only=UpperCAmelCase__,       )
       return get_class_in_module(UpperCAmelCase__,       final_module.replace(""".py""",       """"""				)				)



'''simple docstring'''







import tensorflow as tf

from ...tf_utils import shape_list


class 					A__      (      tf.keras.layers.Layer			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=1		,				UpperCamelCase__=False		,				**UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )

           A_						=      vocab_size
           A_						=      d_embed
           A_						=      d_proj

           A_						=      cutoffs + [vocab_size]
           A_						=      [0] + self.cutoffs
           A_						=      div_val

           A_						=      self.cutoffs[0]
           A_						=      len(self.cutoffs       ) - 1
           A_						=      self.shortlist_size + self.n_clusters
           A_						=      keep_order

           A_						=      []
           A_						=      []



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





           '''simple docstring'''
           if self.n_clusters > 0:
                  A_						=      self.add_weight(
                      shape=(self.n_clusters, self.d_embed)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name="""cluster_weight"""       )
                  A_						=      self.add_weight(
                      shape=(self.n_clusters,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name="""cluster_bias"""       )

           if self.div_val == 1:
                  for i in range(len(self.cutoffs       )       ):
                         if self.d_proj != self.d_embed:
                                A_						=      self.add_weight(
                                    shape=(self.d_embed, self.d_proj)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_projs_._{i}'''		,				)
                                self.out_projs.append(UpperCamelCase__       )
                         else:
                                self.out_projs.append(UpperCamelCase__       )
                         A_						=      self.add_weight(
                             shape=(self.vocab_size, self.d_embed)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._weight'''		,				)
                         A_						=      self.add_weight(
                             shape=(self.vocab_size,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._bias'''		,				)
                         self.out_layers.append((weight, bias)       )
           else:
                  for i in range(len(self.cutoffs       )       ):
                         A_					,			A_						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                         A_						=      self.d_embed // (self.div_val**i)

                         A_						=      self.add_weight(
                             shape=(d_emb_i, self.d_proj)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_projs_._{i}'''       )
                         self.out_projs.append(UpperCamelCase__       )
                         A_						=      self.add_weight(
                             shape=(r_idx - l_idx, d_emb_i)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._weight'''		,				)
                         A_						=      self.add_weight(
                             shape=(r_idx - l_idx,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._bias'''		,				)
                         self.out_layers.append((weight, bias)       )
           super().build(UpperCamelCase__       )



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      List[Any]:





           '''simple docstring'''
           A_						=      x
           if proj is not None:
                  A_						=      tf.einsum("""ibd,ed->ibe"""		,				UpperCamelCase__		,				UpperCamelCase__       )
           return tf.einsum("""ibd,nd->ibn"""		,				UpperCamelCase__		,				UpperCamelCase__       ) + b



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__		,				UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           A_						=      shape_list(UpperCamelCase__       )
           A_						=      tf.range(lp_size[0]		,				dtype=target.dtype       )
           A_						=      tf.stack([r, target]		,				1       )
           return tf.gather_nd(UpperCamelCase__		,				UpperCamelCase__       )







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=True		,				UpperCamelCase__=False       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      0
           if self.n_clusters == 0:
                  A_						=      self._logit(UpperCamelCase__		,				self.out_layers[0][0]		,				self.out_layers[0][1]		,				self.out_projs[0]       )
                  if target is not None:
                         A_						=      tf.nn.sparse_softmax_cross_entropy_with_logits(labels=UpperCamelCase__		,				logits=UpperCamelCase__       )
                  A_						=      tf.nn.log_softmax(UpperCamelCase__		,				axis=-1       )
           else:
                  A_						=      shape_list(UpperCamelCase__       )
                  A_						=      []
                  A_						=      tf.zeros(hidden_sizes[:2]       )
                  for i in range(len(self.cutoffs       )       ):
                         A_					,			A_						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                         if target is not None:
                                A_						=      (target >= l_idx) & (target < r_idx)
                                A_						=      tf.where(UpperCamelCase__       )
                                A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       ) - l_idx

                         if self.div_val == 1:
                                A_						=      self.out_layers[0][0][l_idx:r_idx]
                                A_						=      self.out_layers[0][1][l_idx:r_idx]
                         else:
                                A_						=      self.out_layers[i][0]
                                A_						=      self.out_layers[i][1]

                         if i == 0:
                                A_						=      tf.concat([cur_W, self.cluster_weight]		,				0       )
                                A_						=      tf.concat([cur_b, self.cluster_bias]		,				0       )

                                A_						=      self._logit(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				self.out_projs[0]       )
                                A_						=      tf.nn.log_softmax(UpperCamelCase__       )
                                out.append(head_logprob[..., : self.cutoffs[0]]       )
                                if target is not None:
                                       A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       )
                                       A_						=      self._gather_logprob(UpperCamelCase__		,				UpperCamelCase__       )
                         else:
                                A_						=      self._logit(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				self.out_projs[i]       )
                                A_						=      tf.nn.log_softmax(UpperCamelCase__       )
                                A_						=      self.cutoffs[0] + i - 1  # No probability for the head cluster
                                A_						=      head_logprob[..., cluster_prob_idx, None] + tail_logprob
                                out.append(UpperCamelCase__       )
                                if target is not None:
                                       A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       )
                                       A_						=      tf.boolean_mask(UpperCamelCase__		,				UpperCamelCase__       )
                                       A_						=      self._gather_logprob(UpperCamelCase__		,				UpperCamelCase__       )
                                       cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                         if target is not None:
                                loss += tf.scatter_nd(UpperCamelCase__		,				-cur_logprob		,				shape_list(UpperCamelCase__       )       )
                  A_						=      tf.concat(UpperCamelCase__		,				axis=-1       )

           if target is not None:
                  if return_mean:
                         A_						=      tf.reduce_mean(UpperCamelCase__       )
                  # Add the training-time loss value to the layer using `self.add_loss()`.
                  self.add_loss(UpperCamelCase__       )

                  # Log the loss as a metric (we could log arbitrary metrics,
                  # including different metrics for training and inference.
                  self.add_metric(UpperCamelCase__		,				name=self.name		,				aggregation="""mean""" if return_mean else """"""       )

           return out
