





import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowercase		:  Union[str, Any]			       ="""\

"""

_lowercase		:  Optional[int]			       ="""
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

_lowercase		:  Optional[Any]			       ="""
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,     _KWARGS_DESCRIPTION							)
class 						UpperCamelCase_  (  datasets.Metric							):



					def   __a     (		self     :     int						):
							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,      citation=_CITATION       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
							        {
							            'input_texts': datasets.Value('string'						),
							        }						)       ,      reference_urls=['https://huggingface.co/docs/transformers/perplexity']       ,      )





					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     int = 16       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Optional[Any]=None						):
							if device is not None:
									assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
									if device == "gpu":
											lowerCamelCase_	:     int 						=      'cuda'
							else:
									lowerCamelCase_	:     List[str] 						=      'cuda' if torch.cuda.is_available() else 'cpu'

							lowerCamelCase_	:     str 						=      AutoModelForCausalLM.from_pretrained(lowerCamelCase						)
							lowerCamelCase_	:     Optional[Any] 						=      model.to(lowerCamelCase						)

							lowerCamelCase_	:     Optional[Any] 						=      AutoTokenizer.from_pretrained(lowerCamelCase						)

							# if batch_size > 1 (which generally leads to padding being required), and
							# if there is not an already assigned pad_token, assign an existing
							# special token to also be the padding token
							if tokenizer.pad_token is None and batch_size > 1:
									lowerCamelCase_	:     Optional[int] 						=      list(tokenizer.special_tokens_map_extended.values()						)
									# check that the model already has at least one special token defined
									assert (
									    len(lowerCamelCase						) > 0
									), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
									# assign one of the special tokens to also be the pad token
									tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]}						)

							if add_start_token:
									# leave room for <BOS> token to be added:
									assert (
									    tokenizer.bos_token is not None
									), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
									lowerCamelCase_	:     Any 						=      model.config.max_length - 1
							else:
									lowerCamelCase_	:     str 						=      model.config.max_length

							lowerCamelCase_	:     int 						=      tokenizer(
							    lowerCamelCase       ,      add_special_tokens=lowerCamelCase       ,      padding=lowerCamelCase       ,      truncation=lowerCamelCase       ,      max_length=lowerCamelCase       ,      return_tensors='pt'       ,      return_attention_mask=lowerCamelCase       ,      ).to(lowerCamelCase						)

							lowerCamelCase_	:     Union[str, Any] 						=      encodings['input_ids']
							lowerCamelCase_	:     Union[str, Any] 						=      encodings['attention_mask']

							# check that each input is long enough:
							if add_start_token:
									assert torch.all(torch.ge(attn_masks.sum(1						)       ,      1						)						), "Each input text must be at least one token long."
							else:
									assert torch.all(
									    torch.ge(attn_masks.sum(1						)       ,      2						)						), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

							lowerCamelCase_	:     Optional[int] 						=      []
							lowerCamelCase_	:     Any 						=      CrossEntropyLoss(reduction='none'						)

							for start_index in logging.tqdm(range(0       ,      len(lowerCamelCase						)       ,      lowerCamelCase						)						):
									lowerCamelCase_	:     Dict 						=      min(start_index + batch_size       ,      len(lowerCamelCase						)						)
									lowerCamelCase_	:     List[Any] 						=      encoded_texts[start_index:end_index]
									lowerCamelCase_	:     Any 						=      attn_masks[start_index:end_index]

									if add_start_token:
											lowerCamelCase_	:     Optional[Any] 						=      torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0						)						).to(lowerCamelCase						)
											lowerCamelCase_	:     int 						=      torch.cat([bos_tokens_tensor, encoded_batch]       ,      dim=1						)
											lowerCamelCase_	:     List[str] 						=      torch.cat(
											    [torch.ones(bos_tokens_tensor.size()       ,      dtype=torch.intaa						).to(lowerCamelCase						), attn_mask]       ,      dim=1						)

									lowerCamelCase_	:     Union[str, Any] 						=      encoded_batch

									with torch.no_grad():
											lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase						).logits

									lowerCamelCase_	:     Tuple 						=      out_logits[..., :-1, :].contiguous()
									lowerCamelCase_	:     List[Any] 						=      labels[..., 1:].contiguous()
									lowerCamelCase_	:     Dict 						=      attn_mask[..., 1:].contiguous()

									lowerCamelCase_	:     Tuple 						=      torch.expa(
									    (loss_fct(shift_logits.transpose(1       ,      2						)       ,      lowerCamelCase						) * shift_attention_mask_batch).sum(1						)
									    / shift_attention_mask_batch.sum(1						)						)

									ppls += perplexity_batch.tolist()

							return {"perplexities": ppls, "mean_perplexity": np.mean(lowerCamelCase						)}







import argparse
import os
import re


_lowercase		:  List[str]			       ="""src/diffusers"""

# Pattern that looks at the indentation in a line.
_lowercase		:  str			       =re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase		:  Dict			       =re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowercase		:  Union[str, Any]			       =re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
_lowercase		:  Dict			       =re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowercase		:  Tuple			       =re.compile(r"""\[([^\]]+)\]""")


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      _re_indent.search(lowerCAmelCase__						)
		return "" if search is None else search.groups()[0]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=""							,lowerCAmelCase__=None							,lowerCAmelCase__=None						):
		lowerCamelCase_	:     Optional[Any] 						=      0
		lowerCamelCase_	:     Union[str, Any] 						=      code.split('\n'						)
		if start_prompt is not None:
				while not lines[index].startswith(lowerCAmelCase__						):
						index += 1
				lowerCamelCase_	:     Any 						=      ['\n'.join(lines[:index]						)]
		else:
				lowerCamelCase_	:     str 						=      []

		# We split into blocks until we get to the `end_prompt` (or the end of the block).
		lowerCamelCase_	:     List[str] 						=      [lines[index]]
		index += 1
		while index < len(lowerCAmelCase__						) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__						)):
				if len(lines[index]						) > 0 and get_indent(lines[index]						) == indent_level:
						if len(lowerCAmelCase__						) > 0 and get_indent(current_block[-1]						).startswith(indent_level + ' '						):
								current_block.append(lines[index]						)
								blocks.append('\n'.join(lowerCAmelCase__						)						)
								if index < len(lowerCAmelCase__						) - 1:
										lowerCamelCase_	:     int 						=      [lines[index + 1]]
										index += 1
								else:
										lowerCamelCase_	:     List[str] 						=      []
						else:
								blocks.append('\n'.join(lowerCAmelCase__						)						)
								lowerCamelCase_	:     str 						=      [lines[index]]
				else:
						current_block.append(lines[index]						)
				index += 1

		# Adds current block if it's nonempty.
		if len(lowerCAmelCase__						) > 0:
				blocks.append('\n'.join(lowerCAmelCase__						)						)

		# Add final block after end_prompt if provided.
		if end_prompt is not None and index < len(lowerCAmelCase__						):
				blocks.append('\n'.join(lines[index:]						)						)

		return blocks


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		def _inner(lowerCAmelCase__						):
				return key(lowerCAmelCase__						).lower().replace('_'							,''						)

		return _inner


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=None						):

		# If no key is provided, we use a noop.
		def noop(lowerCAmelCase__						):
				return x

		if key is None:
				lowerCamelCase_	:     int 						=      noop
		# Constants are all uppercase, they go first.
		lowerCamelCase_	:     Any 						=      [obj for obj in objects if key(lowerCAmelCase__						).isupper()]
		# Classes are not all uppercase but start with a capital, they go second.
		lowerCamelCase_	:     Dict 						=      [obj for obj in objects if key(lowerCAmelCase__						)[0].isupper() and not key(lowerCAmelCase__						).isupper()]
		# Functions begin with a lowercase, they go last.
		lowerCamelCase_	:     Any 						=      [obj for obj in objects if not key(lowerCAmelCase__						)[0].isupper()]

		lowerCamelCase_	:     Optional[Any] 						=      ignore_underscore(lowerCAmelCase__						)
		return sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						) + sorted(lowerCAmelCase__							,key=lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		# This inner function sort imports between [ ].
		def _replace(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      match.groups()[0]
				if "," not in imports:
						return F"[{imports}]"
				lowerCamelCase_	:     Optional[int] 						=      [part.strip().replace('"'							,''						) for part in imports.split(','						)]
				# We will have a final empty element if the line finished with a comma.
				if len(keys[-1]						) == 0:
						lowerCamelCase_	:     str 						=      keys[:-1]
				return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						) + "]"

		lowerCamelCase_	:     Tuple 						=      import_statement.split('\n'						)
		if len(lowerCAmelCase__						) > 3:
				# Here we have to sort internal imports that are on several lines (one per name):
				# key: [
				#     "object1",
				#     "object2",
				#     ...
				# ]

				# We may have to ignore one or two lines on each side.
				lowerCamelCase_	:     int 						=      2 if lines[1].strip() == '[' else 1
				lowerCamelCase_	:     Any 						=      [(i, _re_strip_line.search(lowerCAmelCase__						).groups()[0]) for i, line in enumerate(lines[idx:-idx]						)]
				lowerCamelCase_	:     str 						=      sort_objects(lowerCAmelCase__							,key=lambda lowerCAmelCase__						: x[1]						)
				lowerCamelCase_	:     Any 						=      [lines[x[0] + idx] for x in sorted_indices]
				return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]						)
		elif len(lowerCAmelCase__						) == 3:
				# Here we have to sort internal imports that are on one separate line:
				# key: [
				#     "object1", "object2", ...
				# ]
				if _re_bracket_content.search(lines[1]						) is not None:
						lowerCamelCase_	:     Optional[int] 						=      _re_bracket_content.sub(_replace							,lines[1]						)
				else:
						lowerCamelCase_	:     Any 						=      [part.strip().replace('"'							,''						) for part in lines[1].split(','						)]
						# We will have a final empty element if the line finished with a comma.
						if len(keys[-1]						) == 0:
								lowerCamelCase_	:     List[Any] 						=      keys[:-1]
						lowerCamelCase_	:     Optional[Any] 						=      get_indent(lines[1]						) + ', '.join([F"\"{k}\"" for k in sort_objects(lowerCAmelCase__						)]						)
				return "\n".join(lowerCAmelCase__						)
		else:
				# Finally we have to deal with imports fitting on one line
				lowerCamelCase_	:     Any 						=      _re_bracket_content.sub(_replace							,lowerCAmelCase__						)
				return import_statement


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=True						):
		with open(lowerCAmelCase__							,'r'						) as f:
				lowerCamelCase_	:     Any 						=      f.read()

		if "_import_structure" not in code:
				return

		# Blocks of indent level 0
		lowerCamelCase_	:     int 						=      split_code_in_indented_blocks(
		    lowerCAmelCase__							,start_prompt='_import_structure = {'							,end_prompt='if TYPE_CHECKING:'						)

		# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
		for block_idx in range(1							,len(lowerCAmelCase__						) - 1						):
				# Check if the block contains some `_import_structure`s thingy to sort.
				lowerCamelCase_	:     Any 						=      main_blocks[block_idx]
				lowerCamelCase_	:     Tuple 						=      block.split('\n'						)

				# Get to the start of the imports.
				lowerCamelCase_	:     Optional[int] 						=      0
				while line_idx < len(lowerCAmelCase__						) and "_import_structure" not in block_lines[line_idx]:
						# Skip dummy import blocks
						if "import dummy" in block_lines[line_idx]:
								lowerCamelCase_	:     List[Any] 						=      len(lowerCAmelCase__						)
						else:
								line_idx += 1
				if line_idx >= len(lowerCAmelCase__						):
						continue

				# Ignore beginning and last line: they don't contain anything.
				lowerCamelCase_	:     Tuple 						=      '\n'.join(block_lines[line_idx:-1]						)
				lowerCamelCase_	:     Dict 						=      get_indent(block_lines[1]						)
				# Slit the internal block into blocks of indent level 1.
				lowerCamelCase_	:     Dict 						=      split_code_in_indented_blocks(lowerCAmelCase__							,indent_level=lowerCAmelCase__						)
				# We have two categories of import key: list or _import_structure[key].append/extend
				lowerCamelCase_	:     List[str] 						=      _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
				# Grab the keys, but there is a trap: some lines are empty or just comments.
				lowerCamelCase_	:     Tuple 						=      [(pattern.search(lowerCAmelCase__						).groups()[0] if pattern.search(lowerCAmelCase__						) is not None else None) for b in internal_blocks]
				# We only sort the lines with a key.
				lowerCamelCase_	:     Any 						=      [(i, key) for i, key in enumerate(lowerCAmelCase__						) if key is not None]
				lowerCamelCase_	:     Optional[Any] 						=      [x[0] for x in sorted(lowerCAmelCase__							,key=lambda lowerCAmelCase__						: x[1]						)]

				# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
				lowerCamelCase_	:     int 						=      0
				lowerCamelCase_	:     Dict 						=      []
				for i in range(len(lowerCAmelCase__						)						):
						if keys[i] is None:
								reordered_blocks.append(internal_blocks[i]						)
						else:
								lowerCamelCase_	:     Tuple 						=      sort_objects_in_import(internal_blocks[sorted_indices[count]]						)
								reordered_blocks.append(lowerCAmelCase__						)
								count += 1

        # And we put our main block back together with its first and last line.
				lowerCamelCase_	:     Tuple 						=      '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]						)

		if code != "\n".join(lowerCAmelCase__						):
				if check_only:
						return True
				else:
						print(F"Overwriting {file}."						)
						with open(lowerCAmelCase__							,'w'						) as f:
								f.write('\n'.join(lowerCAmelCase__						)						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__=True						):
		lowerCamelCase_	:     Dict 						=      []
		for root, _, files in os.walk(lowerCAmelCase__						):
				if "__init__.py" in files:
						lowerCamelCase_	:     Optional[int] 						=      sort_imports(os.path.join(lowerCAmelCase__							,'__init__.py'						)							,check_only=lowerCAmelCase__						)
						if result:
								lowerCamelCase_	:     Dict 						=      [os.path.join(lowerCAmelCase__							,'__init__.py'						)]
		if len(lowerCAmelCase__						) > 0:
				raise ValueError(F"Would overwrite {len(lowerCAmelCase__						)} files, run `make style`."						)


if __name__ == "__main__":
		_lowercase		:  int			       =argparse.ArgumentParser()
		parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
		_lowercase		:  Union[str, Any]			       =parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)
