from dataclasses import dataclass, field
from typing import Optional




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot'''						,metadata={'''help''': '''Model name or path of model to be trained.'''} )
			__a  : Optional[str]			 =						field(
			    default='''./'''						,metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot-clean-train'''						,metadata={'''help''': '''Name or path of training dataset.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot-clean-valid'''						,metadata={'''help''': '''Name or path of validation dataset.'''} )
			__a  : Optional[int]			 =						field(default=2						,metadata={'''help''': '''Batch size for training.'''} )
			__a  : Optional[int]			 =						field(default=2						,metadata={'''help''': '''Batch size for evaluation.'''} )
			__a  : Optional[float]			 =						field(default=0.1						,metadata={'''help''': '''Value of weight decay.'''} )
			__a  : Optional[int]			 =						field(
			    default=10000						,metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''} )
			__a  : Optional[float]			 =						field(default=2e-4						,metadata={'''help''': '''Learning rate fo training.'''} )
			__a  : Optional[str]			 =						field(default='''cosine'''						,metadata={'''help''': '''Learning rate.'''} )
			__a  : Optional[int]			 =						field(
			    default=750						,metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''} )
			__a  : Optional[int]			 =						field(
			    default=16						,metadata={'''help''': '''Number of gradient accumulation steps.'''} )
			__a  : Optional[bool]			 =						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''} )
			__a  : Optional[int]			 =						field(default=50000						,metadata={'''help''': '''Maximum number of training steps.'''} )
			__a  : Optional[int]			 =						field(
			    default=-1						,metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''} )
			__a  : Optional[int]			 =						field(default=1024						,metadata={'''help''': '''Sequence lengths used for training.'''} )
			__a  : Optional[int]			 =						field(default=1						,metadata={'''help''': '''Training seed.'''} )
			__a  : Optional[int]			 =						field(
			    default=1024						,metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}						,)
			__a  : Optional[str]			 =						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''} )
			__a  : Optional[bool]			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''If True the data is pretokenized.'''} )




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot'''						,metadata={'''help''': '''Model name or path of model to be evaluated.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot-clean-valid'''						,metadata={'''help''': '''Name or path of validation dataset.'''} )
			__a  : Optional[int]			 =						field(default=2						,metadata={'''help''': '''Batch size used for evaluation.'''} )
			__a  : Optional[int]			 =						field(
			    default=-1						,metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''} )
			__a  : Optional[int]			 =						field(default=1024						,metadata={'''help''': '''Length of sequences to be evaluated.'''} )
			__a  : Optional[int]			 =						field(default=1						,metadata={'''help''': '''Random seed used for evaluation.'''} )




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot'''						,metadata={'''help''': '''Model name or path of model to be evaluated.'''} )
			__a  : Optional[int]			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''Number of workers used for code evaluation.'''} )
			__a  : Optional[int]			 =						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}						,)
			__a  : Optional[bool]			 =						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Sample from the language model\'s output distribution.'''} )
			__a  : Optional[float]			 =						field(default=0.2						,metadata={'''help''': '''Sampling temperature used for generation.'''} )
			__a  : Optional[int]			 =						field(default=256						,metadata={'''help''': '''Maximum number of newly generated tokens.'''} )
			__a  : Optional[int]			 =						field(default=0						,metadata={'''help''': '''Top-k parameter used for generation.'''} )
			__a  : Optional[float]			 =						field(default=0.95						,metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''} )
			__a  : Optional[int]			 =						field(default=10						,metadata={'''help''': '''Number of generations to run in parallel.'''} )
			__a  : Optional[int]			 =						field(
			    default=200						,metadata={'''help''': '''Number of completions to generate for each sample.'''} )
			__a  : Optional[int]			 =						field(default=1						,metadata={'''help''': '''Random seed used for evaluation.'''} )
			__a  : Optional[str]			 =						field(
			    default='''eval_results.json'''						,metadata={'''help''': '''Random seed used for evaluation.'''} )
			__a  : Optional[str]			 =						field(
			    default='''0'''						,metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''} )
			__a  : Optional[int]			 =						field(
			    default=-1						,metadata={
			        '''help''': (
			            '''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
			            ''' number corresponds to which GPU device id to run on.'''
			        )
			    }						,)




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[int]			 =						field(
			    default=_UpperCAmelCase						,metadata={
			        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
			    }						,)
			__a  : Optional[str]			 =						field(
			    default='''transformersbook/codeparrot'''						,metadata={'''help''': '''Folder or name of dataset to process.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot-clean'''						,metadata={'''help''': '''Folder to save processed processed dataset.'''} )
			__a  : Optional[int]			 =						field(
			    default=100000						,metadata={'''help''': '''Number of files to save per JSON output file.'''} )
			__a  : Optional[str]			 =						field(default='''content'''						,metadata={'''help''': '''Column containing text data to process.'''} )
			__a  : Optional[float]			 =						field(
			    default=1000						,metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''} )
			__a  : Optional[float]			 =						field(
			    default=100						,metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''} )
			__a  : Optional[float]			 =						field(
			    default=0.25						,metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''} )
			__a  : Optional[float]			 =						field(
			    default=1.5						,metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''} )
			__a  : Optional[float]			 =						field(
			    default=0.7						,metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot'''						,metadata={'''help''': '''Name or path to the tokenizer.'''}						,)
			__a  : Optional[bool]			 =						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''If True, near-duplicate samples are removed.'''} )
			__a  : Optional[float]			 =						field(
			    default=0.85						,metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''} )




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[str]			 =						field(
			    default='''gpt2'''						,metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''} )
			__a  : Optional[str]			 =						field(
			    default='''transformersbook/codeparrot-train'''						,metadata={'''help''': '''Dataset to train tokenizer on.'''} )
			__a  : Optional[str]			 =						field(default='''content'''						,metadata={'''help''': '''Column containing text data to process.'''} )
			__a  : Optional[int]			 =						field(default=200000						,metadata={'''help''': '''Number of examples to train tokenizer on.'''} )
			__a  : Optional[int]			 =						field(
			    default=32768						,metadata={'''help''': '''Number of examples to train the tokenizer on.'''} )
			__a  : Optional[str]			 =						field(default='''codeparrot'''						,metadata={'''help''': '''Name of new tokenizer.'''} )
			__a  : Optional[bool]			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''Push saved tokenizer to the hub.'''} )




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot'''						,metadata={'''help''': '''Name or path to the tokenizer.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot-clean-train'''						,metadata={'''help''': '''Name or path to the dataset to pretokenize.'''} )
			__a  : Optional[str]			 =						field(
			    default='''tokenized-codeparrot-train'''						,metadata={'''help''': '''Repo name of the pretokenized data.'''} )
			__a  : Optional[int]			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''Number of workers used for code evaluation.'''} )




@dataclass
class    _UpperCamelCase      :


			"""simple docstring"""






			__a  : Optional[str]			 =						field(
			    default='''gpt2-large'''						,metadata={'''help''': '''Configuration to use for model initialization.'''} )
			__a  : Optional[str]			 =						field(
			    default='''codeparrot/codeparrot'''						,metadata={'''help''': '''Tokenizer attached to model.'''} )
			__a  : Optional[str]			 =						field(default='''codeparrot'''						,metadata={'''help''': '''Name of the created model.'''} )
			__a  : Optional[bool]			 =						field(default=_UpperCAmelCase						,metadata={'''help''': '''Push saved tokenizer to the hub.'''} )

import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     int       	=      a.name
       lowercase							:     Any       	=      b.name

       lowercase							:     Optional[Any]       	=      """"""
       lowercase							:     Dict       	=      """"""

       lowercase							:     int       	=      a == b

       lowercase							:     int       	=      name_a
       lowercase							:     List[str]       	=      name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       for i, input_name in enumerate(node_proto.input				):
              if input_name == name:
                     node_proto.input.insert(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     node_proto.input.pop(i + 1				)

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              _graph_replace_input_with(node_proto.attribute[1].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for n in graph_proto.node:
              _node_replace_input_with(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Any       	=      list(model.graph.initializer				)
       lowercase							:     Dict       	=      list(model_without_ext.graph.initializer				)
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              lowercase							:     Union[str, Any]       	=      inits[i].name
              lowercase							:     Dict       	=      inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]				)

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Union[str, Any]       	=      os.path.dirname(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      os.path.basename(SCREAMING_SNAKE_CASE__				)

       lowercase							:     str       	=      onnx.load(os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)				)

       lowercase							:     List[str]       	=      list(model.graph.initializer				)

       lowercase							:     Tuple       	=      set()
       lowercase							:     int       	=      {}
       lowercase							:     Optional[Any]       	=      []

       lowercase							:     Dict       	=      0

       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              if i in dup_set:
                     continue

              for j in range(i + 1					,	len(SCREAMING_SNAKE_CASE__				)				):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]					,	inits[j]				):
                            dup_set.add(SCREAMING_SNAKE_CASE__				)
                            dup_set.add(SCREAMING_SNAKE_CASE__				)

                            lowercase							:     int       	=      inits[j].data_type
                            lowercase							:     Optional[int]       	=      numpy.prod(inits[j].dims				)
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print("""unexpected data type: """					,	SCREAMING_SNAKE_CASE__				)
                            total_reduced_size += mem_size

                            lowercase							:     Tuple       	=      inits[i].name
                            lowercase							:     int       	=      inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   lowercase							:     List[str]       	=      [name_j]
                            ind_to_replace.append((j, i)				)

       print("""total reduced size: """					,	total_reduced_size / 1_024 / 1_024 / 1_024					,	"""GB"""				)

       lowercase							:     str       	=      sorted(SCREAMING_SNAKE_CASE__				)
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      """optimized_""" + model_file_name
       lowercase							:     Dict       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       onnx.save(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return new_model
