



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
SCREAMING_SNAKE_CASE_:List[Any]					=						"""src/diffusers"""
SCREAMING_SNAKE_CASE_:Any					=						"""."""


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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	Tuple										=       object_name.split("""."""		)
    A			:	List[Any]										=       0

    # First let's find the module where our object lives.
    A			:	Tuple										=       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			:	List[str]										=       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			:	Tuple										=       f.readlines()

    # Now let's find the class / func in the code!
    A			:	Optional[Any]										=       """"""
    A			:	List[str]										=       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			:	int										=       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			:	Tuple										=       lines[start_index:line_index]
    return "".join(lowerCAmelCase__		)


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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	int										=       code.split("""\n"""		)
    A			:	str										=       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		)	->  Optional[Any]:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=False		)	->  Union[str, Any]:



    """simple docstring"""






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

        A			:	str										=       line_index + 1 if indent == theoretical_indent else line_index + 2
        A			:	str										=       theoretical_indent
        A			:	Tuple										=       start_index

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

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

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



    """simple docstring"""






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

     check_copies(args.fix_and_overwrite)







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
     import torch






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=14,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=0.02,		):
        A			:	List[str]										=       parent
        A			:	Any										=       batch_size
        A			:	Dict										=       seq_length
        A			:	Tuple										=       is_training
        A			:	Any										=       use_input_mask
        A			:	Any										=       use_token_type_ids
        A			:	Any										=       use_labels
        A			:	Optional[int]										=       vocab_size
        A			:	Dict										=       hidden_size
        A			:	Dict										=       rotary_dim
        A			:	Dict										=       num_hidden_layers
        A			:	Tuple										=       num_attention_heads
        A			:	Tuple										=       intermediate_size
        A			:	Union[str, Any]										=       hidden_act
        A			:	Dict										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	Optional[int]										=       max_position_embeddings
        A			:	str										=       initializer_range
        A			:	Any										=       None
        A			:	Any										=       vocab_size - 1
        A			:	int										=       vocab_size - 1
        A			:	int										=       vocab_size - 1






    def       _lowerCAmelCase (	self		):
        A			:	str										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A			:	Optional[int]										=       None
        if self.use_input_mask:
            A			:	Any										=       random_attention_mask([self.batch_size, self.seq_length]		)

        A			:	int										=       GPTJConfig(
            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,		use_cache=lowerCamelCase__,		bos_token_id=self.bos_token_id,		eos_token_id=self.eos_token_id,		pad_token_id=self.pad_token_id,		rotary_dim=self.rotary_dim,		)

        return (config, input_ids, input_mask)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.prepare_config_and_inputs()
        A					,							A					,							A			:	List[str]										=       config_and_inputs
        A			:	Optional[int]										=       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[int]										=       20
        A			:	Tuple										=       model_class_name(lowerCamelCase__		)

        A			:	Dict										=       model.init_cache(input_ids.shape[0],		lowerCamelCase__		)
        A			:	int										=       jnp.ones((input_ids.shape[0], max_decoder_length),		dtype="""i4"""		)

        A			:	Optional[int]										=       jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :],		(input_ids.shape[0], input_ids.shape[-1] - 1)		)
        A			:	List[Any]										=       model(
            input_ids[:, :-1],		attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__,		position_ids=lowerCamelCase__,		)

        A			:	List[Any]										=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],		dtype="""i4"""		)
        A			:	Any										=       model(
            input_ids[:, -1:],		attention_mask=lowerCamelCase__,		past_key_values=outputs_cache.past_key_values,		position_ids=lowerCamelCase__,		)

        A			:	Any										=       model(lowerCamelCase__		)

        A			:	List[Any]										=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
        self.parent.assertTrue(diff < 1e-3,		msg=f'''Max diff is {diff}'''		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Any										=       20
        A			:	Any										=       model_class_name(lowerCamelCase__		)

        A			:	Dict										=       jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)],		axis=-1,		)

        A			:	str										=       model.init_cache(input_ids.shape[0],		lowerCamelCase__		)
        A			:	Any										=       jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :],		(input_ids.shape[0], input_ids.shape[-1] - 1)		)

        A			:	Optional[int]										=       model(
            input_ids[:, :-1],		attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__,		position_ids=lowerCamelCase__,		)
        A			:	str										=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],		dtype="""i4"""		)
        A			:	List[Any]										=       model(
            input_ids[:, -1:],		past_key_values=outputs_cache.past_key_values,		attention_mask=lowerCamelCase__,		position_ids=lowerCamelCase__,		)

        A			:	Union[str, Any]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)

        A			:	str										=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
        self.parent.assertTrue(diff < 1e-3,		msg=f'''Max diff is {diff}'''		)






@require_flax
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    __lowerCamelCase      :			Optional[int] =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       FlaxGPTJModelTester(self		)






    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A					,							A					,							A			:	Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A					,							A					,							A			:	str										=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    @tooslow
    def       _lowerCAmelCase (	self		):
        A			:	int										=       GPTaTokenizer.from_pretrained("""gpt2""",		pad_token="""<|endoftext|>""",		padding_side="""left"""		)
        A			:	Optional[int]										=       tokenizer(["""Hello this is a long string""", """Hey"""],		return_tensors="""np""",		padding=lowerCamelCase__,		truncation=lowerCamelCase__		)

        A			:	Dict										=       FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		)
        A			:	str										=       False
        A			:	Optional[Any]										=       model.config.eos_token_id

        A			:	Union[str, Any]										=       jax.jit(model.generate		)

        A			:	str										=       jit_generate(
            inputs["""input_ids"""],		attention_mask=inputs["""attention_mask"""],		pad_token_id=tokenizer.pad_token_id		).sequences

        A			:	Optional[Any]										=       tokenizer.batch_decode(lowerCamelCase__,		skip_special_tokens=lowerCamelCase__		)

        A			:	Tuple										=       [
            """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
            """Hey, I'm a little late to the party. I'm going to""",
        ]

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)






    @is_pt_flax_cross_test
    def       _lowerCAmelCase (	self		):
        A					,							A			:	Optional[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                # prepare inputs
                A			:	Any										=       self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A			:	Dict										=       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                A			:	List[Any]										=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                A			:	str										=       getattr(lowerCamelCase__,		lowerCamelCase__		)

                A					,							A			:	Optional[int]										=       pt_inputs["""input_ids"""].shape
                A			:	List[str]										=       np.random.randint(0,		seq_length - 1,		size=(batch_size,)		)
                for batch_idx, start_index in enumerate(lowerCamelCase__		):
                    A			:	List[Any]										=       0
                    A			:	Tuple										=       1
                    A			:	Optional[int]										=       0
                    A			:	str										=       1
                A			:	Dict										=       pt_model_class(lowerCamelCase__		).eval()
                A			:	int										=       model_class(lowerCamelCase__,		dtype=jnp.floataa		)

                A			:	int										=       convert_pytorch_state_dict_to_flax(pt_model.state_dict(),		lowerCamelCase__		)
                A			:	Dict										=       fx_state

                with torch.no_grad():
                    A			:	Optional[int]										=       pt_model(**lowerCamelCase__		).to_tuple()

                A			:	str										=       fx_model(**lowerCamelCase__		).to_tuple()
                self.assertEqual(len(lowerCamelCase__		),		len(lowerCamelCase__		),		"""Output lengths differ between Flax and PyTorch"""		)
                for fx_output, pt_output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assert_almost_equals(fx_output[:, -1],		pt_output[:, -1].numpy(),		4e-2		)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    pt_model.save_pretrained(lowerCamelCase__		)
                    A			:	Union[str, Any]										=       model_class.from_pretrained(lowerCamelCase__,		from_pt=lowerCamelCase__		)

                A			:	Any										=       fx_model_loaded(**lowerCamelCase__		).to_tuple()
                self.assertEqual(
                    len(lowerCamelCase__		),		len(lowerCamelCase__		),		"""Output lengths differ between Flax and PyTorch"""		)
                for fx_output_loaded, pt_output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assert_almost_equals(fx_output_loaded[:, -1],		pt_output[:, -1].numpy(),		4e-2		)






    @is_pt_flax_cross_test
    def       _lowerCAmelCase (	self		):
        A					,							A			:	Dict										=       self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                # prepare inputs
                A			:	int										=       self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A			:	List[str]										=       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                A			:	Dict										=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                A			:	Dict										=       getattr(lowerCamelCase__,		lowerCamelCase__		)

                A			:	int										=       pt_model_class(lowerCamelCase__		).eval()
                A			:	int										=       model_class(lowerCamelCase__,		dtype=jnp.floataa		)

                A			:	List[str]										=       load_flax_weights_in_pytorch_model(lowerCamelCase__,		fx_model.params		)
                A					,							A			:	Optional[int]										=       pt_inputs["""input_ids"""].shape
                A			:	Optional[int]										=       np.random.randint(0,		seq_length - 1,		size=(batch_size,)		)
                for batch_idx, start_index in enumerate(lowerCamelCase__		):
                    A			:	Tuple										=       0
                    A			:	Tuple										=       1
                    A			:	str										=       0
                    A			:	int										=       1

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                with torch.no_grad():
                    A			:	List[str]										=       pt_model(**lowerCamelCase__		).to_tuple()

                A			:	Optional[int]										=       fx_model(**lowerCamelCase__		).to_tuple()
                self.assertEqual(len(lowerCamelCase__		),		len(lowerCamelCase__		),		"""Output lengths differ between Flax and PyTorch"""		)
                for fx_output, pt_output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assert_almost_equals(fx_output[:, -1],		pt_output[:, -1].numpy(),		4e-2		)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    fx_model.save_pretrained(lowerCamelCase__		)
                    A			:	str										=       pt_model_class.from_pretrained(lowerCamelCase__,		from_flax=lowerCamelCase__		)

                with torch.no_grad():
                    A			:	str										=       pt_model_loaded(**lowerCamelCase__		).to_tuple()

                self.assertEqual(
                    len(lowerCamelCase__		),		len(lowerCamelCase__		),		"""Output lengths differ between Flax and PyTorch"""		)
                for fx_output, pt_output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assert_almost_equals(fx_output[:, -1],		pt_output[:, -1].numpy(),		4e-2		)






    @tooslow
    def       _lowerCAmelCase (	self		):
        for model_class_name in self.all_model_classes:
            A			:	Union[str, Any]										=       model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""		)
            A			:	List[str]										=       model(np.ones((1, 1)		)		)
            self.assertIsNotNone(lowerCamelCase__		)


