


'''simple docstring'''







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand







def _UpperCamelCase       (     __A    )							->		Union[str, Any]:


      '''simple docstring'''



      return EnvironmentCommand()



class 					lowercase_     (    a__   ):


    @staticmethod
    def    __a							(     a    ):
          UpperCamelCase__										=				parser.add_parser("env"    )
          download_parser.set_defaults(func=a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				huggingface_hub.__version__

          UpperCamelCase__										=				"not installed"
          UpperCamelCase__										=				"NA"
          if is_torch_available():
                import torch

                UpperCamelCase__										=				torch.__version__
                UpperCamelCase__										=				torch.cuda.is_available()

          UpperCamelCase__										=				"not installed"
          if is_transformers_available():
                import transformers

                UpperCamelCase__										=				transformers.__version__

          UpperCamelCase__										=				"not installed"
          if is_accelerate_available():
                import accelerate

                UpperCamelCase__										=				accelerate.__version__

          UpperCamelCase__										=				"not installed"
          if is_xformers_available():
                import xformers

                UpperCamelCase__										=				xformers.__version__

          UpperCamelCase__										=				{
              "`diffusers` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "PyTorch version (GPU?)": f'''{pt_version} ({pt_cuda_available})''',
              "Huggingface_hub version": hub_version,
              "Transformers version": transformers_version,
              "Accelerate version": accelerate_version,
              "xFormers version": xformers_version,
              "Using GPU in script?": "<fill in>",
              "Using distributed or parallel set-up in script?": "<fill in>",
          }

          print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"    )
          print(self.format_dict(a    )    )

          return info


    @staticmethod
    def    __a							(     a    ):
          return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]    ) + "\n"









'''simple docstring'''







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


A__:    int					    =       logging.getLogger(__name__)







@dataclass
class A__       :
      __UpperCamelCase       :  str
      __UpperCamelCase       :  List[str]
      __UpperCamelCase       :  Optional[List[str]]







@dataclass
class A__       :
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  Optional[List[int]]	       =							None
      __UpperCamelCase       :  Optional[List[int]]	       =							None







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str	       =							"train"
      __UpperCamelCase       :  Tuple	       =							"dev"
      __UpperCamelCase       :  str	       =							"test"







class A__       :






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:Union[Split, str]	)       ->					List[InputExample]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:str	)       ->					List[str]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:List[InputExample]     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:int     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Optional[Any]="[CLS]"     ,						SCREAMING_SNAKE_CASE			:Optional[int]=1     ,						SCREAMING_SNAKE_CASE			:Any="[SEP]"     ,						SCREAMING_SNAKE_CASE			:List[Any]=False     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=False     ,						SCREAMING_SNAKE_CASE			:List[str]=0     ,						SCREAMING_SNAKE_CASE			:str=0     ,						SCREAMING_SNAKE_CASE			:Dict=-1_0_0     ,						SCREAMING_SNAKE_CASE			:Optional[int]=0     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						)       ->					List[InputFeatures]:




             '''simple docstring'''
             _a    : str     					={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE	)}

             _a    : Tuple     					=[]
             for ex_index, example in enumerate(SCREAMING_SNAKE_CASE	):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info("""Writing example %d of %d"""     ,						SCREAMING_SNAKE_CASE     ,						len(SCREAMING_SNAKE_CASE	)	)

                    _a    : Optional[Any]     					=[]
                    _a    : List[Any]     					=[]
                    for word, label in zip(example.words     ,						example.labels	):
                           _a    : Optional[int]     					=tokenizer.tokenize(SCREAMING_SNAKE_CASE	)

                           # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                           if len(SCREAMING_SNAKE_CASE	) > 0:
                                  tokens.extend(SCREAMING_SNAKE_CASE	)
                                  # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                                  label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(SCREAMING_SNAKE_CASE	) - 1)	)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                    _a    : Optional[int]     					=tokenizer.num_special_tokens_to_add()
                    if len(SCREAMING_SNAKE_CASE	) > max_seq_length - special_tokens_count:
                           _a    : List[Any]     					=tokens[: (max_seq_length - special_tokens_count)]
                           _a    : Tuple     					=label_ids[: (max_seq_length - special_tokens_count)]

                    # The convention in BERT is:
                    # (a) For sequence pairs:
                    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                    #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                    # (b) For single sequences:
                    #  tokens:   [CLS] the dog is hairy . [SEP]
                    #  type_ids:   0   0   0   0  0     0   0
                    #
                    # Where "type_ids" are used to indicate whether this is the first
                    # sequence or the second sequence. The embedding vectors for `type=0` and
                    # `type=1` were learned during pre-training and are added to the wordpiece
                    # embedding vector (and position vector). This is not *strictly* necessary
                    # since the [SEP] token unambiguously separates the sequences, but it makes
                    # it easier for the model to learn the concept of sequences.
                    #
                    # For classification tasks, the first vector (corresponding to [CLS]) is
                    # used as the "sentence vector". Note that this only makes sense because
                    # the entire model is fine-tuned.
                    tokens += [sep_token]
                    label_ids += [pad_token_label_id]
                    if sep_token_extra:
                           # roberta uses an extra separator b/w pairs of sentences
                           tokens += [sep_token]
                           label_ids += [pad_token_label_id]
                    _a    : Dict     					=[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE	)

                    if cls_token_at_end:
                           tokens += [cls_token]
                           label_ids += [pad_token_label_id]
                           segment_ids += [cls_token_segment_id]
                    else:
                           _a    : Any     					=[cls_token] + tokens
                           _a    : Dict     					=[pad_token_label_id] + label_ids
                           _a    : Union[str, Any]     					=[cls_token_segment_id] + segment_ids

                    _a    : List[str]     					=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)

                    # The mask has 1 for real tokens and 0 for padding tokens. Only real
                    # tokens are attended to.
                    _a    : Optional[int]     					=[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE	)

                    # Zero-pad up to the sequence length.
                    _a    : Union[str, Any]     					=max_seq_length - len(SCREAMING_SNAKE_CASE	)
                    if pad_on_left:
                           _a    : Optional[Any]     					=([pad_token] * padding_length) + input_ids
                           _a    : Optional[int]     					=([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                           _a    : Union[str, Any]     					=([pad_token_segment_id] * padding_length) + segment_ids
                           _a    : Dict     					=([pad_token_label_id] * padding_length) + label_ids
                    else:
                           input_ids += [pad_token] * padding_length
                           input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                           segment_ids += [pad_token_segment_id] * padding_length
                           label_ids += [pad_token_label_id] * padding_length

                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length

                    if ex_index < 5:
                           logger.info("""*** Example ***"""	)
                           logger.info("""guid: %s"""     ,						example.guid	)
                           logger.info("""tokens: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in tokens]	)	)
                           logger.info("""input_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in input_ids]	)	)
                           logger.info("""input_mask: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in input_mask]	)	)
                           logger.info("""segment_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in segment_ids]	)	)
                           logger.info("""label_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in label_ids]	)	)

                    if "token_type_ids" not in tokenizer.model_input_names:
                           _a    : Tuple     					=None

                    features.append(
                        InputFeatures(
                            input_ids=SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE     ,						token_type_ids=SCREAMING_SNAKE_CASE     ,						label_ids=SCREAMING_SNAKE_CASE	)	)
             return features


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset







   class A__       (						UpperCAmelCase__      ):
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							nn.CrossEntropyLoss().ignore_index






         def __init__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:TokenClassificationTask     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:int=False     ,						SCREAMING_SNAKE_CASE			:Split = Split.train     ,						)       ->					List[str]:




                '''simple docstring'''
                # Load data features from cache or dataset file
                _a    : Optional[Any]     					=os.path.join(
                    SCREAMING_SNAKE_CASE     ,						"""cached_{}_{}_{}""".format(mode.value     ,						tokenizer.__class__.__name__     ,						str(SCREAMING_SNAKE_CASE	)	)     ,						)

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                _a    : List[str]     					=cached_features_file + """.lock"""
                with FileLock(SCREAMING_SNAKE_CASE	):
                       if os.path.exists(SCREAMING_SNAKE_CASE	) and not overwrite_cache:
                              logger.info(f"Loading features from cached file {cached_features_file}"	)
                              _a    : Any     					=torch.load(SCREAMING_SNAKE_CASE	)
                       else:
                              logger.info(f"Creating features from dataset file at {data_dir}"	)
                              _a    : Any     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                              # TODO clean up all this to leverage built-in features of tokenizers
                              _a    : List[str]     					=token_classification_task.convert_examples_to_features(
                                  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						cls_token_at_end=bool(model_type in ["""xlnet"""]	)     ,						cls_token=tokenizer.cls_token     ,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,						sep_token=tokenizer.sep_token     ,						sep_token_extra=SCREAMING_SNAKE_CASE     ,						pad_on_left=bool(tokenizer.padding_side == """left"""	)     ,						pad_token=tokenizer.pad_token_id     ,						pad_token_segment_id=tokenizer.pad_token_type_id     ,						pad_token_label_id=self.pad_token_label_id     ,						)
                              logger.info(f"Saving features into cached file {cached_features_file}"	)
                              torch.save(self.features     ,						SCREAMING_SNAKE_CASE	)






         def __len__( self			:Optional[int]	)       ->					Union[str, Any]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:int	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


if is_tf_available():
   import tensorflow as tf







   class A__       :
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							-100






         def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:TokenClassificationTask     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Split = Split.train     ,						)       ->					Any:




                '''simple docstring'''
                _a    : Tuple     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                # TODO clean up all this to leverage built-in features of tokenizers
                _a    : List[Any]     					=token_classification_task.convert_examples_to_features(
                    SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						cls_token_at_end=bool(model_type in ["""xlnet"""]	)     ,						cls_token=tokenizer.cls_token     ,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,						sep_token=tokenizer.sep_token     ,						sep_token_extra=SCREAMING_SNAKE_CASE     ,						pad_on_left=bool(tokenizer.padding_side == """left"""	)     ,						pad_token=tokenizer.pad_token_id     ,						pad_token_segment_id=tokenizer.pad_token_type_id     ,						pad_token_label_id=self.pad_token_label_id     ,						)

                def gen():
                       for ex in self.features:
                              if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                              else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                if "token_type_ids" not in tokenizer.model_input_names:
                       _a    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,						(
                               {"""input_ids""": tf.TensorShape([None]	), """attention_mask""": tf.TensorShape([None]	)},
                               tf.TensorShape([None]	),
                           )     ,						)
                else:
                       _a    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,						(
                               {
                                   """input_ids""": tf.TensorShape([None]	),
                                   """attention_mask""": tf.TensorShape([None]	),
                                   """token_type_ids""": tf.TensorShape([None]	),
                               },
                               tf.TensorShape([None]	),
                           )     ,						)






         def __UpperCAmelCase       ( self			:Tuple	)       ->					Any:




                '''simple docstring'''
                _a    : List[Any]     					=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features	)	)	)

                return self.dataset






         def __len__( self			:str	)       ->					Optional[int]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:int     ,						SCREAMING_SNAKE_CASE			:str	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


