




"""simple docstring"""

import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase	:      Optional[int]   =							logging.getLogger(__name__)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''						)
	parser.add_argument('''--model_name'''	,   type=a_	,   default='''transfo-xl-wt103'''	,   help='''pretrained model name'''						)
	parser.add_argument(
	    '''--split'''	,   type=a_	,   default='''test'''	,   choices=['''all''', '''valid''', '''test''']	,   help='''which split to evaluate'''						)
	parser.add_argument('''--batch_size'''	,   type=a_	,   default=10	,   help='''batch size'''						)
	parser.add_argument('''--tgt_len'''	,   type=a_	,   default=128	,   help='''number of tokens to predict'''						)
	parser.add_argument('''--ext_len'''	,   type=a_	,   default=0	,   help='''length of the extended context'''						)
	parser.add_argument('''--mem_len'''	,   type=a_	,   default=1600	,   help='''length of the retained previous heads'''						)
	parser.add_argument('''--clamp_len'''	,   type=a_	,   default=1000	,   help='''max positional embedding index'''						)
	parser.add_argument('''--no_cuda'''	,   action='''store_true'''	,   help='''Do not use CUDA even though CUA is available'''						)
	parser.add_argument('''--work_dir'''	,   type=a_	,   required=a_	,   help='''path to the work_dir'''						)
	parser.add_argument('''--no_log'''	,   action='''store_true'''	,   help='''do not log the eval result'''						)
	parser.add_argument('''--same_length'''	,   action='''store_true'''	,   help='''set same length attention with masking'''						)
	parser.add_argument('''--server_ip'''	,   type=a_	,   default=''''''	,   help='''Can be used for distant debugging.'''						)
	parser.add_argument('''--server_port'''	,   type=a_	,   default=''''''	,   help='''Can be used for distant debugging.'''						)
	A_	:					List[Any]						 =		parser.parse_args()
	assert args.ext_len >= 0, "extended context length must be non-negative"

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print('''Waiting for debugger attach'''						)
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,   redirect_output=a_						)
		ptvsd.wait_for_attach()

	A_	:					Any						 =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
	logger.info('''device: {}'''.format(a_						)						)

	# Load a pre-processed dataset
	# You can also build the corpus yourself using TransfoXLCorpus methods
	# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
	# and tokenizing the dataset
	# The pre-processed corpus is a convertion (using the conversion script )
	A_	:					Optional[Any]						 =		TransfoXLCorpus.from_pretrained(args.model_name						)

	A_	:					List[str]						 =		corpus.get_iterator('''valid'''	,   args.batch_size	,   args.tgt_len	,   device=a_	,   ext_len=args.ext_len						)
	A_	:					Union[str, Any]						 =		corpus.get_iterator('''test'''	,   args.batch_size	,   args.tgt_len	,   device=a_	,   ext_len=args.ext_len						)

	# Load a pre-trained model
	A_	:					Any						 =		TransfoXLLMHeadModel.from_pretrained(args.model_name						)
	model.to(a_						)

	logger.info(
	    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
	        args.batch_size	,   args.tgt_len	,   args.ext_len	,   args.mem_len	,   args.clamp_len						)						)

	model.reset_memory_length(args.mem_len						)
	if args.clamp_len > 0:
		A_	:					Dict						 =		args.clamp_len
	if args.same_length:
		A_	:					List[Any]						 =		True

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(_UpperCAmelCase						):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		A_ ,			A_	:					str						 =		0, 0.0
		A_	:					int						 =		time.time()
		with torch.no_grad():
			A_	:					Any						 =		None
			for idx, (data, target, seq_len) in enumerate(a_						):
				A_	:					Union[str, Any]						 =		model(a_	,   lm_labels=a_	,   mems=a_						)
				A_ ,			A_ ,			A_	:					int						 =		ret
				A_	:					Optional[int]						 =		loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			A_	:					List[Any]						 =		time.time() - start_time
		logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(a_	,   1000 * total_time / (idx + 1)						)						)
		return total_loss / total_len

	# Run on test data.
	if args.split == "all":
		A_	:					Optional[int]						 =		evaluate(a_						)
		A_	:					Optional[Any]						 =		evaluate(a_						)
	elif args.split == "valid":
		A_	:					List[str]						 =		evaluate(a_						)
		A_	:					Dict						 =		None
	elif args.split == "test":
		A_	:					str						 =		evaluate(a_						)
		A_	:					str						 =		None

	def format_log(_UpperCAmelCase	,   _UpperCAmelCase						):
		A_	:					int						 =		'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(a_	,   a_	,   math.exp(a_						)						)
		return log_str

	A_	:					Optional[Any]						 =		''''''
	if valid_loss is not None:
		log_str += format_log(a_	,   '''valid'''						)
	if test_loss is not None:
		log_str += format_log(a_	,   '''test'''						)

	logger.info('''=''' * 100						)
	logger.info(a_						)
	logger.info('''=''' * 100						)


if __name__ == "__main__":
		main()







"""simple docstring"""







import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


SCREAMING_SNAKE_CASE_  					= {}
for task, _ in pipeline_test_mapping.items():
  SCREAMING_SNAKE_CASE_  					= {'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
SCREAMING_SNAKE_CASE_  					= {
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							str:

   if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "pt"
   elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "tf"
   elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "flax"
   else:
      return None
def       __UpperCAmelCase		(		a_      :		Optional[Any]     ,	a_      :		int					)  ->							List[Any]:
   # Use the cached results
   if PIPELINE_TEST_MAPPING[task].get(a_     ,	a_					) is not None:
      return PIPELINE_TEST_MAPPING[task][framework]

   _UpperCAmelCase 						=       pipeline_test_mapping[task]["test"]
   _UpperCAmelCase 						=       None

   if framework == "pt":
      _UpperCAmelCase 						=       getattr(a_     ,	"model_mapping"     ,	a_					)
   elif framework == "tf":
      _UpperCAmelCase 						=       getattr(a_     ,	"tf_model_mapping"     ,	a_					)

   if mapping is not None:
      _UpperCAmelCase 						=       dict(mapping.items()					)

   # cache the results
   _UpperCAmelCase 						=       mapping
   return mapping
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework is None:
      return None
   _UpperCAmelCase 						=       get_mapping_for_task(a_     ,	a_					)
   if mapping is None:
      return None

   _UpperCAmelCase 						=       list({model_class.config_class for model_class in test_class.all_model_classes}					)
   if len(a_					) != 1:
      raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`."					)

   # This could be a list/tuple of model classes, but it's rare.
   _UpperCAmelCase 						=       mapping.get(config_classes[0]     ,	a_					)
   if isinstance(a_     ,	(tuple, list)					):
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: x.__name__					)

   return model_class
def       __UpperCAmelCase		(		a_      :		Tuple					)  ->							Tuple:
   _UpperCAmelCase 						=       [(task, get_model_for_pipeline_test(a_     ,	a_					)) for task in pipeline_test_mapping]
   _UpperCAmelCase 						=       sorted([(task, model) for task, model in mapping if model is not None]     ,	key=lambda a_					: x[0]					)

   return dict(a_					)
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework == "pt":
      _UpperCAmelCase 						=       "torch"
   _UpperCAmelCase 						=       "{}"

   _UpperCAmelCase 						=       get_pipeline_model_mapping(a_					)
   if len(a_					) == 0:
      return ""

   _UpperCAmelCase 						=       []
   for task, model_classes in mapping.items():
      if isinstance(a_     ,	(tuple, list)					):
         # A list/tuple of model classes
         _UpperCAmelCase 						=       "(" + ", ".join([x.__name__ for x in model_classes]					) + ")"
      else:
         # A single model class
         _UpperCAmelCase 						=       model_classes.__name__
      texts.append(F'''"{task}": {value}'''					)
   _UpperCAmelCase 						=       "{" + ", ".join(a_					) + "}"
   _UpperCAmelCase 						=       F'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''

   return text
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
   if not issubclass(a_     ,	unittest.TestCase					):
      return False
   return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							Any:
   _UpperCAmelCase 						=       [x for x in get_test_classes(a_					) if is_valid_test_class(a_					)]

   _UpperCAmelCase 						=       None
   for test_class in test_classes:
      # If a test class has defined `pipeline_model_mapping`, let's take it
      if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) is not None:
         _UpperCAmelCase 						=       test_class
         break
    # Take the test class with the shortest name (just a heuristic)
   if target_test_class is None and len(a_					) > 0:
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: (len(x.__name__					), x.__name__)					)[0]

   return target_test_class
def       __UpperCAmelCase		(		a_      :		Union[str, Any]     ,	a_      :		str     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       start_idx
   for idx, line in enumerate(lines[start_idx:]					):
      _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
      if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
         _UpperCAmelCase 						=       start_idx + idx
      elif idx > 0 and indent <= indent_level:
         # Outside the definition block of `pipeline_model_mapping`
         break

   return end_idx
def       __UpperCAmelCase		(		a_      :		Tuple     ,	a_      :		Union[str, Any]=False					)  ->							str:
   if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) is not None:
      if not overwrite:
         return "", -1

   _UpperCAmelCase 						=       get_pipeline_model_mapping_string(a_					)
   if len(a_					) == 0:
      return "", -1
   _UpperCAmelCase 						=       line_to_add + "\n"

   # The code defined the class `test_class`
   _UpperCAmelCase						,				_UpperCAmelCase 						=       inspect.getsourcelines(a_					)
   # `inspect` gives the code for an object, including decorator(s) if any.
   # We (only) need the exact line of the class definition.
   for idx, line in enumerate(a_					):
      if line.lstrip().startswith("class "					):
         _UpperCAmelCase 						=       class_lines[idx:]
         class_start_line_no += idx
         break
   _UpperCAmelCase 						=       class_start_line_no + len(a_					) - 1

   # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
   # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
   # block that appears in a `test_class`.
   _UpperCAmelCase 						=       None
   # The indent level of the line at `class_lines[start_idx]` (if defined)
   _UpperCAmelCase 						=       0
   # To record if `pipeline_model_mapping` is found in `test_class`.
   _UpperCAmelCase 						=       None
   for idx, line in enumerate(a_					):
      if line.strip().startswith("all_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("all_generative_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("pipeline_model_mapping = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
         _UpperCAmelCase 						=       line
         break

   if start_idx is None:
      return "", -1
   # Find the ending index (inclusive) of the above found block.
   _UpperCAmelCase 						=       find_block_ending(a_     ,	a_     ,	a_					)

   # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
   # `is_timm_available()` instead of `is_torch_available()`.
   # Keep leading and trailing whitespaces
   _UpperCAmelCase 						=       re.compile(r"\s(is_\S+?_available\(\))\s"					)
   for line in class_lines[start_idx : end_idx + 1]:
      _UpperCAmelCase 						=       r.search(a_					)
      if backend_condition is not None:
         # replace the leading and trailing whitespaces to the space character " ".
         _UpperCAmelCase 						=       " " + backend_condition[0][1:-1] + " "
         _UpperCAmelCase 						=       r.sub(a_     ,	a_					)
         break

   if def_line is None:
      # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
      # `all_model_classes` or `all_generative_model_classes`.
      _UpperCAmelCase 						=       end_idx
   else:
      # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
      _UpperCAmelCase 						=       start_idx - 1
      # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
      for idx in range(a_     ,	end_idx + 1					):
         # These lines are going to be removed before writing to the test file.
         _UpperCAmelCase 						=       None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
   _UpperCAmelCase 						=       [x.__name__ for x in test_class.__bases__]
   if "PipelineTesterMixin" not in parent_classes:
      # Put `PipelineTesterMixin` just before `unittest.TestCase`
      _UpperCAmelCase 						=       [x for x in parent_classes if x != "TestCase"] + ["PipelineTesterMixin"]
      if "TestCase" in parent_classes:
         # Here we **assume** the original string is always with `unittest.TestCase`.
         _parent_classes.append("unittest.TestCase"					)
      _UpperCAmelCase 						=       ", ".join(_parent_classes					)
      for idx, line in enumerate(a_					):
         # Find the ending of the declaration of `test_class`
         if line.strip().endswith("):"					):
            # mark the lines of the declaration of `test_class` to be removed
            for _idx in range(idx + 1					):
               _UpperCAmelCase 						=       None  # noqa
            break
        # Add the new, one-line, class declaration for `test_class`
      _UpperCAmelCase 						=       F'''class {test_class.__name__}({parent_classes}):\n'''

   # Add indentation
   _UpperCAmelCase 						=       " " * indent_level + line_to_add
   # Insert `pipeline_model_mapping` to `class_lines`.
   # (The line at `target_idx` should be kept by definition!)
   _UpperCAmelCase 						=       class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
   # Remove the lines that are marked to be removed
   _UpperCAmelCase 						=       [x for x in class_lines if x is not None]

   # Move from test class to module (in order to write to the test file)
   _UpperCAmelCase 						=       inspect.getsourcelines(inspect.getmodule(a_					)					)[0]
   # Be careful with the 1-off between line numbers and array indices
   _UpperCAmelCase 						=       module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
   _UpperCAmelCase 						=       "".join(a_					)

   _UpperCAmelCase 						=       inspect.getsourcefile(a_					)
   with open(a_     ,	"w"     ,	encoding="UTF-8"     ,	newline="\n"					) as fp:
      fp.write(a_					)

   return line_to_add
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		Optional[Any]=False					)  ->							Dict:
   _UpperCAmelCase 						=       find_test_class(a_					)
   if test_class:
      add_pipeline_model_mapping(a_     ,	overwrite=a_					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_  					= argparse.ArgumentParser()
  parser.add_argument(
      '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
  )
  parser.add_argument(
      '--all',
      action='store_true',
      help='If to check and modify all test files.',
  )
  parser.add_argument(
      '--overwrite',
      action='store_true',
      help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
  )
  SCREAMING_SNAKE_CASE_  					= parser.parse_args()

  if not args.all and not args.test_file:
    raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
  elif args.all and args.test_file:
    raise ValueError('Only one of `--test_file` and `--all` could be specified.')

  SCREAMING_SNAKE_CASE_  					= []
  if args.test_file:
    SCREAMING_SNAKE_CASE_  					= [args.test_file]
  else:
    SCREAMING_SNAKE_CASE_  					= os.path.join('tests', 'models', '**', 'test_modeling_*.py')
    for test_file in glob.glob(pattern):
      # `Flax` is not concerned at this moment
      if not test_file.startswith('test_modeling_flax_'):
        test_files.append(test_file)

  for test_file in test_files:
    if test_file in TEST_FILE_TO_IGNORE:
      print(f'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
      continue
    add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)

