
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__a         =   logging.getLogger(__name__)
@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,		)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether tp freeze the encoder.'}				)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether  to freeze the embeddings.'}				)

@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
	a     :Optional[str]       	=      field(
	    default='summarization'					,		metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}					,		)
	a     :Optional[int]       	=      field(
	    default=1_024					,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=128					,		metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded. '
	            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
	            'during ``evaluate`` and ``predict``.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for test target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# training examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# validation examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# test examples. -1 means use all.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Source language id for translation.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Target language id for translation.'}				)
	a     :Optional[int]       	=      field(default=UpperCAmelCase					,		metadata={'help': '# num_beams to use for evaluation.'}				)
	a     :bool       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}					,		)




def 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				logger.info(F'''***** {split} metrics *****'''   )
				for key in sorted(metrics.keys()   ):
								logger.info(F'''  {key} = {metrics[key]}'''   )
				save_json(snake_case__		,  os.path.join(snake_case__		,  F'''{split}_results.json'''   )   )




def 							a		(				):


				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowercase_	    =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

				if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_args_into_dataclasses()

				check_output_dir(snake_case__   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,  datefmt='''%m/%d/%Y %H:%M:%S'''		,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,  training_args.local_rank		,  training_args.device		,  training_args.n_gpu		,  bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )		,  training_args.fpaa		,  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank   ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''		,  snake_case__   )

				# Set seed
				set_seed(training_args.seed   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase_	    =   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )

				lowercase_	    =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
				for p in extra_model_params:
								if getattr(snake_case__		,  snake_case__		,  snake_case__   ):
												assert hasattr(snake_case__		,  snake_case__   ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
												setattr(snake_case__		,  snake_case__		,  getattr(snake_case__		,  snake_case__   )   )

				lowercase_	    =   AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )
				lowercase_	    =   AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path		,  from_tf='''.ckpt''' in model_args.model_name_or_path		,  config=snake_case__		,  cache_dir=model_args.cache_dir		,  )

				# use task specific params
				use_task_specific_params(snake_case__		,  data_args.task   )

				# set num_beams for evaluation
				if data_args.eval_beams is None:
								lowercase_	    =   model.config.num_beams

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(snake_case__		,  (MBartTokenizer, MBartTokenizerFast)   ):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(snake_case__		,  snake_case__   ):
												lowercase_	    =   tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												lowercase_	    =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

				if model_args.freeze_embeds:
								freeze_embeds(snake_case__   )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()   )
								assert_all_frozen(model.get_encoder()   )

				lowercase_	    =   SeqaSeqDataset

				# Get datasets
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''train'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_train		,  max_target_length=data_args.max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_train
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''val'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_val		,  max_target_length=data_args.val_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''test'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_test		,  max_target_length=data_args.test_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				lowercase_	    =   (
				    build_compute_metrics_fn(data_args.task		,  snake_case__   ) if training_args.predict_with_generate else None
				)
				lowercase_	    =   SeqaSeqTrainer(
				    model=snake_case__		,  args=snake_case__		,  data_args=snake_case__		,  train_dataset=snake_case__		,  eval_dataset=snake_case__		,  data_collator=SeqaSeqDataCollator(
				        snake_case__		,  snake_case__		,  model.config.decoder_start_token_id		,  training_args.tpu_num_cores   )		,  compute_metrics=snake_case__		,  tokenizer=snake_case__		,  )

				lowercase_	    =   {}
				# Training
				if training_args.do_train:
								logger.info('''*** Train ***'''   )

								lowercase_	    =   trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
								lowercase_	    =   train_result.metrics
								lowercase_	    =   data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

								if trainer.is_world_process_zero():
												handle_metrics('''train'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir		,  '''trainer_state.json'''   )   )

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''   )

								lowercase_	    =   trainer.evaluate(metric_key_prefix='''val'''   )
								lowercase_	    =   data_args.n_val
								lowercase_	    =   round(metrics['''val_loss''']		,  4   )

								if trainer.is_world_process_zero():
												handle_metrics('''val'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

				if training_args.do_predict:
								logger.info('''*** Predict ***'''   )

								lowercase_	    =   trainer.predict(test_dataset=snake_case__		,  metric_key_prefix='''test'''   )
								lowercase_	    =   test_output.metrics
								lowercase_	    =   data_args.n_test

								if trainer.is_world_process_zero():
												lowercase_	    =   round(metrics['''test_loss''']		,  4   )
												handle_metrics('''test'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												if training_args.predict_with_generate:
																lowercase_	    =   tokenizer.batch_decode(
																    test_output.predictions		,  skip_special_tokens=snake_case__		,  clean_up_tokenization_spaces=snake_case__   )
																lowercase_	    =   lmap(str.strip		,  snake_case__   )
																write_txt_file(snake_case__		,  os.path.join(training_args.output_dir		,  '''test_generations.txt'''   )   )

				if trainer.is_world_process_zero():
								save_json(snake_case__		,  os.path.join(training_args.output_dir		,  '''all_results.json'''   )   )

				return all_metrics




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()











'''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
__lowerCAmelCase				:      Optional[Any]						      ="src/diffusers"
__lowerCAmelCase				:      Dict						      ="."


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





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Optional[Any]			):
    return line.startswith(_lowerCamelCase			) or len(_lowerCamelCase			) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"		,			_lowerCamelCase			) is not None





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     object_name.split("."			)
    A__							=     0

    # First let's find the module where our object lives.
    A__							=     parts[i]
    while i < len(_lowerCamelCase			) and not os.path.isfile(os.path.join(_lowerCamelCase		,			F"{module}.py"			)			):
        i += 1
        if i < len(_lowerCamelCase			):
            A__							=     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:
        A__							=     f.readlines()

    # Now let's find the class / func in the code!
    A__							=     ""
    A__							=     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).
    A__							=     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

    A__							=     lines[start_index:line_index]
    return "".join(_lowerCamelCase			)


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





def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]			):
    A__							=     code.split("\n"			)
    A__							=     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 		UpperCamelCase							(				_lowerCamelCase   :   Tuple			):
    A__							=     len(get_indent(_lowerCamelCase			)			) > 0
    if has_indent:
        A__							=     F"class Bla:\n{code}"
    A__							=     black.Mode(target_versions={black.TargetVersion.PYaa}		,			line_length=1_19		,			preview=_lowerCamelCase			)
    A__							=     black.format_str(_lowerCamelCase		,			mode=_lowerCamelCase			)
    A__,      A__							=     style_docstrings_in_code(_lowerCamelCase			)
    return result[len("class Bla:\n"			) :] if has_indent else result





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]=False			):
    with open(_lowerCamelCase		,			"r"		,			encoding="utf-8"		,			newline="\n"			) as f:
        A__							=     f.readlines()
    A__							=     []
    A__							=     0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(_lowerCamelCase			):
        A__							=     _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.
        A__,      A__,      A__							=     search.groups()
        A__							=     find_code_in_diffusers(_lowerCamelCase			)
        A__							=     get_indent(_lowerCamelCase			)

        A__							=     line_index + 1 if indent == theoretical_indent else line_index + 2
        A__							=     theoretical_indent
        A__							=     start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        A__							=     True
        while line_index < len(_lowerCamelCase			) and should_continue:
            line_index += 1
            if line_index >= len(_lowerCamelCase			):
                break
            A__							=     lines[line_index]
            A__							=     _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

        A__							=     lines[start_index:line_index]
        A__							=     "".join(_lowerCamelCase			)

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

        # Before comparing, use the `replace_pattern` on the original code.
        if len(_lowerCamelCase			) > 0:
            A__							=     replace_pattern.replace("with"		,			""			).split(","			)
            A__							=     [_re_replace_pattern.search(_lowerCamelCase			) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                A__,      A__,      A__							=     pattern.groups()
                A__							=     re.sub(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)
                if option.strip() == "all-casing":
                    A__							=     re.sub(obja.lower()		,			obja.lower()		,			_lowerCamelCase			)
                    A__							=     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
            A__							=     blackify(lines[start_index - 1] + theoretical_code			)
            A__							=     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:
                A__							=     lines[:start_index] + [theoretical_code] + lines[line_index:]
                A__							=     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 		UpperCamelCase							(				_lowerCamelCase   :   bool = False			):
    A__							=     glob.glob(os.path.join(_lowerCamelCase		,			"**/*.py"			)		,			recursive=_lowerCamelCase			)
    A__							=     []
    for filename in all_files:
        A__							=     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:
        A__							=     "\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__":
  __lowerCAmelCase				:      Union[str, Any]						      =argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  __lowerCAmelCase				:      int						      =parser.parse_args()

  check_copies(args.fix_and_overwrite)



