


"""simple docstring"""


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
A	:  Union[str, Any]            =     "src/diffusers"
A	:  Optional[Any]            =     "."


# This is to make sure the diffusers module imported is the one in the repo.
A	:  List[Any]            =     importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
A	:  Union[str, Any]            =     spec.loader.load_module()


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

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

			with open(os.path.join(_UpperCamelCase       ,					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(_UpperCamelCase ) 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(_UpperCamelCase ):
						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(_UpperCamelCase ) and _should_continue(lines[line_index]       ,					_UpperCamelCase ):
						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(_UpperCamelCase )


A	:  Tuple            =     re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
A	:  List[str]            =     re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
A	:  Any            =     re.compile(R"<FILL\s+[^>]*>")


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=False ):

			'''simple docstring'''




			with open(_UpperCamelCase       ,					"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(_UpperCamelCase ):
						__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(_UpperCamelCase )
						__lowerCAmelCase							       =     get_indent(_UpperCamelCase )

						__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(_UpperCamelCase ) and should_continue:
									line_index += 1
									if line_index >= len(_UpperCamelCase ):
												break
									__lowerCAmelCase							       =     lines[line_index]
									__lowerCAmelCase							       =     _should_continue(_UpperCamelCase       ,					_UpperCamelCase ) and re.search(f"^{indent}# End copy"       ,					_UpperCamelCase ) 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(_UpperCamelCase )

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

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

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


def _lowerCamelCase      (						_UpperCamelCase = False ):

			'''simple docstring'''




			__lowerCAmelCase							       =     glob.glob(os.path.join(_UpperCamelCase       ,					"**/*.py" )       ,					recursive=_UpperCamelCase )
			__lowerCAmelCase							       =     []
			for filename in all_files:
						__lowerCAmelCase							       =     is_copy_consistent(_UpperCamelCase       ,					_UpperCamelCase )
						diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
			if not overwrite and len(_UpperCamelCase ) > 0:
						__lowerCAmelCase							       =     "\n".join(_UpperCamelCase )
						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__":
	A	:  Any            =     argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	A	:  int            =     parser.parse_args()

	check_copies(args.fix_and_overwrite)








"""simple docstring"""


import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A	:  Dict            =     get_logger(__name__)


A	:  Dict            =     R"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  **__a      ):
										for processor in self:
													__lowerCAmelCase							       =     inspect.signature(processor.__call__      ).parameters
													if len(__a      ) > 3:
																if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																			raise ValueError(
																			    f"Make sure that all the required parameters: {list(function_args.keys()      )} for "
																			    f"{processor.__class__} are passed to the logits processor."      )
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a       ,  **__a      )
													else:
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a      )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or not (temperature > 0):
													raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"      )

										__lowerCAmelCase							       =     temperature



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores / self.temperature
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) or (top_p < 0 or top_p > 1.0):
													raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}"      )
										if not isinstance(__a       ,  __a      ) or (min_tokens_to_keep < 1):
													raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"      )

										__lowerCAmelCase							       =     top_p
										__lowerCAmelCase							       =     filter_value
										__lowerCAmelCase							       =     min_tokens_to_keep



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  scores.shape[-1]      )

										__lowerCAmelCase							       =     jnp.full_like(__a       ,  self.filter_value      )
										__lowerCAmelCase							       =     jax.nn.softmax(__a       ,  axis=-1      ).cumsum(axis=-1      )
										__lowerCAmelCase							       =     cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										__lowerCAmelCase							       =     jnp.roll(__a       ,  1      )
										score_mask |= score_mask.at[:, 0].set(__a      )

										# min tokens to keep
										__lowerCAmelCase							       =     score_mask.at[:, : self.min_tokens_to_keep].set(__a      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jax.lax.sort_key_val(__a       ,  __a      )[-1]

										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) or top_k <= 0:
													raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}"      )

										__lowerCAmelCase							       =     max(__a       ,  __a      )
										__lowerCAmelCase							       =     filter_value



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     scores.shape
										__lowerCAmelCase							       =     jnp.full(batch_size * vocab_size       ,  self.filter_value      )

										__lowerCAmelCase							       =     min(self.top_k       ,  scores.shape[-1]      )  # Safety check
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  __a      )
										__lowerCAmelCase							       =     jnp.broadcast_to((jnp.arange(__a      ) * vocab_size)[:, None]       ,  (batch_size, topk)      ).flatten()
										__lowerCAmelCase							       =     topk_scores.flatten()
										__lowerCAmelCase							       =     topk_indices.flatten() + shift

										__lowerCAmelCase							       =     next_scores_flat.at[topk_indices_flat].set(__a      )
										__lowerCAmelCase							       =     next_scores_flat.reshape(__a       ,  __a      )
										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     bos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.bos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     max_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.max_length + 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.eos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or min_length < 0:
													raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}"      )

										if not isinstance(__a       ,  __a      ) or eos_token_id < 0:
													raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}"      )

										__lowerCAmelCase							       =     min_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# create boolean flag to decide if min length penalty should be applied
										__lowerCAmelCase							       =     1 - jnp.clip(cur_len - self.min_length       ,  0       ,  1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     begin_index



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.begin_index      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     dict(__a      )
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										__lowerCAmelCase							       =     jnp.ones((max(force_token_map.keys()      ) + 1)       ,  dtype=jnp.intaa      ) * -1
										for index, token in force_token_map.items():
													if token is not None:
																__lowerCAmelCase							       =     force_token_array.at[index].set(__a      )
										__lowerCAmelCase							       =     jnp.intaa(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										def _force_token(__a      ):
													__lowerCAmelCase							       =     scores.shape[0]
													__lowerCAmelCase							       =     self.force_token_array[generation_idx]

													__lowerCAmelCase							       =     jnp.ones_like(__a       ,  dtype=scores.dtype      ) * -float("inf"      )
													__lowerCAmelCase							       =     jnp.zeros((batch_size, 1)       ,  dtype=scores.dtype      )
													__lowerCAmelCase							       =     lax.dynamic_update_slice(__a       ,  __a       ,  (0, current_token)      )
													return new_scores

										__lowerCAmelCase							       =     lax.cond(
										    cur_len >= self.force_token_array.shape[0]       ,  lambda: scores       ,  lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0       ,  lambda: _force_token(__a      )       ,  lambda: scores       ,  )       ,  )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     generate_config.eos_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id + 1

										__lowerCAmelCase							       =     decoder_input_length + 1

										if generate_config.is_multilingual:
													# room for language token and task token
													self.begin_index += 2
										if hasattr(__a       ,  "max_initial_timestamp_index"      ):
													__lowerCAmelCase							       =     generate_config.max_initial_timestamp_index
										else:
													__lowerCAmelCase							       =     model_config.vocab_size
										if self.max_initial_timestamp_index is None:
													__lowerCAmelCase							       =     model_config.vocab_size



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										__lowerCAmelCase							       =     scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

										def handle_pairs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) >= 1       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __a       ,  )

													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) < 2       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin       ,  __a       ,  __a       ,  )

													return jnp.where(
													    __a       ,  jnp.where(
													        penultimate_was_timestamp > 0       ,  scores_k.at[self.timestamp_begin :].set(-float("inf"      )      )       ,  scores_k.at[: self.eos_token_id].set(-float("inf"      )      )       ,  )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										__lowerCAmelCase							       =     jnp.where(cur_len == self.begin_index       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jnp.where(
										    self.max_initial_timestamp_index is not None       ,  True and apply_max_initial_timestamp       ,  __a       ,  )

										__lowerCAmelCase							       =     self.timestamp_begin + self.max_initial_timestamp_index

										__lowerCAmelCase							       =     jnp.where(
										    __a       ,  scores.at[:, last_allowed + 1 :].set(-float("inf"      )      )       ,  __a       ,  )

										# if sum of probability over timestamps is above any other token, sample timestamp
										__lowerCAmelCase							       =     jax.nn.log_softmax(__a       ,  axis=-1      )

										def handle_cumulative_probs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,  axis=-1      )
													__lowerCAmelCase							       =     jnp.max(logprobs_k[: self.timestamp_begin]      )
													return jnp.where(
													    timestamp_logprob > max_text_token_logprob       ,  scores_k.at[: self.timestamp_begin].set(-float("inf"      )      )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										return scores




