

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					:							Optional[int]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, Any]=1_4				,  _UpperCamelCase					:							int=7				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							List[Any]=True				,  _UpperCamelCase					:							Any=False				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							List[str]=9_9				,  _UpperCamelCase					:							Dict=3_2				,  _UpperCamelCase					:							Dict=4				,  _UpperCamelCase					:							Optional[int]=4				,  _UpperCamelCase					:							List[Any]=4				,  _UpperCamelCase					:							Dict=3_7				,  _UpperCamelCase					:							int="gelu"				,  _UpperCamelCase					:							str=0.1				,  _UpperCamelCase					:							Any=0.1				,  _UpperCamelCase					:							Union[str, Any]=5_1_2				,  _UpperCamelCase					:							Optional[int]=0.02				,  ) ->List[Any]:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      seq_length
									snake_case_  =      is_training
									snake_case_  =      use_input_mask
									snake_case_  =      use_token_type_ids
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      rotary_dim
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      intermediate_size
									snake_case_  =      hidden_act
									snake_case_  =      hidden_dropout_prob
									snake_case_  =      attention_probs_dropout_prob
									snake_case_  =      max_position_embeddings
									snake_case_  =      initializer_range
									snake_case_  =      None
									snake_case_  =      vocab_size - 1
									snake_case_  =      vocab_size - 1
									snake_case_  =      vocab_size - 1







			def 							snake_case__(	self					:							Optional[int]				) ->List[Any]:
									snake_case_  =      ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size				)

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

									snake_case_  =      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=_UpperCamelCase				,  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 							snake_case__(	self					:							Dict				) ->Union[str, Any]:
									snake_case_  =      self.prepare_config_and_inputs()
									snake_case_,       snake_case_,       snake_case_  =      config_and_inputs
									snake_case_  =      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, Any]				) ->Union[str, Any]:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(_UpperCamelCase				)

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

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

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

									snake_case_  =      model(_UpperCamelCase				)

									snake_case_  =      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					:							str				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							List[str]				) ->List[Any]:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(_UpperCamelCase				)

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

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

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

									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

									snake_case_  =      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   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()







			def 							snake_case__(	self					:							List[Any]				) ->Union[str, Any]:
									snake_case_  =      FlaxGPTJModelTester(self				)







			def 							snake_case__(	self					:							int				) ->str:
									for model_class_name in self.all_model_classes:
															snake_case_,       snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							List[Any]				) ->str:
									for model_class_name in self.all_model_classes:
															snake_case_,       snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			@tooslow
			def 							snake_case__(	self					:							str				) ->Dict:
									snake_case_  =      GPTaTokenizer.from_pretrained('''gpt2'''				,  pad_token='''<|endoftext|>'''				,  padding_side='''left'''				)
									snake_case_  =      tokenizer(['''Hello this is a long string''', '''Hey''']				,  return_tensors='''np'''				,  padding=_UpperCamelCase				,  truncation=_UpperCamelCase				)

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

									snake_case_  =      jax.jit(model.generate				)

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

									snake_case_  =      tokenizer.batch_decode(_UpperCamelCase				,  skip_special_tokens=_UpperCamelCase				)

									snake_case_  =      [
									    '''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(_UpperCamelCase				,  _UpperCamelCase				)







			@is_pt_flax_cross_test
			def 							snake_case__(	self					:							List[str]				) ->List[str]:
									snake_case_,       snake_case_  =      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
																					snake_case_  =      self._prepare_for_class(_UpperCamelCase				,  _UpperCamelCase				)
																					snake_case_  =      {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

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

																					snake_case_  =      convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,  _UpperCamelCase				)
																					snake_case_  =      fx_state

																					with torch.no_grad():
																											snake_case_  =      pt_model(**_UpperCamelCase				).to_tuple()

																					snake_case_  =      fx_model(**_UpperCamelCase				).to_tuple()
																					self.assertEqual(len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(_UpperCamelCase				)
																											snake_case_  =      model_class.from_pretrained(_UpperCamelCase				,  from_pt=_UpperCamelCase				)

																					snake_case_  =      fx_model_loaded(**_UpperCamelCase				).to_tuple()
																					self.assertEqual(
																					    len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output_loaded, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output_loaded[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)







			@is_pt_flax_cross_test
			def 							snake_case__(	self					:							Optional[int]				) ->Dict:
									snake_case_,       snake_case_  =      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
																					snake_case_  =      self._prepare_for_class(_UpperCamelCase				,  _UpperCamelCase				)
																					snake_case_  =      {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

																					snake_case_  =      pt_model_class(_UpperCamelCase				).eval()
																					snake_case_  =      model_class(_UpperCamelCase				,  dtype=jnp.floataa				)

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

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

																					with torch.no_grad():
																											snake_case_  =      pt_model(**_UpperCamelCase				).to_tuple()

																					snake_case_  =      fx_model(**_UpperCamelCase				).to_tuple()
																					self.assertEqual(len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(_UpperCamelCase				)
																											snake_case_  =      pt_model_class.from_pretrained(_UpperCamelCase				,  from_flax=_UpperCamelCase				)

																					with torch.no_grad():
																											snake_case_  =      pt_model_loaded(**_UpperCamelCase				).to_tuple()

																					self.assertEqual(
																					    len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)







			@tooslow
			def 							snake_case__(	self					:							Optional[int]				) ->int:
									for model_class_name in self.all_model_classes:
															snake_case_  =      model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''				)
															snake_case_  =      model(np.ones((1, 1)				)				)
															self.assertIsNotNone(_UpperCamelCase				)


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()


