




"""simple docstring"""




import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			os.path.abspath(lowerCAmelCase_      )
     logger.info(F'Converting TensorFlow checkpoint from {tf_path}'      )
     # Load weights from TF model
     lowerCAmelCase__          =			tf.train.list_variables(lowerCAmelCase_      )
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			[]
     for full_name, shape in init_vars:
          # logger.info(f"Loading TF weight {name} with shape {shape}")
          lowerCAmelCase__          =			full_name.split("/"      )
          if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
               logger.info(F'Skipping non-model layer {full_name}'      )
               continue
          if "optimizer" in full_name:
               logger.info(F'Skipping optimization layer {full_name}'      )
               continue
          if name[0] == "model":
               # ignore initial 'model'
               lowerCAmelCase__          =			name[1:]
          # figure out how many levels deep the name is
          lowerCAmelCase__          =			0
          for _name in name:
               if _name.startswith("layer_with_weights"      ):
                    depth += 1
               else:
                    break
          layer_depth.append(lowerCAmelCase_      )
          # read data
          lowerCAmelCase__          =			tf.train.load_variable(lowerCAmelCase_       ,    lowerCAmelCase_      )
          names.append("/".join(lowerCAmelCase_      )      )
          arrays.append(lowerCAmelCase_      )
     logger.info(F'Read a total of {len(lowerCAmelCase_      ):,} layers'      )

     # Sanity check
     if len(set(lowerCAmelCase_      )      ) != 1:
          raise ValueError(F'Found layer names with different depths (layer depth {list(set(lowerCAmelCase_      )      )})'      )
     lowerCAmelCase__          =			list(set(lowerCAmelCase_      )      )[0]
     if layer_depth != 1:
          raise ValueError(
              "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
              " heads."      )

     # convert layers
     logger.info("Converting weights..."      )
     for full_name, array in zip(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			full_name.split("/"      )
          lowerCAmelCase__          =			model
          lowerCAmelCase__          =			[]
          for i, m_name in enumerate(lowerCAmelCase_      ):
               if m_name == ".ATTRIBUTES":
                    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                    break
               if m_name.startswith("layer_with_weights"      ):
                    lowerCAmelCase__          =			int(m_name.split("-"      )[-1]      )
                    if layer_num <= 2:
                         # embedding layers
                         # layer_num 0: word_embeddings
                         # layer_num 1: position_embeddings
                         # layer_num 2: token_type_embeddings
                         continue
                    elif layer_num == 3:
                         # embedding LayerNorm
                         trace.extend(["embeddings", "LayerNorm"]      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "embeddings"      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "LayerNorm"      )
                    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                         # encoder layers
                         trace.extend(["encoder", "layer", str(layer_num - 4      )]      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "encoder"      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "layer"      )
                         lowerCAmelCase__          =			pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(["pooler", "dense"]      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "pooler"      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "dense"      )
               elif m_name == "embeddings":
                    trace.append("embeddings"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "embeddings"      )
                    if layer_num == 0:
                         trace.append("word_embeddings"      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "word_embeddings"      )
                    elif layer_num == 1:
                         trace.append("position_embeddings"      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "position_embeddings"      )
                    elif layer_num == 2:
                         trace.append("token_type_embeddings"      )
                         lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "token_type_embeddings"      )
                    else:
                         raise ValueError(F'Unknown embedding layer with name {full_name}'      )
                    trace.append("weight"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "weight"      )
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(["attention", "self"]      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "attention"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "self"      )
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(["attention", "output", "LayerNorm"]      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "attention"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "output"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "LayerNorm"      )
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(["attention", "output", "dense"]      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "attention"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "output"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "dense"      )
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(["output", "dense"]      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "output"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "dense"      )
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(["output", "LayerNorm"]      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "output"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "LayerNorm"      )
               elif m_name == "_key_dense":
                    # attention key
                    trace.append("key"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "key"      )
               elif m_name == "_query_dense":
                    # attention query
                    trace.append("query"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "query"      )
               elif m_name == "_value_dense":
                    # attention value
                    trace.append("value"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "value"      )
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(["intermediate", "dense"]      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "intermediate"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "dense"      )
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append("output"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "output"      )
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append("bias"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "bias"      )
               elif m_name in ["kernel", "gamma"]:
                    trace.append("weight"      )
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "weight"      )
               else:
                    logger.warning(F'Ignored {m_name}'      )
        # for certain layers reshape is necessary
          lowerCAmelCase__          =			".".join(lowerCAmelCase_      )
          if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"       ,    lowerCAmelCase_      ) or re.match(
              r"(\S+)\.attention\.output\.dense\.weight"       ,    lowerCAmelCase_      ):
               lowerCAmelCase__          =			array.reshape(pointer.data.shape      )
          if "kernel" in full_name:
               lowerCAmelCase__          =			array.transpose()
          if pointer.shape == array.shape:
               lowerCAmelCase__          =			torch.from_numpy(lowerCAmelCase_      )
          else:
               raise ValueError(
                   F'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                   F' {array.shape}'      )
          logger.info(F'Successfully set variable {full_name} to PyTorch layer {trace}'      )
     return model



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

     logger.info(F'Loading model based on config from {config_path}...'      )
     lowerCAmelCase__          =			BertConfig.from_json_file(lowerCAmelCase_      )
     lowerCAmelCase__          =			BertModel(lowerCAmelCase_      )

     # Load weights from checkpoint
     logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...'      )
     load_tfa_weights_in_bert(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )

     # Save pytorch-model
     logger.info(F'Saving PyTorch model to {pytorch_dump_path}...'      )
     torch.save(model.state_dict()       ,    lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument(
         '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
     )
     parser.add_argument(
         '--bert_config_file',
         type=str,
         required=True,
         help='The config json file corresponding to the BERT model. This specifies the model architecture.',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         type=str,
         required=True,
         help='Path to the output PyTorch model (must include filename).',
     )
     UpperCamelCase								=					parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase								=					'src/diffusers'
UpperCamelCase								=					'.'


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase								=					importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCamelCase								=					spec.loader.load_module()



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     return line.startswith(lowerCAmelCase_      ) or len(lowerCAmelCase_      ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"       ,    lowerCAmelCase_      ) is not None



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			object_name.split("."      )
     lowerCAmelCase__          =			0

     # First let's find the module where our object lives.
     lowerCAmelCase__          =			parts[i]
     while i < len(lowerCAmelCase_      ) and not os.path.isfile(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )      ):
          i += 1
          if i < len(lowerCAmelCase_      ):
               lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    parts[i]      )
     if i >= len(lowerCAmelCase_      ):
          raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'      )

     with open(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )       ,    "r"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     # Now let's find the class / func in the code!
     lowerCAmelCase__          =			""
     lowerCAmelCase__          =			0
     for name in parts[i + 1 :]:
          while (
              line_index < len(lowerCAmelCase_      ) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)'       ,    lines[line_index]      ) is None
          ):
               line_index += 1
          indent += "    "
          line_index += 1

     if line_index >= len(lowerCAmelCase_      ):
          raise ValueError(F' {object_name} does not match any function or class in {module}.'      )

     # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
     lowerCAmelCase__          =			line_index
     while line_index < len(lowerCAmelCase_      ) and _should_continue(lines[line_index]       ,    lowerCAmelCase_      ):
          line_index += 1
     # Clean up empty lines at the end (if any).
     while len(lines[line_index - 1]      ) <= 1:
          line_index -= 1

     lowerCAmelCase__          =			lines[start_index:line_index]
     return "".join(lowerCAmelCase_      )


UpperCamelCase								=					re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCamelCase								=					re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCamelCase								=					re.compile(R'<FILL\s+[^>]*>')



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			code.split("\n"      )
     lowerCAmelCase__          =			0
     while idx < len(lowerCAmelCase_      ) and len(lines[idx]      ) == 0:
          idx += 1
     if idx < len(lowerCAmelCase_      ):
          return re.search(r"^(\s*)\S"       ,    lines[idx]      ).groups()[0]
     return ""



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(get_indent(lowerCAmelCase_      )      ) > 0
     if has_indent:
          lowerCAmelCase__          =			F'class Bla:\n{code}'
     lowerCAmelCase__          =			black.Mode(target_versions={black.TargetVersion.PYaa}       ,    line_length=119       ,    preview=lowerCAmelCase_      )
     lowerCAmelCase__          =			black.format_str(lowerCAmelCase_       ,    mode=lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			style_docstrings_in_code(lowerCAmelCase_      )
     return result[len("class Bla:\n"      ) :] if has_indent else result



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[Any]=False      ):
     """simple docstring"""

     with open(lowerCAmelCase_       ,    "r"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
          lowerCAmelCase__          =			f.readlines()
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			0
     # Not a for loop cause `lines` is going to change (if `overwrite=True`).
     while line_index < len(lowerCAmelCase_      ):
          lowerCAmelCase__          =			_re_copy_warning.search(lines[line_index]      )
          if search is None:
               line_index += 1
               continue

          # There is some copied code here, let's retrieve the original.
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			search.groups()
          lowerCAmelCase__          =			find_code_in_diffusers(lowerCAmelCase_      )
          lowerCAmelCase__          =			get_indent(lowerCAmelCase_      )

          lowerCAmelCase__          =			line_index + 1 if indent == theoretical_indent else line_index + 2
          lowerCAmelCase__          =			theoretical_indent
          lowerCAmelCase__          =			start_index

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          lowerCAmelCase__          =			True
          while line_index < len(lowerCAmelCase_      ) and should_continue:
               line_index += 1
               if line_index >= len(lowerCAmelCase_      ):
                    break
               lowerCAmelCase__          =			lines[line_index]
               lowerCAmelCase__          =			_should_continue(lowerCAmelCase_       ,    lowerCAmelCase_      ) and re.search(F'^{indent}# End copy'       ,    lowerCAmelCase_      ) is None
          # Clean up empty lines at the end (if any).
          while len(lines[line_index - 1]      ) <= 1:
               line_index -= 1

          lowerCAmelCase__          =			lines[start_index:line_index]
          lowerCAmelCase__          =			"".join(lowerCAmelCase_      )

          # Remove any nested `Copied from` comments to avoid circular copies
          lowerCAmelCase__          =			[line for line in theoretical_code.split("\n"      ) if _re_copy_warning.search(lowerCAmelCase_      ) is None]
          lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )

          # Before comparing, use the `replace_pattern` on the original code.
          if len(lowerCAmelCase_      ) > 0:
               lowerCAmelCase__          =			replace_pattern.replace("with"       ,    ""      ).split(","      )
               lowerCAmelCase__          =			[_re_replace_pattern.search(lowerCAmelCase_      ) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			pattern.groups()
                    lowerCAmelCase__          =			re.sub(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
                    if option.strip() == "all-casing":
                         lowerCAmelCase__          =			re.sub(obja.lower()       ,    obja.lower()       ,    lowerCAmelCase_      )
                         lowerCAmelCase__          =			re.sub(obja.upper()       ,    obja.upper()       ,    lowerCAmelCase_      )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
               lowerCAmelCase__          =			blackify(lines[start_index - 1] + theoretical_code      )
               lowerCAmelCase__          =			theoretical_code[len(lines[start_index - 1]      ) :]

          # Test for a diff and act accordingly.
          if observed_code != theoretical_code:
               diffs.append([object_name, start_index]      )
               if overwrite:
                    lowerCAmelCase__          =			lines[:start_index] + [theoretical_code] + lines[line_index:]
                    lowerCAmelCase__          =			start_index + 1

     if overwrite and len(lowerCAmelCase_      ) > 0:
          # Warn the user a file has been modified.
          print(F'Detected changes, rewriting {filename}.'      )
          with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
               f.writelines(lowerCAmelCase_      )
     return diffs



def 							_A       (						lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     lowerCAmelCase__          =			glob.glob(os.path.join(lowerCAmelCase_       ,    "**/*.py"      )       ,    recursive=lowerCAmelCase_      )
     lowerCAmelCase__          =			[]
     for filename in all_files:
          lowerCAmelCase__          =			is_copy_consistent(lowerCAmelCase_       ,    lowerCAmelCase_      )
          diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
     if not overwrite and len(lowerCAmelCase_      ) > 0:
          lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )
          raise Exception(
              "Found the following copy inconsistencies:\n"
              + diff
              + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     UpperCamelCase								=					parser.parse_args()

     check_copies(args.fix_and_overwrite)
