




'''simple docstring'''





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


a		:	Union[str, Any]       =      logging.get_logger(__name__)




@dataclass
class      UpperCamelCase       :
  a     :str
  a     :str
  a     :Optional[str]          =					None
  a     :Optional[str]          =					None



  def     a_							(			self ):







      '''simple docstring'''


      return json.dumps(dataclasses.asdict(self )		,     indent=2 ) + "\n"






@dataclass(frozen=snake_case__       )
class      UpperCamelCase       :
  a     :List[int]
  a     :Optional[List[int]]          =					None
  a     :Optional[List[int]]          =					None
  a     :Optional[Union[int, float]]          =					None



  def     a_							(			self ):







      '''simple docstring'''


      return json.dumps(dataclasses.asdict(self ) ) + "\n"






class      UpperCamelCase       :



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if len(self.get_labels() ) > 1:
          _a				:							Optional[Any]							       =      self.get_labels()[int(example.label )]
      return example



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=None ):







      '''simple docstring'''


      with open(__lowerCAmelCase		,     """r"""		,     encoding="""utf-8-sig""" ) as f:
          return list(csv.reader(__lowerCAmelCase		,     delimiter="""\t"""		,     quotechar=__lowerCAmelCase ) )






class      UpperCamelCase       (	snake_case__       ):



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="classification"		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      [] if labels is None else labels
      _a				:							Any							       =      [] if examples is None else examples
      _a				:							Tuple							       =      mode
      _a				:							int							       =      verbose



  def __len__(			self ):







      '''simple docstring'''


      return len(self.examples )



  def __getitem__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          return SingleSentenceClassificationProcessor(labels=self.labels		,     examples=self.examples[idx] )
      return self.examples[idx]



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=""		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      cls(**__lowerCAmelCase )
      processor.add_examples_from_csv(
          __lowerCAmelCase		,     split_name=__lowerCAmelCase		,     column_label=__lowerCAmelCase		,     column_text=__lowerCAmelCase		,     column_id=__lowerCAmelCase		,     skip_first_row=__lowerCAmelCase		,     overwrite_labels=__lowerCAmelCase		,     overwrite_examples=__lowerCAmelCase		,     )
      return processor



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[Any]							       =      cls(**__lowerCAmelCase )
      processor.add_examples(__lowerCAmelCase		,     labels=__lowerCAmelCase )
      return processor



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=""		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							Any							       =      self._read_tsv(__lowerCAmelCase )
      if skip_first_row:
          _a				:							Any							       =      lines[1:]
      _a				:							Tuple							       =      []
      _a				:							Optional[Any]							       =      []
      _a				:							str							       =      []
      for i, line in enumerate(__lowerCAmelCase ):
          texts.append(line[column_text] )
          labels.append(line[column_label] )
          if column_id is not None:
              ids.append(line[column_id] )
          else:
              _a				:							Union[str, Any]							       =      f'''{split_name}-{i}''' if split_name else str(__lowerCAmelCase )
              ids.append(__lowerCAmelCase )

      return self.add_examples(
          __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     overwrite_labels=__lowerCAmelCase		,     overwrite_examples=__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if labels is not None and len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(
              f'''Text and labels have mismatched lengths {len(__lowerCAmelCase )} and {len(__lowerCAmelCase )}''' )
      if ids is not None and len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(f'''Text and ids have mismatched lengths {len(__lowerCAmelCase )} and {len(__lowerCAmelCase )}''' )
      if ids is None:
          _a				:							Tuple							       =      [None] * len(__lowerCAmelCase )
      if labels is None:
          _a				:							Dict							       =      [None] * len(__lowerCAmelCase )
      _a				:							List[Any]							       =      []
      _a				:							List[Any]							       =      set()
      for text_or_text_and_label, label, guid in zip(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):
          if isinstance(__lowerCAmelCase		,     (tuple, list) ) and label is None:
              _a       , _a				:							str							       =      text_or_text_and_label
          else:
              _a				:							str							       =      text_or_text_and_label
          added_labels.add(__lowerCAmelCase )
          examples.append(InputExample(guid=__lowerCAmelCase		,     text_a=__lowerCAmelCase		,     text_b=__lowerCAmelCase		,     label=__lowerCAmelCase ) )

      # Update examples
      if overwrite_examples:
          _a				:							Optional[int]							       =      examples
      else:
          self.examples.extend(__lowerCAmelCase )

      # Update labels
      if overwrite_labels:
          _a				:							List[str]							       =      list(__lowerCAmelCase )
      else:
          _a				:							Union[str, Any]							       =      list(set(self.labels ).union(__lowerCAmelCase ) )

      return self.examples



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     ):







      '''simple docstring'''


      if max_length is None:
          _a				:							List[str]							       =      tokenizer.max_len

      _a				:							Optional[int]							       =      {label: i for i, label in enumerate(self.labels )}

      _a				:							List[Any]							       =      []
      for ex_index, example in enumerate(self.examples ):
          if ex_index % 1_0000 == 0:
              logger.info(f'''Tokenizing example {ex_index}''' )

          _a				:							int							       =      tokenizer.encode(
              example.text_a		,     add_special_tokens=__lowerCAmelCase		,     max_length=min(__lowerCAmelCase		,     tokenizer.max_len )		,     )
          all_input_ids.append(__lowerCAmelCase )

      _a				:							Union[str, Any]							       =      max(len(__lowerCAmelCase ) for input_ids in all_input_ids )

      _a				:							Optional[int]							       =      []
      for ex_index, (input_ids, example) in enumerate(zip(__lowerCAmelCase		,     self.examples ) ):
          if ex_index % 1_0000 == 0:
              logger.info(f'''Writing example {ex_index}/{len(self.examples )}''' )
          # The mask has 1 for real tokens and 0 for padding tokens. Only real
          # tokens are attended to.
          _a				:							Union[str, Any]							       =      [1 if mask_padding_with_zero else 0] * len(__lowerCAmelCase )

          # Zero-pad up to the sequence length.
          _a				:							Optional[Any]							       =      batch_length - len(__lowerCAmelCase )
          if pad_on_left:
              _a				:							Optional[Any]							       =      ([pad_token] * padding_length) + input_ids
              _a				:							Optional[Any]							       =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
              _a				:							Union[str, Any]							       =      input_ids + ([pad_token] * padding_length)
              _a				:							List[Any]							       =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

          if len(__lowerCAmelCase ) != batch_length:
              raise ValueError(f'''Error with input length {len(__lowerCAmelCase )} vs {batch_length}''' )
          if len(__lowerCAmelCase ) != batch_length:
              raise ValueError(f'''Error with input length {len(__lowerCAmelCase )} vs {batch_length}''' )

          if self.mode == "classification":
              _a				:							str							       =      label_map[example.label]
          elif self.mode == "regression":
              _a				:							int							       =      float(example.label )
          else:
              raise ValueError(self.mode )

          if ex_index < 5 and self.verbose:
              logger.info("""*** Example ***""" )
              logger.info(f'''guid: {example.guid}''' )
              logger.info(f'''input_ids: {' '.join([str(__lowerCAmelCase ) for x in input_ids] )}''' )
              logger.info(f'''attention_mask: {' '.join([str(__lowerCAmelCase ) for x in attention_mask] )}''' )
              logger.info(f'''label: {example.label} (id = {label})''' )

          features.append(InputFeatures(input_ids=__lowerCAmelCase		,     attention_mask=__lowerCAmelCase		,     label=__lowerCAmelCase ) )

      if return_tensors is None:
          return features
      elif return_tensors == "tf":
          if not is_tf_available():
              raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported""" )
          import tensorflow as tf

          def gen():
              for ex in features:
                  yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

          _a				:							Any							       =      tf.data.Dataset.from_generator(
              __lowerCAmelCase		,     ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)		,     ({"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )}, tf.TensorShape([] ))		,     )
          return dataset
      elif return_tensors == "pt":
          if not is_torch_available():
              raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported""" )
          import torch
          from torch.utils.data import TensorDataset

          _a				:							str							       =      torch.tensor([f.input_ids for f in features]		,     dtype=torch.long )
          _a				:							Any							       =      torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long )
          if self.mode == "classification":
              _a				:							Union[str, Any]							       =      torch.tensor([f.label for f in features]		,     dtype=torch.long )
          elif self.mode == "regression":
              _a				:							List[str]							       =      torch.tensor([f.label for f in features]		,     dtype=torch.float )

          _a				:							Any							       =      TensorDataset(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
          return dataset
      else:
          raise ValueError("""return_tensors should be one of 'tf' or 'pt'""" )







import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
		import torch






@require_flax
class 	_a			:






			def __init__(    self    ,  lowercase_    ,  lowercase_=14    ,  lowercase_=7    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=99    ,  lowercase_=32    ,  lowercase_=5    ,  lowercase_=4    ,  lowercase_=37    ,  lowercase_="gelu"    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=512    ,  lowercase_=0.0_2    ,  lowercase_=None    ,  )	->					Tuple:
									lowerCAmelCase	:   Union[str, Any]					     =					parent
									lowerCAmelCase	:   Optional[Any]					     =					batch_size
									lowerCAmelCase	:   Tuple					     =					seq_length
									lowerCAmelCase	:   Optional[Any]					     =					is_training
									lowerCAmelCase	:   Union[str, Any]					     =					use_input_mask
									lowerCAmelCase	:   int					     =					use_labels
									lowerCAmelCase	:   List[str]					     =					vocab_size
									lowerCAmelCase	:   List[Any]					     =					d_model
									lowerCAmelCase	:   List[Any]					     =					num_hidden_layers
									lowerCAmelCase	:   int					     =					num_attention_heads
									lowerCAmelCase	:   Optional[Any]					     =					ffn_dim
									lowerCAmelCase	:   str					     =					activation_function
									lowerCAmelCase	:   Any					     =					activation_dropout
									lowerCAmelCase	:   List[str]					     =					attention_dropout
									lowerCAmelCase	:   str					     =					max_position_embeddings
									lowerCAmelCase	:   Dict					     =					initializer_range
									lowerCAmelCase	:   str					     =					None
									lowerCAmelCase	:   Any					     =					0
									lowerCAmelCase	:   Tuple					     =					2
									lowerCAmelCase	:   Dict					     =					1






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Tuple					     =					np.clip(ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )    ,  3    ,  self.vocab_size )

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

									lowerCAmelCase	:   Tuple					     =					XGLMConfig(
									    vocab_size=self.vocab_size    ,  d_model=self.hidden_size    ,  num_layers=self.num_hidden_layers    ,  attention_heads=self.num_attention_heads    ,  ffn_dim=self.ffn_dim    ,  activation_function=self.activation_function    ,  activation_dropout=self.activation_dropout    ,  attention_dropout=self.attention_dropout    ,  max_position_embeddings=self.max_position_embeddings    ,  initializer_range=self.initializer_range    ,  use_cache=lowercase_    ,  bos_token_id=self.bos_token_id    ,  eos_token_id=self.eos_token_id    ,  pad_token_id=self.pad_token_id    ,  )

									return (config, input_ids, input_mask)






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Dict					     =					self.prepare_config_and_inputs()
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					config_and_inputs
									lowerCAmelCase	:   int					     =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   str					     =					self.prepare_config_and_inputs()

									lowerCAmelCase	:   Union[str, Any]					     =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									lowerCAmelCase	:   List[str]					     =					ids_tensor([self.batch_size, self.seq_length]    ,  vocab_size=2 )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   str					     =					20
									lowerCAmelCase	:   List[str]					     =					model_class_name(lowercase_ )

									lowerCAmelCase	:   Any					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   Optional[int]					     =					jnp.ones((input_ids.shape[0], max_decoder_length)    ,  dtype="""i4""" )

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

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

									lowerCAmelCase	:   List[str]					     =					model(lowercase_ )

									lowerCAmelCase	:   Optional[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 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Union[str, Any]					     =					20
									lowerCAmelCase	:   List[Any]					     =					model_class_name(lowercase_ )

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

									lowerCAmelCase	:   int					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   int					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

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

									lowerCAmelCase	:   Union[str, Any]					     =					model(lowercase_    ,  attention_mask=lowercase_ )
									lowerCAmelCase	:   str					     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
									self.parent.assertTrue(diff < 1e-3    ,  msg=f"""Max diff is {diff}""" )






@require_sentencepiece
@require_flax
class 	_a			(      snake_case_     , snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   List[str]	     =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			_UpperCamelCase:   str	     =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Any					     =					FlaxXGLMModelTester(self )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			@slow
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   List[str]					     =					XGLMTokenizer.from_pretrained("""XGLM"""    ,  padding_side="""left""" )
									lowerCAmelCase	:   int					     =					tokenizer(["""Hello this is a long string""", """Hey"""]    ,  return_tensors="""np"""    ,  padding=lowercase_    ,  truncation=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
									lowerCAmelCase	:   Union[str, Any]					     =					1
									lowerCAmelCase	:   Optional[Any]					     =					False

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

									lowerCAmelCase	:   Optional[Any]					     =					jit_generate(inputs["""input_ids"""]    ,  attention_mask=inputs["""attention_mask"""] ).sequences

									lowerCAmelCase	:   List[str]					     =					tokenizer.batch_decode(lowercase_    ,  skip_special_tokens=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					[
									    """Hello this is a long string of questions, but I'm not sure if I'm""",
									    """Hey, I'm a newbie to the forum and I'""",
									]

									self.assertListEqual(lowercase_    ,  lowercase_ )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase     ,	lowerCAmelCase	:   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	:   Union[str, Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   List[Any]					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

																					lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					pt_inputs["""input_ids"""].shape
																					lowerCAmelCase	:   Tuple					     =					np.random.randint(0    ,  seq_length - 1    ,  size=(batch_size,) )
																					for batch_idx, start_index in enumerate(lowercase_ ):
																											lowerCAmelCase	:   List[str]					     =					0
																											lowerCAmelCase	:   List[Any]					     =					1
																											lowerCAmelCase	:   str					     =					0
																											lowerCAmelCase	:   Union[str, Any]					     =					1
																					lowerCAmelCase	:   List[Any]					     =					pt_model_class(lowercase_ ).eval()
																					# Flax models don't use the `use_cache` option and cache is not returned as a default.
																					# So we disable `use_cache` here for PyTorch model.
																					lowerCAmelCase	:   Optional[int]					     =					False
																					lowerCAmelCase	:   Optional[Any]					     =					model_class(lowercase_    ,  dtype=jnp.floataa )
																					lowerCAmelCase	:   Union[str, Any]					     =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,  lowercase_ )
																					lowerCAmelCase	:   List[str]					     =					fx_state

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

																					lowerCAmelCase	:   Tuple					     =					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_ )
																											lowerCAmelCase	:   Optional[int]					     =					model_class.from_pretrained(lowercase_    ,  from_pt=lowercase_ )

																					lowerCAmelCase	:   Optional[int]					     =					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 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__ ):
																					# prepare inputs
																					lowerCAmelCase	:   List[Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   Tuple					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

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

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

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Optional[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:
																											fx_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   List[str]					     =					pt_model_class.from_pretrained(lowercase_    ,  from_flax=lowercase_ )

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					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 )






			@slow
			def 							_snake_case   (    self )	->					str:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase	:   Optional[Any]					     =					model_class_name.from_pretrained("""facebook/xglm-564M""" )
															lowerCAmelCase	:   int					     =					model(np.ones((1, 1) ) )
															self.assertIsNotNone(lowercase_ )

