



'''simple docstring'''




import argparse
import os
import re


UpperCamelCase_       :  str					=  """src/diffusers"""

# Pattern that looks at the indentation in a line.
UpperCamelCase_       :  str					=  re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_       :  Optional[Any]					=  re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCamelCase_       :  str					=  re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCamelCase_       :  Any					=  re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCamelCase_       :  Union[str, Any]					=  re.compile(r"""\[([^\]]+)\]""")






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= _re_indent.search(lowercase_			)
       return "" if search is None else search.groups()[0]






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase=""   ,					_lowercase=None   ,					_lowercase=None			):

       """simple docstring"""

       a__								= 0
       a__								= code.split("\n"			)
       if start_prompt is not None:
              while not lines[index].startswith(lowercase_			):
                     index += 1
              a__								= ["""\n""".join(lines[:index]			)]
       else:
              a__								= []

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a__								= [lines[index]]
       index += 1
       while index < len(lowercase_			) and (end_prompt is None or not lines[index].startswith(lowercase_			)):
              if len(lines[index]			) > 0 and get_indent(lines[index]			) == indent_level:
                     if len(lowercase_			) > 0 and get_indent(current_block[-1]			).startswith(indent_level + " "			):
                            current_block.append(lines[index]			)
                            blocks.append("\n".join(lowercase_			)			)
                            if index < len(lowercase_			) - 1:
                                   a__								= [lines[index + 1]]
                                   index += 1
                            else:
                                   a__								= []
                     else:
                            blocks.append("\n".join(lowercase_			)			)
                            a__								= [lines[index]]
              else:
                     current_block.append(lines[index]			)
              index += 1

       # Adds current block if it's nonempty.
       if len(lowercase_			) > 0:
              blocks.append("\n".join(lowercase_			)			)

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(lowercase_			):
              blocks.append("\n".join(lines[index:]			)			)

       return blocks






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       def _inner(_lowercase			):
              return key(lowercase_			).lower().replace("_"   ,					""			)

       return _inner






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase=None			):

       """simple docstring"""

       def noop(_lowercase			):
              return x

       if key is None:
              a__								= noop
       # Constants are all uppercase, they go first.
       a__								= [obj for obj in objects if key(lowercase_			).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a__								= [obj for obj in objects if key(lowercase_			)[0].isupper() and not key(lowercase_			).isupper()]
       # Functions begin with a lowercase, they go last.
       a__								= [obj for obj in objects if not key(lowercase_			)[0].isupper()]

       a__								= ignore_underscore(lowercase_			)
       return sorted(lowercase_   ,					key=lowercase_			) + sorted(lowercase_   ,					key=lowercase_			) + sorted(lowercase_   ,					key=lowercase_			)






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       def _replace(_lowercase			):
              a__								= match.groups()[0]
              if "," not in imports:
                     return F'[{imports}]'
              a__								= [part.strip().replace("\""   ,					""			) for part in imports.split(","			)]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]			) == 0:
                     a__								= keys[:-1]
              return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_			)]			) + "]"

       a__								= import_statement.split("\n"			)
       if len(lowercase_			) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a__								= 2 if lines[1].strip() == """[""" else 1
              a__								= [(i, _re_strip_line.search(lowercase_			).groups()[0]) for i, line in enumerate(lines[idx:-idx]			)]
              a__								= sort_objects(lowercase_   ,					key=lambda _lowercase			: x[1]			)
              a__								= [lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]			)
       elif len(lowercase_			) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]			) is not None:
                     a__								= _re_bracket_content.sub(_replace   ,					lines[1]			)
              else:
                     a__								= [part.strip().replace("\""   ,					""			) for part in lines[1].split(","			)]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]			) == 0:
                            a__								= keys[:-1]
                     a__								= get_indent(lines[1]			) + """, """.join([F'"{k}"' for k in sort_objects(lowercase_			)]			)
              return "\n".join(lowercase_			)
       else:
              # Finally we have to deal with imports fitting on one line
              a__								= _re_bracket_content.sub(_replace   ,					lowercase_			)
              return import_statement






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase=True			):

       """simple docstring"""

       with open(lowercase_   ,					"r"			) as f:
              a__								= f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a__								= split_code_in_indented_blocks(
           lowercase_   ,					start_prompt="_import_structure = {"   ,					end_prompt="if TYPE_CHECKING:"			)

       # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1   ,					len(lowercase_			) - 1			):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a__								= main_blocks[block_idx]
              a__								= block.split("\n"			)

              # Get to the start of the imports.
              a__								= 0
              while line_idx < len(lowercase_			) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a__								= len(lowercase_			)
                     else:
                            line_idx += 1
              if line_idx >= len(lowercase_			):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a__								= """\n""".join(block_lines[line_idx:-1]			)
              a__								= get_indent(block_lines[1]			)
              # Slit the internal block into blocks of indent level 1.
              a__								= split_code_in_indented_blocks(lowercase_   ,					indent_level=lowercase_			)
              # We have two categories of import key: list or _import_structure[key].append/extend
              a__								= _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a__								= [(pattern.search(lowercase_			).groups()[0] if pattern.search(lowercase_			) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a__								= [(i, key) for i, key in enumerate(lowercase_			) if key is not None]
              a__								= [x[0] for x in sorted(lowercase_   ,					key=lambda _lowercase			: x[1]			)]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a__								= 0
              a__								= []
              for i in range(len(lowercase_			)			):
                     if keys[i] is None:
                            reordered_blocks.append(internal_blocks[i]			)
                     else:
                            a__								= sort_objects_in_import(internal_blocks[sorted_indices[count]]			)
                            reordered_blocks.append(lowercase_			)
                            count += 1

                            # And we put our main block back together with its first and last line.
              a__								= """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]			)

       if code != "\n".join(lowercase_			):
              if check_only:
                     return True
              else:
                     print(F'Overwriting {file}.'			)
                     with open(lowercase_   ,					"w"			) as f:
                            f.write("\n".join(lowercase_			)			)






def 		_lowerCAmelCase				(_lowercase=True			):

       """simple docstring"""

       a__								= []
       for root, _, files in os.walk(lowercase_			):
              if "__init__.py" in files:
                     a__								= sort_imports(os.path.join(lowercase_   ,					"__init__.py"			)   ,					check_only=lowercase_			)
                     if result:
                            a__								= [os.path.join(lowercase_   ,					"__init__.py"			)]
       if len(lowercase_			) > 0:
              raise ValueError(F'Would overwrite {len(lowercase_			)} files, run `make style`.'			)


if __name__ == "__main__":
    UpperCamelCase_       :  List[str]					=  argparse.ArgumentParser()
    parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
    UpperCamelCase_       :  str					=  parser.parse_args()

    sort_imports_in_all_inits(check_only=args.check_only)


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    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=14       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size)

   lowercase__	: Any     		=   None
   if self.use_input_mask:
     lowercase__	: Dict     		=   random_attention_mask([self.batch_size, self.seq_length])

   lowercase__	: List[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=SCREAMING_SNAKE_CASE_       ,	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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.prepare_config_and_inputs()
   lowercase__		,     lowercase__		,     lowercase__	: Optional[Any]     		=   config_and_inputs
   lowercase__	: Optional[Any]     		=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   20
   lowercase__	: int     		=   model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="""i4""")

   lowercase__	: Tuple     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))
   lowercase__	: List[str]     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

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

   lowercase__	: Tuple     		=   model(SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

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

   lowercase__	: Dict     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))

   lowercase__	: Any     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: int     		=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4""")
   lowercase__	: Tuple     		=   model(
       input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)

   lowercase__	: 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    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Dict	   =      (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __lowerCAmelCase	:  str	   =      (FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   FlaxGPTJModelTester(self)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   GPTaTokenizer.from_pretrained("""gpt2"""       ,	pad_token="""<|endoftext|>"""       ,	padding_side="""left""")
   lowercase__	: List[str]     		=   tokenizer(["""Hello this is a long string""", """Hey"""]       ,	return_tensors="""np"""       ,	padding=SCREAMING_SNAKE_CASE_       ,	truncation=SCREAMING_SNAKE_CASE_)

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

   lowercase__	: List[Any]     		=   jax.jit(model.generate)

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

   lowercase__	: List[str]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,	skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__	: 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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




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

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

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

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

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

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: List[str]     		=   model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_pt=SCREAMING_SNAKE_CASE_)

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


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: 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
       lowercase__	: Tuple     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: str     		=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

       lowercase__	: str     		=   pt_model_class(SCREAMING_SNAKE_CASE_).eval()
       lowercase__	: Union[str, Any]     		=   model_class(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.floataa)

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

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

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

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: Tuple     		=   pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_flax=SCREAMING_SNAKE_CASE_)

       with torch.no_grad():
         lowercase__	: Tuple     		=   pt_model_loaded(**SCREAMING_SNAKE_CASE_).to_tuple()

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



 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__	: Any     		=   model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""")
     lowercase__	: int     		=   model(np.ones((1, 1)))
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)






