




"""simple docstring"""




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



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

     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
     return k, o, q, v



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

     if split_mlp_wi:
          lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
          lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
          lowerCAmelCase__          =			(wi_a, wi_a)
     else:
          lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wi/kernel']

     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wo/kernel']
     return wi, wo



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

     return params[F'{prefix}/layers_{i}/{layer_name}/scale']



def 							_A       (						lowerCAmelCase_					: dict       ,    *, lowerCAmelCase_					: int       ,    lowerCAmelCase_					: bool      ):
     """simple docstring"""

     lowerCAmelCase__          =			traverse_util.flatten_dict(variables["target"]      )
     lowerCAmelCase__          =			{"/".join(lowerCAmelCase_      ): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     lowerCAmelCase__          =			"encoder/layers_0/mlp/wi_0/kernel" in old
     print("Split MLP:"       ,    lowerCAmelCase_      )

     lowerCAmelCase__          =			collections.OrderedDict()

     # Shared embeddings.
     lowerCAmelCase__          =			old["token_embedder/embedding"]

     # Encoder.
     for i in range(lowerCAmelCase_      ):
          # Block i, layer 0 (Self Attention).
          lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    "pre_attention_layer_norm"      )
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_attention_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    "attention"      )
          lowerCAmelCase__          =			layer_norm
          lowerCAmelCase__          =			k.T
          lowerCAmelCase__          =			o.T
          lowerCAmelCase__          =			q.T
          lowerCAmelCase__          =			v.T

          # Block i, layer 1 (MLP).
          lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    "pre_mlp_layer_norm"      )
          lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_mlp_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			layer_norm
          if split_mlp_wi:
               lowerCAmelCase__          =			wi[0].T
               lowerCAmelCase__          =			wi[1].T
          else:
               lowerCAmelCase__          =			wi.T
          lowerCAmelCase__          =			wo.T

     lowerCAmelCase__          =			old[
         "encoder/relpos_bias/rel_embedding"
     ].T
     lowerCAmelCase__          =			old["encoder/encoder_norm/scale"]

     if not is_encoder_only:
          # Decoder.
          for i in range(lowerCAmelCase_      ):
               # Block i, layer 0 (Self Attention).
               lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "pre_self_attention_layer_norm"      )
               lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_attention_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "self_attention"      )
               lowerCAmelCase__          =			layer_norm
               lowerCAmelCase__          =			k.T
               lowerCAmelCase__          =			o.T
               lowerCAmelCase__          =			q.T
               lowerCAmelCase__          =			v.T

               # Block i, layer 1 (Cross Attention).
               lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "pre_cross_attention_layer_norm"      )
               lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_attention_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "encoder_decoder_attention"      )
               lowerCAmelCase__          =			layer_norm
               lowerCAmelCase__          =			k.T
               lowerCAmelCase__          =			o.T
               lowerCAmelCase__          =			q.T
               lowerCAmelCase__          =			v.T

               # Block i, layer 2 (MLP).
               lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "pre_mlp_layer_norm"      )
               lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_mlp_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			layer_norm
               if split_mlp_wi:
                    lowerCAmelCase__          =			wi[0].T
                    lowerCAmelCase__          =			wi[1].T
               else:
                    lowerCAmelCase__          =			wi.T
               lowerCAmelCase__          =			wo.T

          lowerCAmelCase__          =			old["decoder/decoder_norm/scale"]
          lowerCAmelCase__          =			old[
              "decoder/relpos_bias/rel_embedding"
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               lowerCAmelCase__          =			old["decoder/logits_dense/kernel"].T

     return new



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

     lowerCAmelCase__          =			collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          lowerCAmelCase__          =			state_dict["shared.weight"]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               lowerCAmelCase__          =			state_dict["shared.weight"]

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print("Using shared word embeddings as lm_head."      )
               lowerCAmelCase__          =			state_dict["shared.weight"]

     return state_dict



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

     lowerCAmelCase__          =			checkpoints.load_tax_checkpoint(lowerCAmelCase_      )
     lowerCAmelCase__          =			convert_tax_to_pytorch(lowerCAmelCase_       ,    num_layers=config.num_layers       ,    is_encoder_only=lowerCAmelCase_      )
     lowerCAmelCase__          =			make_state_dict(lowerCAmelCase_       ,    lowerCAmelCase_      )
     model.load_state_dict(lowerCAmelCase_       ,    strict=lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     lowerCAmelCase__          =			TaConfig.from_json_file(lowerCAmelCase_      )
     print(F'Building PyTorch model from configuration: {config}'      )
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          lowerCAmelCase__          =			TaEncoderModel(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			TaForConditionalGeneration(lowerCAmelCase_      )

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'      )
     model.save_pretrained(lowerCAmelCase_      )

     # Verify that we can load the checkpoint.
     model.from_pretrained(lowerCAmelCase_      )
     print("Done"      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
     )
     UpperCamelCase								=					parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )






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)
