






"""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
UpperCAmelCase_     :				Any         =				"""src/diffusers"""
UpperCAmelCase_     :				Optional[Any]         =				"""."""


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



def       _A    (__a					,       __a							)				-> Optional[Any]:
    """simple docstring"""






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



def       _A    (__a							)				-> Any:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	object_name.split('''.'''							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	0

    # First let's find the module where our object lives.
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	parts[i]
    while i < len(__a							) and not os.path.isfile(os.path.join(__a					,       f'{module}.py'							)							):
        i += 1
        if i < len(__a							):
            SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	os.path.join(__a					,       parts[i]							)
    if i >= len(__a							):
        raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'							)

    with open(os.path.join(__a					,       f'{module}.py'							)					,       '''r'''					,       encoding='''utf-8'''					,       newline='''\n'''							) as f:
        SCREAMING_SNAKE_CASE_   :						Dict							=	f.readlines()

    # Now let's find the class / func in the code!
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	''''''
    SCREAMING_SNAKE_CASE_   :						str							=	0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__a							) 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(__a							):
        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).
    SCREAMING_SNAKE_CASE_   :						Dict							=	line_index
    while line_index < len(__a							) and _should_continue(lines[line_index]					,       __a							):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]							) <= 1:
        line_index -= 1

    SCREAMING_SNAKE_CASE_   :						Dict							=	lines[start_index:line_index]
    return "".join(__a							)


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



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	code.split('''\n'''							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	0
    while idx < len(__a							) and len(lines[idx]							) == 0:
        idx += 1
    if idx < len(__a							):
        return re.search(R'''^(\s*)\S'''					,       lines[idx]							).groups()[0]
    return ""



def       _A    (__a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	len(get_indent(__a							)							) > 0
    if has_indent:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	f'class Bla:\n{code}'
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	black.Mode(target_versions={black.TargetVersion.PYaa}					,       line_length=1_19					,       preview=__a							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	black.format_str(__a					,       mode=__a							)
    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						str							=	style_docstrings_in_code(__a							)
    return result[len('''class Bla:\n'''							) :] if has_indent else result



def       _A    (__a					,       __a=False							)				-> Union[str, Any]:
    """simple docstring"""






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

        SCREAMING_SNAKE_CASE_   :						int							=	line_index + 1 if indent == theoretical_indent else line_index + 2
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	theoretical_indent
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	True
        while line_index < len(__a							) and should_continue:
            line_index += 1
            if line_index >= len(__a							):
                break
            SCREAMING_SNAKE_CASE_   :						List[Any]							=	lines[line_index]
            SCREAMING_SNAKE_CASE_   :						Tuple							=	_should_continue(__a					,       __a							) and re.search(f'^{indent}# End copy'					,       __a							) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]							) <= 1:
            line_index -= 1

        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	lines[start_index:line_index]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	''''''.join(__a							)

        # Remove any nested `Copied from` comments to avoid circular copies
        SCREAMING_SNAKE_CASE_   :						str							=	[line for line in theoretical_code.split('''\n'''							) if _re_copy_warning.search(__a							) is None]
        SCREAMING_SNAKE_CASE_   :						Dict							=	'''\n'''.join(__a							)

        # Before comparing, use the `replace_pattern` on the original code.
        if len(__a							) > 0:
            SCREAMING_SNAKE_CASE_   :						str							=	replace_pattern.replace('''with'''					,       ''''''							).split(''','''							)
            SCREAMING_SNAKE_CASE_   :						Dict							=	[_re_replace_pattern.search(__a							) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	pattern.groups()
                SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	re.sub(__a					,       __a					,       __a							)
                if option.strip() == "all-casing":
                    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	re.sub(obja.lower()					,       obja.lower()					,       __a							)
                    SCREAMING_SNAKE_CASE_   :						List[str]							=	re.sub(obja.upper()					,       obja.upper()					,       __a							)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            SCREAMING_SNAKE_CASE_   :						int							=	blackify(lines[start_index - 1] + theoretical_code							)
            SCREAMING_SNAKE_CASE_   :						Dict							=	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:
                SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	lines[:start_index] + [theoretical_code] + lines[line_index:]
                SCREAMING_SNAKE_CASE_   :						Any							=	start_index + 1

    if overwrite and len(__a							) > 0:
        # Warn the user a file has been modified.
        print(f'Detected changes, rewriting {filename}.'							)
        with open(__a					,       '''w'''					,       encoding='''utf-8'''					,       newline='''\n'''							) as f:
            f.writelines(__a							)
    return diffs



def       _A    (__a = False							)				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	glob.glob(os.path.join(__a					,       '''**/*.py'''							)					,       recursive=__a							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[]
    for filename in all_files:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	is_copy_consistent(__a					,       __a							)
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__a							) > 0:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''\n'''.join(__a							)
        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__":
  UpperCAmelCase_     :				Optional[Any]         =				argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  UpperCAmelCase_     :				Optional[int]         =				parser.parse_args()

  check_copies(args.fix_and_overwrite)










"""simple docstring"""






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 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						List[Any]		,			lowercase_  :						int=14		,			lowercase_  :						Tuple=7		,			lowercase_  :						int=True		,			lowercase_  :						int=True		,			lowercase_  :						int=False		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Union[str, Any]=99		,			lowercase_  :						Optional[int]=32		,			lowercase_  :						List[Any]=4		,			lowercase_  :						Optional[int]=4		,			lowercase_  :						List[str]=4		,			lowercase_  :						Tuple=37		,			lowercase_  :						Any="gelu"		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=0.1		,			lowercase_  :						Optional[int]=512		,			lowercase_  :						Dict=0.02		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	parent
          SCREAMING_SNAKE_CASE_   :						Any							=	batch_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	seq_length
          SCREAMING_SNAKE_CASE_   :						Any							=	is_training
          SCREAMING_SNAKE_CASE_   :						int							=	use_input_mask
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_token_type_ids
          SCREAMING_SNAKE_CASE_   :						Tuple							=	use_labels
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	rotary_dim
          SCREAMING_SNAKE_CASE_   :						str							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						int							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						Dict							=	None
          SCREAMING_SNAKE_CASE_   :						str							=	vocab_size - 1
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size - 1
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size - 1
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

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

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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=lowercase_		,			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 _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						int							=	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Any		,			lowercase_  :						Any		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	20
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.ones((input_ids.shape[0], max_decoder_length)		,			dtype='''i4''')

          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1)[None, :]		,			(input_ids.shape[0], input_ids.shape[-1] - 1))
          SCREAMING_SNAKE_CASE_   :						Any							=	model(
              input_ids[:, :-1]		,			attention_mask=lowercase_		,			past_key_values=lowercase_		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						str							=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='''i4''')
          SCREAMING_SNAKE_CASE_   :						int							=	model(
              input_ids[:, -1:]		,			attention_mask=lowercase_		,			past_key_values=outputs_cache.past_key_values		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	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 _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	20
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]		,			axis=-1		,			)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1)[None, :]		,			(input_ids.shape[0], input_ids.shape[-1] - 1))

          SCREAMING_SNAKE_CASE_   :						Dict							=	model(
              input_ids[:, :-1]		,			attention_mask=lowercase_		,			past_key_values=lowercase_		,			position_ids=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='''i4''')
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(
              input_ids[:, -1:]		,			past_key_values=outputs_cache.past_key_values		,			attention_mask=lowercase_		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			attention_mask=lowercase_)

          SCREAMING_SNAKE_CASE_   :						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}')




@require_flax
class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __UpperCamelCase							=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJModelTester(self)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	GPTaTokenizer.from_pretrained('''gpt2'''		,			pad_token='''<|endoftext|>'''		,			padding_side='''left''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''Hello this is a long string''', '''Hey''']		,			return_tensors='''np'''		,			padding=lowercase_		,			truncation=lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	False
          SCREAMING_SNAKE_CASE_   :						str							=	model.config.eos_token_id

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jax.jit(model.generate)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	jit_generate(
              inputs['''input_ids''']		,			attention_mask=inputs['''attention_mask''']		,			pad_token_id=tokenizer.pad_token_id).sequences

          SCREAMING_SNAKE_CASE_   :						int							=	tokenizer.batch_decode(lowercase_		,			skip_special_tokens=lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	[
              '''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(lowercase_		,			lowercase_)
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Any							=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	getattr(lowercase_		,			lowercase_)

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

                  SCREAMING_SNAKE_CASE_   :						List[str]							=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	fx_state

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Dict							=	pt_model(**lowercase_).to_tuple()

                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	fx_model(**lowercase_).to_tuple()
                  self.assertEqual(len(lowercase_)		,			len(lowercase_)		,			'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(lowercase_		,			lowercase_):
                      self.assert_almost_equals(fx_output[:, -1]		,			pt_output[:, -1].numpy()		,			4e-2)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(lowercase_)
                      SCREAMING_SNAKE_CASE_   :						int							=	model_class.from_pretrained(lowercase_		,			from_pt=lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	fx_model_loaded(**lowercase_).to_tuple()
                  self.assertEqual(
                      len(lowercase_)		,			len(lowercase_)		,			'''Output lengths differ between Flax and PyTorch''')
                  for fx_output_loaded, pt_output in zip(lowercase_		,			lowercase_):
                      self.assert_almost_equals(fx_output_loaded[:, -1]		,			pt_output[:, -1].numpy()		,			4e-2)
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[str]							=	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
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                  SCREAMING_SNAKE_CASE_   :						int							=	pt_model_class(lowercase_).eval()
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_class(lowercase_		,			dtype=jnp.floataa)

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

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

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	pt_model(**lowercase_).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(lowercase_)
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	pt_model_class.from_pretrained(lowercase_		,			from_flax=lowercase_)

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	pt_model_loaded(**lowercase_).to_tuple()

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



      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						Any							=	model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
              SCREAMING_SNAKE_CASE_   :						List[str]							=	model(np.ones((1, 1)))
              self.assertIsNotNone(lowercase_)


