



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


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

def     UpperCamelCase_   (   lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    Dict						)						->       List[str]:

		"""simple docstring"""




		return line.startswith(lowerCAmelCase__						) or len(lowerCAmelCase__						) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$'   ,       lowerCAmelCase__						) is not None

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]						)						->       Optional[Any]:

		"""simple docstring"""




		lowerCAmelCase_       :    Dict								=			object_name.split('.'						)
		lowerCAmelCase_       :    Any								=			0

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

		# Now let's find the class / func in the code!
		lowerCAmelCase_       :    Any								=			''
		lowerCAmelCase_       :    List[Any]								=			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).
		lowerCAmelCase_       :    Any								=			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

		lowerCAmelCase_       :    Dict								=			lines[start_index:line_index]
		return "".join(lowerCAmelCase__						)


lowercase__		:    str				   =     re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowercase__		:    Optional[int]				   =     re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowercase__		:    Dict				   =     re.compile(R"""<FILL\s+[^>]*>""")

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       List[str]:

		"""simple docstring"""




		lowerCAmelCase_       :    List[Any]								=			code.split('\n'						)
		lowerCAmelCase_       :    Union[str, Any]								=			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__				:    Dict						)						->       Dict:

		"""simple docstring"""




		lowerCAmelCase_       :    List[Any]								=			len(get_indent(lowerCAmelCase__						)						) > 0
		if has_indent:
				lowerCAmelCase_       :    List[str]								=			f"class Bla:\n{code}"
		lowerCAmelCase_       :    Tuple								=			black.Mode(target_versions={black.TargetVersion.PYaa}   ,       line_length=119   ,       preview=lowerCAmelCase__						)
		lowerCAmelCase_       :    Dict								=			black.format_str(lowerCAmelCase__   ,       mode=lowerCAmelCase__						)
		lowerCAmelCase_       :    Dict								=			style_docstrings_in_code(lowerCAmelCase__						)
		return result[len('class Bla:\n'						) :] if has_indent else result

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    Union[str, Any]=False						)						->       str:

		"""simple docstring"""




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

				lowerCAmelCase_       :    Optional[int]								=			line_index + 1 if indent == theoretical_indent else line_index + 2
				lowerCAmelCase_       :    Dict								=			theoretical_indent
				lowerCAmelCase_       :    Dict								=			start_index

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

				lowerCAmelCase_       :    str								=			lines[start_index:line_index]
				lowerCAmelCase_       :    str								=			''.join(lowerCAmelCase__						)

				# Remove any nested `Copied from` comments to avoid circular copies
				lowerCAmelCase_       :    Tuple								=			[line for line in theoretical_code.split('\n'						) if _re_copy_warning.search(lowerCAmelCase__						) is None]
				lowerCAmelCase_       :    Union[str, Any]								=			'\n'.join(lowerCAmelCase__						)

				# Before comparing, use the `replace_pattern` on the original code.
				if len(lowerCAmelCase__						) > 0:
						lowerCAmelCase_       :    List[str]								=			replace_pattern.replace('with'   ,       ''						).split(','						)
						lowerCAmelCase_       :    int								=			[_re_replace_pattern.search(lowerCAmelCase__						) for p in patterns]
						for pattern in patterns:
								if pattern is None:
										continue
								lowerCAmelCase_       :    Optional[int]								=			pattern.groups()
								lowerCAmelCase_       :    Union[str, Any]								=			re.sub(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
								if option.strip() == "all-casing":
										lowerCAmelCase_       :    int								=			re.sub(obja.lower()   ,       obja.lower()   ,       lowerCAmelCase__						)
										lowerCAmelCase_       :    Optional[int]								=			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
						lowerCAmelCase_       :    int								=			blackify(lines[start_index - 1] + theoretical_code						)
						lowerCAmelCase_       :    List[Any]								=			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:
								lowerCAmelCase_       :    str								=			lines[:start_index] + [theoretical_code] + lines[line_index:]
								lowerCAmelCase_       :    Optional[Any]								=			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						)						->       Union[str, Any]:

		"""simple docstring"""




		lowerCAmelCase_       :    List[str]								=			glob.glob(os.path.join(lowerCAmelCase__   ,       '**/*.py'						)   ,       recursive=lowerCAmelCase__						)
		lowerCAmelCase_       :    str								=			[]
		for filename in all_files:
				lowerCAmelCase_       :    Union[str, Any]								=			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:
				lowerCAmelCase_       :    Dict								=			'\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__":
					lowercase__		:    Optional[int]				   =     argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					lowercase__		:    Any				   =     parser.parse_args()

					check_copies(args.fix_and_overwrite)










"""simple docstring"""







import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         AlbertForMaskedLM,
         AlbertForMultipleChoice,
         AlbertForPreTraining,
         AlbertForQuestionAnswering,
         AlbertForSequenceClassification,
         AlbertForTokenClassification,
         AlbertModel,
     )
     from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict=1_3   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=7   ,      SCREAMING_SNAKE_CASE_    :				Dict=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				List[str]=9_9   ,      SCREAMING_SNAKE_CASE_    :				int=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=3_6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=6   ,      SCREAMING_SNAKE_CASE_    :				Tuple=6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=6   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_7   ,      SCREAMING_SNAKE_CASE_    :				Tuple="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				Dict=3   ,      SCREAMING_SNAKE_CASE_    :				int=4   ,      SCREAMING_SNAKE_CASE_    :				Tuple=None   ,      ):
        lowerCAmelCase_       :    Any								=			parent
        lowerCAmelCase_       :    Optional[int]								=			batch_size
        lowerCAmelCase_       :    Dict								=			seq_length
        lowerCAmelCase_       :    Tuple								=			is_training
        lowerCAmelCase_       :    str								=			use_input_mask
        lowerCAmelCase_       :    Union[str, Any]								=			use_token_type_ids
        lowerCAmelCase_       :    Tuple								=			use_labels
        lowerCAmelCase_       :    Optional[int]								=			vocab_size
        lowerCAmelCase_       :    Any								=			embedding_size
        lowerCAmelCase_       :    Optional[Any]								=			hidden_size
        lowerCAmelCase_       :    str								=			num_hidden_layers
        lowerCAmelCase_       :    Optional[Any]								=			num_hidden_groups
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    Optional[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_act
        lowerCAmelCase_       :    Union[str, Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    int								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    int								=			max_position_embeddings
        lowerCAmelCase_       :    List[Any]								=			type_vocab_size
        lowerCAmelCase_       :    Any								=			type_sequence_label_size
        lowerCAmelCase_       :    Optional[int]								=			initializer_range
        lowerCAmelCase_       :    Tuple								=			num_labels
        lowerCAmelCase_       :    Dict								=			num_choices
        lowerCAmelCase_       :    Tuple								=			scope



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size	)

        lowerCAmelCase_       :    str								=			None
        if self.use_input_mask:
          lowerCAmelCase_       :    int								=			random_attention_mask([self.batch_size, self.seq_length]	)

        lowerCAmelCase_       :    List[Any]								=			None
        if self.use_token_type_ids:
          lowerCAmelCase_       :    str								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size	)

        lowerCAmelCase_       :    List[str]								=			None
        lowerCAmelCase_       :    Union[str, Any]								=			None
        lowerCAmelCase_       :    str								=			None
        if self.use_labels:
          lowerCAmelCase_       :    Any								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size	)
          lowerCAmelCase_       :    List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels	)
          lowerCAmelCase_       :    Dict								=			ids_tensor([self.batch_size]   ,      self.num_choices	)

        lowerCAmelCase_       :    List[Any]								=			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return AlbertConfig(
            vocab_size=self.vocab_size   ,      hidden_size=self.hidden_size   ,      num_hidden_layers=self.num_hidden_layers   ,      num_attention_heads=self.num_attention_heads   ,      intermediate_size=self.intermediate_size   ,      hidden_act=self.hidden_act   ,      hidden_dropout_prob=self.hidden_dropout_prob   ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,      max_position_embeddings=self.max_position_embeddings   ,      type_vocab_size=self.type_vocab_size   ,      initializer_range=self.initializer_range   ,      num_hidden_groups=self.num_hidden_groups   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str	):
        lowerCAmelCase_       :    Union[str, Any]								=			AlbertModel(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)
        self.parent.assertEqual(result.pooler_output.shape   ,      (self.batch_size, self.hidden_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        lowerCAmelCase_       :    Optional[Any]								=			AlbertForPreTraining(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[Any]								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      sentence_order_label=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.prediction_logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)
        self.parent.assertEqual(result.sop_logits.shape   ,      (self.batch_size, config.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    str								=			AlbertForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]	):
        lowerCAmelCase_       :    List[str]								=			AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Any								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      start_positions=SCREAMING_SNAKE_CASE_   ,      end_positions=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.start_logits.shape   ,      (self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape   ,      (self.batch_size, self.seq_length)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    Union[str, Any]								=			self.num_labels
        lowerCAmelCase_       :    Union[str, Any]								=			AlbertForSequenceClassification(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Union[str, Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    List[str]								=			self.num_labels
        lowerCAmelCase_       :    List[Any]								=			AlbertForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.num_choices
        lowerCAmelCase_       :    int								=			AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[int]								=			input_ids.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			token_type_ids.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			input_mask.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_choices)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Optional[int]								=			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,
        )       :    Optional[int]								=			config_and_inputs
        lowerCAmelCase_       :    Dict								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(
          (
              AlbertModel,
              AlbertForPreTraining,
              AlbertForMaskedLM,
              AlbertForMultipleChoice,
              AlbertForSequenceClassification,
              AlbertForTokenClassification,
              AlbertForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE      =					(
          {
              """feature-extraction""": AlbertModel,
              """fill-mask""": AlbertForMaskedLM,
              """question-answering""": AlbertForQuestionAnswering,
              """text-classification""": AlbertForSequenceClassification,
              """token-classification""": AlbertForTokenClassification,
              """zero-shot""": AlbertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE      =					True



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				str=False	):
        lowerCAmelCase_       :    List[str]								=			super()._prepare_for_class(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      return_labels=SCREAMING_SNAKE_CASE_	)

        if return_labels:
          if model_class in get_values(SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    List[str]								=			torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)
            lowerCAmelCase_       :    Any								=			torch.zeros(
                self.model_tester.batch_size   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)
        return inputs_dict



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			AlbertModelTester(self	)
        lowerCAmelCase_       :    Optional[int]								=			ConfigTester(self   ,      config_class=SCREAMING_SNAKE_CASE_   ,      hidden_size=3_7	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        self.config_tester.run_common_tests()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Any								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Optional[int]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          lowerCAmelCase_       :    int								=			type
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)





      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    Optional[Any]								=			AlbertModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







@require_torch
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Any								=			AlbertModel.from_pretrained('albert-base-v2'	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
        with torch.no_grad():
          lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)[0]
        lowerCAmelCase_       :    str								=			torch.Size((1, 1_1, 7_6_8)	)
        self.assertEqual(output.shape   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			torch.tensor(
            [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]	)

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-4	)	)





