






"""simple docstring"""






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase_     :				List[Any]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				List[str]         =				OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

UpperCAmelCase_     :				List[Any]         =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



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






    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            SCREAMING_SNAKE_CASE_   :						Tuple							=	model_type_to_module_name(__a							)

            SCREAMING_SNAKE_CASE_   :						Tuple							=	importlib.import_module(f'.{module_name}'					,       '''transformers.models'''							)
            try:
                return getattr(__a					,       __a							)
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(__a					,       '''__name__'''					,       __a							) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	importlib.import_module('''transformers'''							)
    if hasattr(__a					,       __a							):
        return getattr(__a					,       __a							)

    return None



def       _A    (__a					,       __a = None					,       __a = False					,       __a = False					,       __a = None					,       __a = None					,       __a = None					,       __a = False					,       **__a					,       )				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	get_file_from_repo(
        __a					,       __a					,       cache_dir=__a					,       force_download=__a					,       resume_download=__a					,       proxies=__a					,       use_auth_token=__a					,       revision=__a					,       local_files_only=__a					,       )
    if resolved_config_file is None:
        logger.info(
            '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''							)
        return {}

    with open(__a					,       encoding='''utf-8'''							) as reader:
        return json.load(__a							)





class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Optional[Any]):




          '''simple docstring'''


          raise EnvironmentError(
              '''AutoFeatureExtractor is designed to be instantiated '''
              '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''')
      @classmethod
      @replace_list_option_in_docstrings(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		cls  :						Any		,			lowercase_  :						str		,			**lowercase_  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	kwargs.pop('''config'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	kwargs.pop('''trust_remote_code'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	True

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	FeatureExtractionMixin.get_feature_extractor_dict(lowercase_		,			**lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	config_dict.get('''feature_extractor_type'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	None
          if "AutoFeatureExtractor" in config_dict.get('''auto_map'''		,			{}):
              SCREAMING_SNAKE_CASE_   :						List[str]							=	config_dict['''auto_map''']['''AutoFeatureExtractor''']

          # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
          if feature_extractor_class is None and feature_extractor_auto_map is None:
              if not isinstance(lowercase_		,			lowercase_):
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	AutoConfig.from_pretrained(lowercase_		,			**lowercase_)
              # It could be in `config.feature_extractor_type``
              SCREAMING_SNAKE_CASE_   :						Any							=	getattr(lowercase_		,			'''feature_extractor_type'''		,			lowercase_)
              if hasattr(lowercase_		,			'''auto_map''') and "AutoFeatureExtractor" in config.auto_map:
                  SCREAMING_SNAKE_CASE_   :						Dict							=	config.auto_map['''AutoFeatureExtractor''']

          if feature_extractor_class is not None:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	feature_extractor_class_from_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	feature_extractor_auto_map is not None
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feature_extractor_class is not None or type(lowercase_) in FEATURE_EXTRACTOR_MAPPING
          SCREAMING_SNAKE_CASE_   :						Dict							=	resolve_trust_remote_code(
              lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)

          if has_remote_code and trust_remote_code:
              SCREAMING_SNAKE_CASE_   :						int							=	get_class_from_dynamic_module(
                  lowercase_		,			lowercase_		,			**lowercase_)
              SCREAMING_SNAKE_CASE_   :						Any							=	kwargs.pop('''code_revision'''		,			lowercase_)
              if os.path.isdir(lowercase_):
                  feature_extractor_class.register_for_auto_class()
              return feature_extractor_class.from_dict(lowercase_		,			**lowercase_)
          elif feature_extractor_class is not None:
              return feature_extractor_class.from_dict(lowercase_		,			**lowercase_)
          # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
          elif type(lowercase_) in FEATURE_EXTRACTOR_MAPPING:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FEATURE_EXTRACTOR_MAPPING[type(lowercase_)]
              return feature_extractor_class.from_dict(lowercase_		,			**lowercase_)

          raise ValueError(
              F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
              F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
              F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}')



      @staticmethod
      def _SCREAMING_SNAKE_CASE				(		lowercase_  :						str		,			lowercase_  :						Tuple):




          '''simple docstring'''


          FEATURE_EXTRACTOR_MAPPING.register(lowercase_		,			lowercase_)










"""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_)


