





import collections
import os
import re
from pathlib import Path


_A					:				int				=			"""src/transformers"""


# Matches is_xxx_available()
_A					:				Optional[int]				=			re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
_A					:				Union[str, Any]				=			re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_A					:				Any				=			re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
_A					:				Dict				=			re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
_A					:				Optional[Any]				=			re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_A					:				int				=			re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
_A					:				Union[str, Any]				=			re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
_A					:				Union[str, Any]				=			re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
_A					:				Union[str, Any]				=			re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
_A					:				Optional[int]				=			re.compile(r"""^\s*try:""")
# Catches a line with else:
_A					:				Union[str, Any]				=			re.compile(r"""^\s*else:""")


def 							__snake_case							(    lowerCAmelCase_	)				->						List[str]:
    if _re_test_backend.search(lowerCAmelCase_	) is None:
        return None
    SCREAMING_SNAKE_CASE__											=			[b[0] for b in _re_backend.findall(lowerCAmelCase_	)]
    backends.sort()
    return "_and_".join(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    with open(lowerCAmelCase_ ,							'''r''' ,							encoding='''utf-8''' ,							newline='''\n'''	) as f:
        SCREAMING_SNAKE_CASE__											=			f.readlines()

    SCREAMING_SNAKE_CASE__											=			0
    while line_index < len(lowerCAmelCase_	) and not lines[line_index].startswith('''_import_structure = {'''	):
        line_index += 1

    # If this is a traditional init, just return.
    if line_index >= len(lowerCAmelCase_	):
        return None

    # First grab the objects without a specific backend in _import_structure
    SCREAMING_SNAKE_CASE__											=			[]
    while not lines[line_index].startswith('''if TYPE_CHECKING'''	) and find_backend(lines[line_index]	) is None:
        SCREAMING_SNAKE_CASE__											=			lines[line_index]
        # If we have everything on a single line, let's deal with it.
        if _re_one_line_import_struct.search(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			_re_one_line_import_struct.search(lowerCAmelCase_	).groups()[0]
            SCREAMING_SNAKE_CASE__											=			re.findall(r'''\[([^\]]+)\]''' ,							lowerCAmelCase_	)
            for imp in imports:
                objects.extend([obj[1:-1] for obj in imp.split(''', '''	)]	)
            line_index += 1
            continue
        SCREAMING_SNAKE_CASE__											=			_re_import_struct_key_value.search(lowerCAmelCase_	)
        if single_line_import_search is not None:
            SCREAMING_SNAKE_CASE__											=			[obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''	) if len(lowerCAmelCase_	) > 0]
            objects.extend(lowerCAmelCase_	)
        elif line.startswith(''' ''' * 8 + '''"'''	):
            objects.append(line[9:-3]	)
        line_index += 1

    SCREAMING_SNAKE_CASE__											=			{'''none''': objects}
    # Let's continue with backend-specific objects in _import_structure
    while not lines[line_index].startswith('''if TYPE_CHECKING'''	):
        # If the line is an if not is_backend_available, we grab all objects associated.
        SCREAMING_SNAKE_CASE__											=			find_backend(lines[line_index]	)
        # Check if the backend declaration is inside a try block:
        if _re_try.search(lines[line_index - 1]	) is None:
            SCREAMING_SNAKE_CASE__											=			None

        if backend is not None:
            line_index += 1

            # Scroll until we hit the else block of try-except-else
            while _re_else.search(lines[line_index]	) is None:
                line_index += 1

            line_index += 1

            SCREAMING_SNAKE_CASE__											=			[]
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]	) <= 1 or lines[line_index].startswith(''' ''' * 4	):
                SCREAMING_SNAKE_CASE__											=			lines[line_index]
                if _re_import_struct_add_one.search(lowerCAmelCase_	) is not None:
                    objects.append(_re_import_struct_add_one.search(lowerCAmelCase_	).groups()[0]	)
                elif _re_import_struct_add_many.search(lowerCAmelCase_	) is not None:
                    SCREAMING_SNAKE_CASE__											=			_re_import_struct_add_many.search(lowerCAmelCase_	).groups()[0].split(''', '''	)
                    SCREAMING_SNAKE_CASE__											=			[obj[1:-1] for obj in imports if len(lowerCAmelCase_	) > 0]
                    objects.extend(lowerCAmelCase_	)
                elif _re_between_brackets.search(lowerCAmelCase_	) is not None:
                    SCREAMING_SNAKE_CASE__											=			_re_between_brackets.search(lowerCAmelCase_	).groups()[0].split(''', '''	)
                    SCREAMING_SNAKE_CASE__											=			[obj[1:-1] for obj in imports if len(lowerCAmelCase_	) > 0]
                    objects.extend(lowerCAmelCase_	)
                elif _re_quote_object.search(lowerCAmelCase_	) is not None:
                    objects.append(_re_quote_object.search(lowerCAmelCase_	).groups()[0]	)
                elif line.startswith(''' ''' * 8 + '''"'''	):
                    objects.append(line[9:-3]	)
                elif line.startswith(''' ''' * 1_2 + '''"'''	):
                    objects.append(line[1_3:-3]	)
                line_index += 1

            SCREAMING_SNAKE_CASE__											=			objects
        else:
            line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
    SCREAMING_SNAKE_CASE__											=			[]
    while (
        line_index < len(lowerCAmelCase_	)
        and find_backend(lines[line_index]	) is None
        and not lines[line_index].startswith('''else'''	)
    ):
        SCREAMING_SNAKE_CASE__											=			lines[line_index]
        SCREAMING_SNAKE_CASE__											=			_re_import.search(lowerCAmelCase_	)
        if single_line_import_search is not None:
            objects.extend(single_line_import_search.groups()[0].split(''', '''	)	)
        elif line.startswith(''' ''' * 8	):
            objects.append(line[8:-2]	)
        line_index += 1

    SCREAMING_SNAKE_CASE__											=			{'''none''': objects}
    # Let's continue with backend-specific objects
    while line_index < len(lowerCAmelCase_	):
        # If the line is an if is_backend_available, we grab all objects associated.
        SCREAMING_SNAKE_CASE__											=			find_backend(lines[line_index]	)
        # Check if the backend declaration is inside a try block:
        if _re_try.search(lines[line_index - 1]	) is None:
            SCREAMING_SNAKE_CASE__											=			None

        if backend is not None:
            line_index += 1

            # Scroll until we hit the else block of try-except-else
            while _re_else.search(lines[line_index]	) is None:
                line_index += 1

            line_index += 1

            SCREAMING_SNAKE_CASE__											=			[]
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]	) <= 1 or lines[line_index].startswith(''' ''' * 8	):
                SCREAMING_SNAKE_CASE__											=			lines[line_index]
                SCREAMING_SNAKE_CASE__											=			_re_import.search(lowerCAmelCase_	)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(''', '''	)	)
                elif line.startswith(''' ''' * 1_2	):
                    objects.append(line[1_2:-2]	)
                line_index += 1

            SCREAMING_SNAKE_CASE__											=			objects
        else:
            line_index += 1

    return import_dict_objects, type_hint_objects


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:

    def find_duplicates(lowerCAmelCase_	):
        return [k for k, v in collections.Counter(lowerCAmelCase_	).items() if v > 1]

    if list(import_dict_objects.keys()	) != list(type_hint_objects.keys()	):
        return ["Both sides of the init do not have the same backends!"]

    SCREAMING_SNAKE_CASE__											=			[]
    for key in import_dict_objects.keys():
        SCREAMING_SNAKE_CASE__											=			find_duplicates(import_dict_objects[key]	)
        if duplicate_imports:
            errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''	)
        SCREAMING_SNAKE_CASE__											=			find_duplicates(type_hint_objects[key]	)
        if duplicate_type_hints:
            errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''	)

        if sorted(set(import_dict_objects[key]	)	) != sorted(set(type_hint_objects[key]	)	):
            SCREAMING_SNAKE_CASE__											=			'''base imports''' if key == '''none''' else f'''{key} backend'''
            errors.append(f'''Differences for {name}:'''	)
            for a in type_hint_objects[key]:
                if a not in import_dict_objects[key]:
                    errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''	)
            for a in import_dict_objects[key]:
                if a not in type_hint_objects[key]:
                    errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''	)
    return errors


def 							__snake_case							(    )				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for root, _, files in os.walk(lowerCAmelCase_	):
        if "__init__.py" in files:
            SCREAMING_SNAKE_CASE__											=			os.path.join(lowerCAmelCase_ ,							'''__init__.py'''	)
            SCREAMING_SNAKE_CASE__											=			parse_init(lowerCAmelCase_	)
            if objects is not None:
                SCREAMING_SNAKE_CASE__											=			analyze_results(*lowerCAmelCase_	)
                if len(lowerCAmelCase_	) > 0:
                    SCREAMING_SNAKE_CASE__											=			f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
                    failures.append('''\n'''.join(lowerCAmelCase_	)	)
    if len(lowerCAmelCase_	) > 0:
        raise ValueError('''\n\n'''.join(lowerCAmelCase_	)	)


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    for path, directories, files in os.walk(lowerCAmelCase_	):
        for folder in directories:
            # Ignore private modules
            if folder.startswith('''_'''	):
                directories.remove(lowerCAmelCase_	)
                continue
            # Ignore leftovers from branches (empty folders apart from pycache)
            if len(list((Path(lowerCAmelCase_	) / folder).glob('''*.py'''	)	)	) == 0:
                continue
            SCREAMING_SNAKE_CASE__											=			str((Path(lowerCAmelCase_	) / folder).relative_to(lowerCAmelCase_	)	)
            SCREAMING_SNAKE_CASE__											=			short_path.replace(os.path.sep ,							'''.'''	)
            submodules.append(lowerCAmelCase_	)
        for fname in files:
            if fname == "__init__.py":
                continue
            SCREAMING_SNAKE_CASE__											=			str((Path(lowerCAmelCase_	) / fname).relative_to(lowerCAmelCase_	)	)
            SCREAMING_SNAKE_CASE__											=			short_path.replace('''.py''' ,							''''''	).replace(os.path.sep ,							'''.'''	)
            if len(submodule.split('''.'''	)	) == 1:
                submodules.append(lowerCAmelCase_	)
    return submodules


_A					:				Optional[Any]				=			[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
    """models.esm.openfold_utils""",
]


def 							__snake_case							(    )				->						Dict:
    # This is to make sure the transformers module imported is the one in the repo.
    from transformers.utils import direct_transformers_import

    SCREAMING_SNAKE_CASE__											=			direct_transformers_import(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			set(transformers._import_structure.keys()	)
    # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
    # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
    # (potentiall re-) add them.
    with open(os.path.join(lowerCAmelCase_ ,							'''__init__.py'''	) ,							'''r'''	) as f:
        SCREAMING_SNAKE_CASE__											=			f.read()
    import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' ,							lowerCAmelCase_	)	)	)

    SCREAMING_SNAKE_CASE__											=			[
        module
        for module in get_transformers_submodules()
        if module not in IGNORE_SUBMODULES and module not in import_structure_keys
    ]

    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			'''\n'''.join(f'''- {module}''' for module in module_not_registered	)
        raise ValueError(
            '''The following submodules are not properly registed in the main init of Transformers:\n'''
            f'''{list_of_modules}\n'''
            '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''	)


if __name__ == "__main__":
  check_all_inits()
  check_submodules()







import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
  )



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=14   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			use_mc_token_ids
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			type_vocab_size
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			num_labels
        SCREAMING_SNAKE_CASE__											=			num_choices
        SCREAMING_SNAKE_CASE__											=			scope
        SCREAMING_SNAKE_CASE__											=			self.vocab_size - 1



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_input_mask:
            SCREAMING_SNAKE_CASE__											=			random_attention_mask([self.batch_size, self.seq_length]     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_mc_token_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.num_choices]   ,				self.seq_length     )

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.num_choices     )

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,				2     )

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return CTRLConfig(
            vocab_size=self.vocab_size   ,				n_embd=self.hidden_size   ,				n_layer=self.num_hidden_layers   ,				n_head=self.num_attention_heads   ,				n_positions=self.max_position_embeddings   ,				pad_token_id=self.pad_token_id   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModel(config=A_     )
        model.to(A_     )
        model.eval()

        model(A_   ,				token_type_ids=A_   ,				head_mask=A_     )
        model(A_   ,				token_type_ids=A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertEqual(len(result.past_key_values     )   ,				config.n_layer     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel(A_     )
        model.to(A_     )
        model.eval()

        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.loss.shape   ,				()     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()

        (
            (
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		
        )											=			config_and_inputs

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

        return config, inputs_dict




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.num_labels
        SCREAMING_SNAKE_CASE__											=			CTRLForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(CTRLLMHeadModel,) if is_torch_available() else ()
    lowerCamelCase__    :		Tuple				=	(
        {
            """feature-extraction""": CTRLModel,
            """text-classification""": CTRLForSequenceClassification,
            """text-generation""": CTRLLMHeadModel,
            """zero-shot""": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	True
    lowerCamelCase__    :		Optional[int]				=	False
    lowerCamelCase__    :		Any				=	False



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
            # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
            # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
            # config could not be created.
            return True

        return False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				n_embd=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*A_     )



    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			CTRLModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )




    @unittest.skip('''The model doesn\'t support left padding'''     )  # and it's not used enough to be worth fixing :)
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass





@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel.from_pretrained('''ctrl'''     )
        model.to(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor(
            [[1_18_59, 0, 16_11, 8]]   ,				dtype=torch.long   ,				device=A_     )  # Legal the president is
        SCREAMING_SNAKE_CASE__											=			[
            1_18_59,
            0,
            16_11,
            8,
            5,
            1_50,
            2_64_49,
            2,
            19,
            3_48,
            4_69,
            3,
            25_95,
            48,
            2_07_40,
            24_65_33,
            24_65_33,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        SCREAMING_SNAKE_CASE__											=			model.generate(A_   ,				do_sample=A_     )
        self.assertListEqual(output_ids[0].tolist()   ,				A_     )
