





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
		import torch

class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Any					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Tuple=14					,      __lowerCamelCase							:     Optional[int]=7					,      __lowerCamelCase							:     Any=True					,      __lowerCamelCase							:     Union[str, Any]=True					,      __lowerCamelCase							:     Tuple=False					,      __lowerCamelCase							:     List[str]=True					,      __lowerCamelCase							:     Optional[int]=99					,      __lowerCamelCase							:     List[str]=32					,      __lowerCamelCase							:     Any=4					,      __lowerCamelCase							:     List[Any]=4					,      __lowerCamelCase							:     Optional[int]=4					,      __lowerCamelCase							:     int=37					,      __lowerCamelCase							:     List[str]="gelu"					,      __lowerCamelCase							:     List[str]=0.1					,      __lowerCamelCase							:     Any=0.1					,      __lowerCamelCase							:     int=512					,      __lowerCamelCase							:     Optional[int]=0.02					,      )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     parent
			__lowercase											=     batch_size
			__lowercase											=     seq_length
			__lowercase											=     is_training
			__lowercase											=     use_input_mask
			__lowercase											=     use_token_type_ids
			__lowercase											=     use_labels
			__lowercase											=     vocab_size
			__lowercase											=     hidden_size
			__lowercase											=     rotary_dim
			__lowercase											=     num_hidden_layers
			__lowercase											=     num_attention_heads
			__lowercase											=     intermediate_size
			__lowercase											=     hidden_act
			__lowercase											=     hidden_dropout_prob
			__lowercase											=     attention_probs_dropout_prob
			__lowercase											=     max_position_embeddings
			__lowercase											=     initializer_range
			__lowercase											=     None
			__lowercase											=     vocab_size - 1
			__lowercase											=     vocab_size - 1
			__lowercase											=     vocab_size - 1




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size   )

			__lowercase											=     None
			if self.use_input_mask:
					__lowercase											=     random_attention_mask([self.batch_size, self.seq_length]   )

			__lowercase											=     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    UpperCAmelCase			(						self							:     int   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     self.prepare_config_and_inputs()
			__lowercase						,      __lowercase						,      __lowercase											=     config_and_inputs
			__lowercase											=     {'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Optional[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Dict   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     20
			__lowercase											=     model_class_name(__lowerCamelCase   )

			__lowercase											=     model.init_cache(input_ids.shape[0]					,      __lowerCamelCase   )
			__lowercase											=     jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype='i4'   )

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

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

			__lowercase											=     model(__lowerCamelCase   )

			__lowercase											=     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    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     Any   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     20
			__lowercase											=     model_class_name(__lowerCamelCase   )

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

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

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

			__lowercase											=     model(__lowerCamelCase					,      attention_mask=__lowerCamelCase   )

			__lowercase											=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
			self.parent.assertTrue(diff < 1E-3					,      msg=F"Max diff is {diff}"   )





@require_flax
class 		snake_case_   (							UpperCAmelCase_     ,     UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
	__UpperCamelCase   =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()




	def    UpperCAmelCase			(						self							:     Any   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     FlaxGPTJModelTester(self   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> Tuple:

			'''simple docstring'''




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




	def    UpperCAmelCase			(						self							:     List[str]   )       -> Optional[int]:

			'''simple docstring'''




			for model_class_name in self.all_model_classes:
					__lowercase						,      __lowercase						,      __lowercase											=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase   )




	@tooslow
	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     GPTaTokenizer.from_pretrained('gpt2'					,      pad_token='<|endoftext|>'					,      padding_side='left'   )
			__lowercase											=     tokenizer(['Hello this is a long string', 'Hey']					,      return_tensors='np'					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )

			__lowercase											=     FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   )
			__lowercase											=     False
			__lowercase											=     model.config.eos_token_id

			__lowercase											=     jax.jit(model.generate   )

			__lowercase											=     jit_generate(
			    inputs['input_ids']					,      attention_mask=inputs['attention_mask']					,      pad_token_id=tokenizer.pad_token_id   ).sequences

			__lowercase											=     tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase   )

			__lowercase											=     [
			    '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    UpperCAmelCase			(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							# prepare inputs
							__lowercase											=     self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase   )
							__lowercase											=     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

							__lowercase						,      __lowercase											=     pt_inputs['input_ids'].shape
							__lowercase											=     np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)   )
							for batch_idx, start_index in enumerate(__lowerCamelCase   ):
									__lowercase											=     0
									__lowercase											=     1
									__lowercase											=     0
									__lowercase											=     1
							__lowercase											=     pt_model_class(__lowerCamelCase   ).eval()
							__lowercase											=     model_class(__lowerCamelCase					,      dtype=jnp.floataa   )

							__lowercase											=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      __lowerCamelCase   )
							__lowercase											=     fx_state

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

							__lowercase											=     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   )
									__lowercase											=     model_class.from_pretrained(__lowerCamelCase					,      from_pt=__lowerCamelCase   )

							__lowercase											=     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    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Dict:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     self.model_tester.prepare_config_and_inputs_for_common()
			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							# prepare inputs
							__lowercase											=     self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase   )
							__lowercase											=     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

							__lowercase											=     pt_model_class(__lowerCamelCase   ).eval()
							__lowercase											=     model_class(__lowerCamelCase					,      dtype=jnp.floataa   )

							__lowercase											=     load_flax_weights_in_pytorch_model(__lowerCamelCase					,      fx_model.params   )
							__lowercase						,      __lowercase											=     pt_inputs['input_ids'].shape
							__lowercase											=     np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)   )
							for batch_idx, start_index in enumerate(__lowerCamelCase   ):
									__lowercase											=     0
									__lowercase											=     1
									__lowercase											=     0
									__lowercase											=     1

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

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

							__lowercase											=     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   )
									__lowercase											=     pt_model_class.from_pretrained(__lowerCamelCase					,      from_flax=__lowerCamelCase   )

							with torch.no_grad():
									__lowercase											=     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    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			for model_class_name in self.all_model_classes:
					__lowercase											=     model_class_name.from_pretrained('EleutherAI/gpt-j-6B'   )
					__lowercase											=     model(np.ones((1, 1)   )   )
					self.assertIsNotNone(__lowerCamelCase   )











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'							)							),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']							)							),
		                    'langs': Sequence(Value('string'							)							),
		                    'spans': Sequence(Value('string'							)							),
		                }							)						,   splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_351_563,
		                    'num_examples': 10_000,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 238_418,
		                    'num_examples': 1_000,
		                },
		            ]						,   download_size=3_940_680						,   dataset_size=2_589_981						,   )
		    }							)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(snake_case							)
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,   expected[split].num_bytes							)
				else:
						result == expected




