





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   UpperCamelCase_       :


							def __init__(							self    :str							,				__A    :List[Any]							,				__A    :Any=14							,				__A    :Optional[Any]=7							,				__A    :Optional[int]=True							,				__A    :List[Any]=True							,				__A    :int=False							,				__A    :str=True							,				__A    :Optional[int]=99							,				__A    :Union[str, Any]=32							,				__A    :Union[str, Any]=4							,				__A    :List[Any]=4							,				__A    :int=4							,				__A    :List[str]=37							,				__A    :int="gelu"							,				__A    :List[Any]=0.1							,				__A    :List[str]=0.1							,				__A    :Optional[Any]=512							,				__A    :int=0.0_2							,				)   ->   List[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Optional[Any]		)   ->   Optional[int]:



									"""simple docstring"""



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

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

									SCREAMING_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=__A							,				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    :Optional[int]		)   ->   List[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :int							,				__A    :int							,				__A    :Any							,				__A    :Any							,				__A    :Optional[Any]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      20
									SCREAMING_SNAKE_CASE__			=      model_class_name(__A		)

									SCREAMING_SNAKE_CASE__			=      model.init_cache(input_ids.shape[0]							,				__A		)
									SCREAMING_SNAKE_CASE__			=      jnp.ones((input_ids.shape[0], max_decoder_length)							,				dtype="""i4"""		)

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

									SCREAMING_SNAKE_CASE__			=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,				dtype="""i4"""		)
									SCREAMING_SNAKE_CASE__			=      model(
									    input_ids[:, -1:]							,				attention_mask=__A							,				past_key_values=outputs_cache.past_key_values							,				position_ids=__A							,				)

									SCREAMING_SNAKE_CASE__			=      model(__A		)

									SCREAMING_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    :Dict							,				__A    :List[Any]							,				__A    :List[str]							,				__A    :Optional[Any]							,				__A    :Union[str, Any]		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      20
									SCREAMING_SNAKE_CASE__			=      model_class_name(__A		)

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

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

									SCREAMING_SNAKE_CASE__			=      model(
									    input_ids[:, :-1]							,				attention_mask=__A							,				past_key_values=__A							,				position_ids=__A							,				)
									SCREAMING_SNAKE_CASE__			=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,				dtype="""i4"""		)
									SCREAMING_SNAKE_CASE__			=      model(
									    input_ids[:, -1:]							,				past_key_values=outputs_cache.past_key_values							,				attention_mask=__A							,				position_ids=__A							,				)

									SCREAMING_SNAKE_CASE__			=      model(__A							,				attention_mask=__A		)

									SCREAMING_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   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							lowerCamelCase_           =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()


							def      _snake_case  (							self    :Tuple		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      FlaxGPTJModelTester(self		)


							def      _snake_case  (							self    :Union[str, Any]		)   ->   int:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__A							,				__A							,				__A							,				__A		)


							def      _snake_case  (							self    :Dict		)   ->   Dict:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __A							,				__A							,				__A							,				__A		)


							@tooslow
							def      _snake_case  (							self    :List[str]		)   ->   int:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__			=      jax.jit(model.generate		)

									SCREAMING_SNAKE_CASE__			=      jit_generate(
									    inputs["""input_ids"""]							,				attention_mask=inputs["""attention_mask"""]							,				pad_token_id=tokenizer.pad_token_id		).sequences

									SCREAMING_SNAKE_CASE__			=      tokenizer.batch_decode(__A							,				skip_special_tokens=__A		)

									SCREAMING_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(__A							,				__A		)


							@is_pt_flax_cross_test
							def      _snake_case  (							self    :Any		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_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
													SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(__A							,				__A		)
													SCREAMING_SNAKE_CASE__			=      {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

													SCREAMING_SNAKE_CASE__			=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,				__A		)
													SCREAMING_SNAKE_CASE__			=      fx_state

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__			=      pt_model(**__A		).to_tuple()

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

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

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


							@is_pt_flax_cross_test
							def      _snake_case  (							self    :Any		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_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
													SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(__A							,				__A		)
													SCREAMING_SNAKE_CASE__			=      {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

													SCREAMING_SNAKE_CASE__			=      pt_model_class(__A		).eval()
													SCREAMING_SNAKE_CASE__			=      model_class(__A							,				dtype=jnp.floataa		)

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

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

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__			=      pt_model(**__A		).to_tuple()

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

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

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__			=      pt_model_loaded(**__A		).to_tuple()

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


							@tooslow
							def      _snake_case  (							self    :Union[str, Any]		)   ->   Tuple:



									"""simple docstring"""



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






from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		return {key.lstrip("""-"""    ): value for key, value in zip(unknown_args[::2]					,   unknown_args[1::2]    )}






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      ArgumentParser(
		    """HuggingFace Datasets CLI tool"""					,   usage="""datasets-cli <command> [<args>]"""					,   allow_abbrev=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      parser.add_subparsers(help="""datasets-cli command helpers"""    )
		set_verbosity_info()

		# Register commands
		ConvertCommand.register_subcommand(UpperCamelCase__    )
		EnvironmentCommand.register_subcommand(UpperCamelCase__    )
		TestCommand.register_subcommand(UpperCamelCase__    )
		RunBeamCommand.register_subcommand(UpperCamelCase__    )
		DummyDataCommand.register_subcommand(UpperCamelCase__    )

		# Parse args
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      parser.parse_known_args()
		if not hasattr(UpperCamelCase__					,   """func"""    ):
				parser.print_help()
				exit(1    )
		SCREAMING_SNAKE_CASE__			=      parse_unknown_args(UpperCamelCase__    )

		# Run
		SCREAMING_SNAKE_CASE__			=      args.func(UpperCamelCase__					,   **UpperCamelCase__    )
		service.run()


if __name__ == "__main__":
	main()