



import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase		:      Dict					="src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_lowercase		:      Optional[int]					=direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowercase		:      Tuple					=transformers.models.auto.configuration_auto.CONFIG_MAPPING

_lowercase		:      str					={
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)

def 	lowerCAmelCase_     (  _lowercase				:   int						,   _lowercase				:   Any						,   _lowercase				:   Optional[Any]						,   _lowercase				:   Any)							->       Tuple:







			"""simple docstring"""
			a__   :						Optional[int]						       =  False
			for attribute in attributes:
						for modeling_source in source_strings:
									# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
									if (
									    F'''config.{attribute}''' in modeling_source
									    or F'''getattr(config, \"{attribute}\"''' in modeling_source
									    or F'''getattr(self.config, \"{attribute}\"''' in modeling_source
									):
												a__   :						List[str]						       =  True
									# Deal with multi-line cases
									elif (
									    re.search(
									        RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"'''						,   _A						,   )
									    is not None
									):
												a__   :						int						       =  True
									# `SequenceSummary` is called with `SequenceSummary(config)`
									elif attribute in [
									    "summary_type",
									    "summary_use_proj",
									    "summary_activation",
									    "summary_last_dropout",
									    "summary_proj_to_labels",
									    "summary_first_dropout",
									]:
												if "SequenceSummary" in modeling_source:
															a__   :						Any						       =  True
									if attribute_used:
												break
						if attribute_used:
									break

    # common and important attributes, even if they do not always appear in the modeling files
			a__   :						Union[str, Any]						       =  [
			    "bos_index",
			    "eos_index",
			    "pad_index",
			    "unk_index",
			    "mask_index",
			    "image_size",
			    "use_cache",
			    "out_features",
			    "out_indices",
			]
			a__   :						Dict						       =  ["encoder_no_repeat_ngram_size"]

			# Special cases to be allowed
			a__   :						Any						       =  True
			if not attribute_used:
						a__   :						Union[str, Any]						       =  False
						for attribute in attributes:
									# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
									if attribute in ["is_encoder_decoder"] and default_value is True:
												a__   :						int						       =  True
									elif attribute in ["tie_word_embeddings"] and default_value is False:
												a__   :						List[str]						       =  True

									# Allow cases without checking the default value in the configuration class
									elif attribute in attributes_to_allow + attributes_used_in_generation:
												a__   :						Optional[int]						       =  True
									elif attribute.endswith("""_token_id"""):
												a__   :						List[Any]						       =  True

									# configuration class specific cases
									if not case_allowed:
												a__   :						Union[str, Any]						       =  SPECIAL_CASES_TO_ALLOW.get(config_class.__name__						,   [])
												a__   :						List[str]						       =  allowed_cases is True or attribute in allowed_cases

			return attribute_used or case_allowed

def 	lowerCAmelCase_     (  _lowercase				:   Dict)							->       Any:







			"""simple docstring"""
			a__   :						str						       =  dict(inspect.signature(config_class.__init__).parameters)
			a__   :						Dict						       =  [x for x in list(signature.keys()) if x not in ["self", "kwargs"]]
			a__   :						Dict						       =  [signature[param].default for param in parameter_names]

			# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
			# as one variant is used, the test should pass
			a__   :						int						       =  {}
			if len(config_class.attribute_map) > 0:
						a__   :						int						       =  {v: k for k, v in config_class.attribute_map.items()}

			# Get the path to modeling source files
			a__   :						Optional[int]						       =  inspect.getsourcefile(_A)
			a__   :						List[Any]						       =  os.path.dirname(_A)
			# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
			a__   :						Tuple						       =  [os.path.join(_A						,   _A) for fn in os.listdir(_A) if fn.startswith("""modeling_""")]

			# Get the source code strings
			a__   :						str						       =  []
			for path in modeling_paths:
						if os.path.isfile(_A):
									with open(_A) as fp:
												modeling_sources.append(fp.read())

			a__   :						Any						       =  []
			for config_param, default_value in zip(_A						,   _A):
						# `attributes` here is all the variant names for `config_param`
						a__   :						Union[str, Any]						       =  [config_param]
						# some configuration classes have non-empty `attribute_map`, and both names could be used in the
						# corresponding modeling files. As long as one of them appears, it is fine.
						if config_param in reversed_attribute_map:
									attributes.append(reversed_attribute_map[config_param])

						if not check_attribute_being_used(_A						,   _A						,   _A						,   _A):
									unused_attributes.append(attributes[0])

			return sorted(_A)

def 	lowerCAmelCase_     (  )							->       Optional[Any]:







			"""simple docstring"""
			a__   :						Tuple						       =  {}
			for _config_class in list(CONFIG_MAPPING.values()):
						# Skip deprecated models
						if "models.deprecated" in _config_class.__module__:
									continue
						# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
						a__   :						List[str]						       =  [
						    cls
						    for name, cls in inspect.getmembers(
						        inspect.getmodule(_config_class)						,   lambda _lowercase: inspect.isclass(_A)
						        and issubclass(_A						,   _A)
						        and inspect.getmodule(_A) == inspect.getmodule(_config_class)						,   )
						]
						for config_class in config_classes_in_module:
									a__   :						int						       =  check_config_attributes_being_used(_A)
									if len(_A) > 0:
												a__   :						Dict						       =  unused_attributes

			if len(_A) > 0:
						a__   :						int						       =  "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
						for name, attributes in configs_with_unused_attributes.items():
									error += F'''{name}: {attributes}\n'''

						raise ValueError(_A)


if __name__ == "__main__":
		check_config_attributes()








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 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Union[str, Any]=14					,      __lowerCamelCase							:			str=7					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Optional[int]=False					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			List[Any]=99					,      __lowerCamelCase							:			Optional[Any]=32					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			List[str]=4					,      __lowerCamelCase							:			List[Any]=37					,      __lowerCamelCase							:			str="gelu"					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			Any=0.1					,      __lowerCamelCase							:			Any=512					,      __lowerCamelCase							:			Dict=0.0_2					,      ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size       )

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

             lowerCamelCase__		:				Dict    =  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							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.prepare_config_and_inputs()
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  config_and_inputs
             lowerCamelCase__		:				Tuple    =  {"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  20
             lowerCamelCase__		:				Tuple    =  model_class_name(__lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  model.init_cache(input_ids.shape[0]					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype="i4"       )

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

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

             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase       )

             lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  20
             lowerCamelCase__		:				int    =  model_class_name(__lowerCamelCase       )

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

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

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

             lowerCamelCase__		:				Tuple    =  model(__lowerCamelCase					,      attention_mask=__lowerCamelCase       )

             lowerCamelCase__		:				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}"       )







@require_flax
class 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       A__											=			(FlaxGPTJForCausalLM,) if is_flax_available() else ()





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  FlaxGPTJModelTester(self       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  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							:			str       ):


             '''simple docstring'''



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

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

             lowerCamelCase__		:				int    =  jax.jit(model.generate       )

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

             lowerCamelCase__		:				Tuple    =  tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  [
                 "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							:			Optional[int]       ):


             '''simple docstring'''



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

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

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

                         lowerCamelCase__		:				Optional[int]    =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      __lowerCamelCase       )
                         lowerCamelCase__		:				Optional[int]    =  fx_state

                         with torch.no_grad():
                               lowerCamelCase__		:				List[Any]    =  pt_model(**__lowerCamelCase       ).to_tuple()

                         lowerCamelCase__		:				Union[str, Any]    =  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       )
                               lowerCamelCase__		:				List[Any]    =  model_class.from_pretrained(__lowerCamelCase					,      from_pt=__lowerCamelCase       )

                         lowerCamelCase__		:				int    =  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							:			str       ):


             '''simple docstring'''



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

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

                         lowerCamelCase__		:				Optional[Any]    =  pt_model_class(__lowerCamelCase       ).eval()
                         lowerCamelCase__		:				Tuple    =  model_class(__lowerCamelCase					,      dtype=jnp.floataa       )

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

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

                         with torch.no_grad():
                               lowerCamelCase__		:				Dict    =  pt_model(**__lowerCamelCase       ).to_tuple()

                         lowerCamelCase__		:				List[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:
                               fx_model.save_pretrained(__lowerCamelCase       )
                               lowerCamelCase__		:				Any    =  pt_model_class.from_pretrained(__lowerCamelCase					,      from_flax=__lowerCamelCase       )

                         with torch.no_grad():
                               lowerCamelCase__		:				Optional[int]    =  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							:			List[str]       ):


             '''simple docstring'''



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


