





import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class 							snake_case_		(      _A):







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase=False )					-> str:
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=(
				    filename_skip_take['''filename'''],
				    filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
				    filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
				)
				open(os.path.join(__lowercase )      ,  '''wb''' ).close()
				lowerCamelCase :			str    		=pa.Table.from_pydict({'''filename''': [Path(__lowercase ).name] * 1_0_0} )
				if take == -1:
						lowerCamelCase :			Dict    		=len(__lowercase ) - skip
				if skip is not None and take is not None:
						lowerCamelCase :			Optional[Any]    		=pa_table.slice(__lowercase      ,  __lowercase )
				return pa_table

class 							snake_case_		(      _A):







		def       __lowercase (		self )					-> str:
				lowerCamelCase :			Union[str, Any]    		='''my_name'''
				lowerCamelCase :			Tuple    		=SplitInfo(name='''train'''      ,  num_examples=1_0_0 )
				lowerCamelCase :			Dict    		=SplitInfo(name='''test'''      ,  num_examples=1_0_0 )
				lowerCamelCase :			Tuple    		=[train_info, test_info]
				lowerCamelCase :			Optional[int]    		=SplitDict()
				split_dict.add(__lowercase )
				split_dict.add(__lowercase )
				lowerCamelCase :			List[str]    		=DatasetInfo(splits=__lowercase )

				with tempfile.TemporaryDirectory() as tmp_dir:
						lowerCamelCase :			Any    		=ReaderTest(__lowercase      ,  __lowercase )

						lowerCamelCase :			int    		='''test[:33%]'''
						lowerCamelCase :			Optional[Any]    		=Dataset(**reader.read(__lowercase      ,  __lowercase      ,  __lowercase ) )
						self.assertEqual(dset['''filename'''][0]      ,  F"{name}-test" )
						self.assertEqual(dset.num_rows      ,  3_3 )
						self.assertEqual(dset.num_columns      ,  1 )

						lowerCamelCase :			Optional[int]    		=['''train''', '''test[:33%]''']
						lowerCamelCase :			List[Any]    		=[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
						for instructions in [instructionsa, instructionsa]:
								lowerCamelCase :			Union[str, Any]    		=[reader.read(__lowercase      ,  __lowercase      ,  __lowercase ) for instr in instructions]
								lowerCamelCase		, lowerCamelCase :			List[str]    		=(Dataset(**__lowercase ) for dataset_kwargs in datasets_kwargs)
								self.assertEqual(train_dset['''filename'''][0]      ,  F"{name}-train" )
								self.assertEqual(train_dset.num_rows      ,  1_0_0 )
								self.assertEqual(train_dset.num_columns      ,  1 )
								self.assertIsInstance(train_dset.split      ,  __lowercase )
								self.assertEqual(str(train_dset.split )      ,  '''train''' )
								self.assertEqual(test_dset['''filename'''][0]      ,  F"{name}-test" )
								self.assertEqual(test_dset.num_rows      ,  3_3 )
								self.assertEqual(test_dset.num_columns      ,  1 )
								self.assertIsInstance(test_dset.split      ,  __lowercase )
								self.assertEqual(str(test_dset.split )      ,  '''test[:33%]''' )
								del train_dset, test_dset







		def       __lowercase (		self )					-> Tuple:
				lowerCamelCase :			Dict    		='''my_name'''
				lowerCamelCase :			int    		=SplitInfo(name='''train'''      ,  num_examples=1_0_0_0      ,  shard_lengths=[1_0_0] * 1_0 )
				lowerCamelCase :			List[str]    		=[train_info]
				lowerCamelCase :			List[Any]    		=SplitDict()
				split_dict.add(__lowercase )
				lowerCamelCase :			Dict    		=DatasetInfo(splits=__lowercase )

				with tempfile.TemporaryDirectory() as tmp_dir:
						lowerCamelCase :			Dict    		=ReaderTest(__lowercase      ,  __lowercase )

						lowerCamelCase :			List[str]    		='''train[:33%]'''
						lowerCamelCase :			str    		=Dataset(**reader.read(__lowercase      ,  __lowercase      ,  __lowercase ) )
						self.assertEqual(dset['''filename'''][0]      ,  F"{name}-train-00000-of-00010" )
						self.assertEqual(dset['''filename'''][-1]      ,  F"{name}-train-00003-of-00010" )
						self.assertEqual(dset.num_rows      ,  3_3_0 )
						self.assertEqual(dset.num_columns      ,  1 )







		def       __lowercase (		self )					-> str:
				lowerCamelCase :			Any    		=SplitInfo(name='''train'''      ,  num_examples=1_0_0 )
				lowerCamelCase :			List[str]    		=SplitInfo(name='''test'''      ,  num_examples=1_0_0 )
				lowerCamelCase :			int    		=SplitDict()
				split_dict.add(__lowercase )
				split_dict.add(__lowercase )
				lowerCamelCase :			str    		=DatasetInfo(splits=__lowercase )

				with tempfile.TemporaryDirectory() as tmp_dir:
						lowerCamelCase :			str    		=ReaderTest(__lowercase      ,  __lowercase )

						lowerCamelCase :			Optional[int]    		=[
						    {'''filename''': os.path.join(__lowercase      ,  '''train''' )},
						    {'''filename''': os.path.join(__lowercase      ,  '''test''' ), '''skip''': 1_0, '''take''': 1_0},
						]
						lowerCamelCase :			Optional[Any]    		=Dataset(**reader.read_files(__lowercase      ,  original_instructions='''train+test[10:20]''' ) )
						self.assertEqual(dset.num_rows      ,  1_1_0 )
						self.assertEqual(dset.num_columns      ,  1 )
						del dset


@pytest.mark.parametrize('''in_memory'''				,							[False, True]		)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		lowerCamelCase :			Dict    		=arrow_file
		with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase :			Tuple    		=ArrowReader.read_table(SCREAMING_SNAKE_CASE_				,							in_memory=SCREAMING_SNAKE_CASE_		)
		assert table.shape == dataset.data.shape
		assert set(table.column_names		) == set(dataset.data.column_names		)
		assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict


@pytest.mark.parametrize('''in_memory'''				,							[False, True]		)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Optional[Any]    		=arrow_file
		lowerCamelCase :			Dict    		=ArrowReader(''''''				,							SCREAMING_SNAKE_CASE_		)
		with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase :			str    		=reader.read_files([{'''filename''': filename}]				,							in_memory=SCREAMING_SNAKE_CASE_		)
		assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
		lowerCamelCase :			Union[str, Any]    		=dataset_kwargs['''arrow_table''']
		assert table.shape == dataset.data.shape
		assert set(table.column_names		) == set(dataset.data.column_names		)
		assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict


def        A__						(							) ->  Union[str, Any]:
		assert ReadInstruction('''train'''				,							to=1_0				,							unit='''abs'''		).to_spec() == "train[:10]"
		assert ReadInstruction('''train'''				,							from_=-8_0				,							to=1_0				,							unit='''%'''		).to_spec() == "train[-80%:10%]"

		lowerCamelCase :			List[Any]    		='''train+test'''
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_		).to_spec() == spec_train_test

		lowerCamelCase :			Any    		='''train[2:10]'''
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_		).to_spec() == spec_train_abs

		lowerCamelCase :			List[Any]    		='''train[15%:-20%]'''
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_		).to_spec() == spec_train_pct

		lowerCamelCase :			Optional[Any]    		='''train[:10%](closest)'''
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_		).to_spec() == "train[:10%]"

		lowerCamelCase :			Dict    		='''train[:10%](pct1_dropremainder)'''
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_		).to_spec() == spec_train_pct_rounding

		lowerCamelCase :			Optional[Any]    		='''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_		).to_spec() == spec_train_test_pct_rounding


def        A__						(							) ->  Dict:
		lowerCamelCase :			Tuple    		='''dummy'''
		lowerCamelCase :			Any    		=[SplitInfo(name='''train'''				,							num_examples=1_0_0		)]
		lowerCamelCase :			Union[str, Any]    		='''train[:33%]'''
		lowerCamelCase :			Optional[int]    		='''arrow'''
		lowerCamelCase :			Union[str, Any]    		='''prefix'''

		lowerCamelCase :			List[str]    		=make_file_instructions(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		assert isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		assert file_instructions.num_examples == 3_3
		assert file_instructions.file_instructions == [
		    {"filename": os.path.join(SCREAMING_SNAKE_CASE_				,							F"{name}-train.arrow"		), "skip": 0, "take": 3_3}
		]

		lowerCamelCase :			List[str]    		=[SplitInfo(name='''train'''				,							num_examples=1_0_0				,							shard_lengths=[1_0] * 1_0		)]
		lowerCamelCase :			Tuple    		=make_file_instructions(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		assert isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		assert file_instructions.num_examples == 3_3
		assert file_instructions.file_instructions == [
		    {"filename": os.path.join(SCREAMING_SNAKE_CASE_				,							F"{name}-train-00000-of-00010.arrow"		), "skip": 0, "take": -1},
		    {"filename": os.path.join(SCREAMING_SNAKE_CASE_				,							F"{name}-train-00001-of-00010.arrow"		), "skip": 0, "take": -1},
		    {"filename": os.path.join(SCREAMING_SNAKE_CASE_				,							F"{name}-train-00002-of-00010.arrow"		), "skip": 0, "take": -1},
		    {"filename": os.path.join(SCREAMING_SNAKE_CASE_				,							F"{name}-train-00003-of-00010.arrow"		), "skip": 0, "take": 3},
		]



@pytest.mark.parametrize('''name, expected_exception'''				,							[(None, TypeError), ('''''', ValueError)]		)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			Dict    		=[SplitInfo(name='''train'''				,							num_examples=1_0_0		)]
		lowerCamelCase :			Dict    		='''train'''
		lowerCamelCase :			Optional[Any]    		='''arrow'''
		lowerCamelCase :			Optional[int]    		='''prefix_path'''
		with pytest.raises(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Tuple    		=make_file_instructions(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
				import torch
class 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_4      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=False      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=3_2      ,  __lowercase=4      ,  __lowercase=4      ,  __lowercase=[[["global", "local"], 2]]      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=7      ,  __lowercase=0.0_2      ,  )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=parent
				lowerCamelCase :			Optional[int]    		=batch_size
				lowerCamelCase :			Any    		=seq_length
				lowerCamelCase :			Optional[Any]    		=is_training
				lowerCamelCase :			Any    		=use_input_mask
				lowerCamelCase :			int    		=use_token_type_ids
				lowerCamelCase :			int    		=use_labels
				lowerCamelCase :			str    		=vocab_size
				lowerCamelCase :			Optional[int]    		=hidden_size
				lowerCamelCase :			int    		=num_hidden_layers
				lowerCamelCase :			List[Any]    		=num_attention_heads
				lowerCamelCase :			Any    		=attention_types
				lowerCamelCase :			List[str]    		=intermediate_size
				lowerCamelCase :			Dict    		=hidden_act
				lowerCamelCase :			Union[str, Any]    		=hidden_dropout_prob
				lowerCamelCase :			Optional[int]    		=attention_probs_dropout_prob
				lowerCamelCase :			Any    		=max_position_embeddings
				lowerCamelCase :			int    		=window_size
				lowerCamelCase :			List[Any]    		=initializer_range
				lowerCamelCase :			Any    		=None
				lowerCamelCase :			List[Any]    		=vocab_size - 1
				lowerCamelCase :			str    		=vocab_size - 1
				lowerCamelCase :			int    		=vocab_size - 1







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			List[Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

				lowerCamelCase :			Tuple    		=None
				if self.use_input_mask:
						lowerCamelCase :			Union[str, Any]    		=random_attention_mask([self.batch_size, self.seq_length] )

				lowerCamelCase :			List[Any]    		=GPTNeoConfig(
				    vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  num_layers=self.num_hidden_layers      ,  num_heads=self.num_attention_heads      ,  max_position_embeddings=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      ,  window_size=self.window_size      ,  attention_types=self.attention_types      ,  )

				return (config, input_ids, input_mask)







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			List[Any]    		=self.prepare_config_and_inputs()
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[Any]    		=config_and_inputs
				lowerCamelCase :			List[str]    		={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> List[Any]:
				lowerCamelCase :			Tuple    		=2_0
				lowerCamelCase :			Union[str, Any]    		=model_class_name(__lowercase )

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

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

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

				lowerCamelCase :			Any    		=model(__lowercase )

				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}" )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> Dict:
				lowerCamelCase :			str    		=2_0
				lowerCamelCase :			Optional[int]    		=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 :			Union[str, Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			List[Any]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

				lowerCamelCase :			Optional[Any]    		=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 :			Union[str, 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 :			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 							snake_case_		(      _A    ,       _A    ,       unittest.TestCase):
		lowerCamelCase     :List[Any]							=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		lowerCamelCase     :Union[str, Any]							=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			str    		=FlaxGPTNeoModelTester(self )







		def       __lowercase (		self )					-> Dict:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(__lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		def       __lowercase (		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       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Tuple    		=GPTaTokenizer.from_pretrained('''gpt2'''      ,  pad_token='''<|endoftext|>'''      ,  padding_side='''left''' )
				lowerCamelCase :			Any    		=tokenizer(['''Hello this is a long string''', '''Hey''']      ,  return_tensors='''np'''      ,  padding=__lowercase      ,  truncation=__lowercase )

				lowerCamelCase :			List[Any]    		=FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
				lowerCamelCase :			Union[str, Any]    		=False
				lowerCamelCase :			Optional[int]    		=model.config.eos_token_id

				lowerCamelCase :			str    		=jax.jit(model.generate )

				lowerCamelCase :			Optional[int]    		=jit_generate(
				    inputs['''input_ids''']      ,  attention_mask=inputs['''attention_mask''']      ,  pad_token_id=tokenizer.pad_token_id ).sequences

				lowerCamelCase :			Any    		=tokenizer.batch_decode(__lowercase      ,  skip_special_tokens=__lowercase )

				lowerCamelCase :			str    		=[
				    '''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       __lowercase (		self )					-> int:
				lowerCamelCase		, lowerCamelCase :			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
								lowerCamelCase :			Optional[Any]    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Optional[Any]    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

								lowerCamelCase :			Union[str, Any]    		=convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,  __lowercase )
								lowerCamelCase :			Optional[int]    		=fx_state

								with torch.no_grad():
										lowerCamelCase :			List[Any]    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			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 )
										lowerCamelCase :			List[str]    		=model_class.from_pretrained(__lowercase      ,  from_pt=__lowercase )

								lowerCamelCase :			List[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       __lowercase (		self )					-> str:
				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 :			Tuple    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Any    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

								lowerCamelCase :			List[str]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Union[str, Any]    		=model_class(__lowercase      ,  dtype=jnp.floataa )

								lowerCamelCase :			Dict    		=load_flax_weights_in_pytorch_model(__lowercase      ,  fx_model.params )
								lowerCamelCase		, lowerCamelCase :			Any    		=pt_inputs['''input_ids'''].shape
								lowerCamelCase :			str    		=np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
								for batch_idx, start_index in enumerate(__lowercase ):
										lowerCamelCase :			List[str]    		=0
										lowerCamelCase :			Union[str, Any]    		=1
										lowerCamelCase :			Optional[int]    		=0
										lowerCamelCase :			Dict    		=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 :			Union[str, Any]    		=pt_model_class.from_pretrained(__lowercase      ,  from_flax=__lowercase )

								with torch.no_grad():
										lowerCamelCase :			str    		=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       __lowercase (		self )					-> Any:
				for model_class_name in self.all_model_classes:
						lowerCamelCase :			Optional[Any]    		=model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
						lowerCamelCase :			Union[str, Any]    		=model(np.ones((1, 1) ) )
						self.assertIsNotNone(__lowercase )

