
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch







class 	A_       :




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=14		,   snake_case=7		,   snake_case=True		,   snake_case=True		,   snake_case=False		,   snake_case=True		,   snake_case=99		,   snake_case=32		,   snake_case=5		,   snake_case=4		,   snake_case=37		,   snake_case="gelu"		,   snake_case=0.1		,   snake_case=0.1		,   snake_case=512		,   snake_case=0.02		,   ):
		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					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		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					  =   GPTaConfig(
		    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=snake_case		,   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      SCREAMING_SNAKE_CASE__						(	self	):
		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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase  , lowercase					  =   self.prepare_config_and_inputs()

		lowercase					  =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
		lowercase					  =   ids_tensor([self.batch_size, self.seq_length]		,   vocab_size=2	)

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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   20
		lowercase					  =   model_class_name(snake_case	)

		lowercase					  =   model.init_cache(input_ids.shape[0]		,   snake_case	)
		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=snake_case		,   past_key_values=snake_case		,   position_ids=snake_case		,   )

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

		lowercase					  =   model(snake_case	)

		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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   20
		lowercase					  =   model_class_name(snake_case	)

		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]		,   snake_case	)
		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=snake_case		,   past_key_values=snake_case		,   position_ids=snake_case		,   )
		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=snake_case		,   position_ids=snake_case		,   )

		lowercase					  =   model(snake_case		,   attention_mask=snake_case	)

		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 	A_       (      __lowerCamelCase				,     __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
	_UpperCamelCase					:					List[Any]										=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   FlaxGPTaModelTester(self	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		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(snake_case		,   snake_case		,   snake_case		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		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(
			    snake_case		,   snake_case		,   snake_case		,   snake_case	)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   GPTaTokenizer.from_pretrained('gpt2'		,   pad_token='</s>'		,   padding_side='left'	)
		lowercase					  =   tokenizer(['Hello this is a long string', 'Hey']		,   return_tensors='np'		,   padding=snake_case		,   truncation=snake_case	)

		lowercase					  =   FlaxGPTaLMHeadModel.from_pretrained('gpt2'	)
		lowercase					  =   False
		lowercase					  =   model.config.eos_token_id

		lowercase					  =   jax.jit(model.generate	)

		lowercase					  =   jit_generate(inputs['input_ids']		,   attention_mask=inputs['attention_mask']	).sequences

		lowercase					  =   tokenizer.batch_decode(snake_case		,   skip_special_tokens=snake_case	)

		lowercase					  =   [
		    'Hello this is a long string of words. I\'m going to start with the first one.\n',
		    'Hey, I\'m not sure if I\'m going to be able to do',
		]

		self.assertListEqual(snake_case		,   snake_case	)





	@is_pt_flax_cross_test
	def      SCREAMING_SNAKE_CASE__						(	self	):
		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(snake_case		,   snake_case	)
				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(snake_case		,   snake_case	)

				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(snake_case	):
					lowercase					  =   0
					lowercase					  =   1
					lowercase					  =   0
					lowercase					  =   1
				lowercase					  =   pt_model_class(snake_case	).eval()
				lowercase					  =   model_class(snake_case		,   dtype=jnp.floataa	)

				lowercase					  =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,   snake_case	)
				lowercase					  =   fx_state

				with torch.no_grad():
					lowercase					  =   pt_model(**snake_case	).to_tuple()

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

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

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





	@is_pt_flax_cross_test
	def      SCREAMING_SNAKE_CASE__						(	self	):
		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(snake_case		,   snake_case	)
				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(snake_case		,   snake_case	)

				lowercase					  =   pt_model_class(snake_case	).eval()
				lowercase					  =   model_class(snake_case		,   dtype=jnp.floataa	)

				lowercase					  =   load_flax_weights_in_pytorch_model(snake_case		,   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(snake_case	):
					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(**snake_case	).to_tuple()

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

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

				with torch.no_grad():
					lowercase					  =   pt_model_loaded(**snake_case	).to_tuple()

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





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		for model_class_name in self.all_model_classes:
			lowercase					  =   model_class_name.from_pretrained('gpt2'		,   from_pt=snake_case	)
			lowercase					  =   model(np.ones((1, 1)	)	)
			self.assertIsNotNone(snake_case	)



# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase     				=			logging.get_logger(__name__)


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	from transformers import Trainer







	class 	A_       (      __lowerCamelCase       ):




		'''simple docstring'''


		def __init__(	self		,   snake_case = 101	):
			lowercase					  =   length





		def __len__(	self	):
			return self.length





		def __getitem__(	self		,   snake_case	):
			return i







	class 	A_       :




		'''simple docstring'''


		def __call__(	self		,   snake_case	):
			return {"input_ids": torch.tensor(snake_case	), "labels": torch.tensor(snake_case	)}







	class 	A_       (      nn.Module       ):




		'''simple docstring'''


		def __init__(	self	):
			super().__init__()
			# Add some (unused) params otherwise DDP will complain.
			lowercase					  =   nn.Linear(120		,   80	)





		def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case=None	):
			if labels is not None:
				return torch.tensor(0.0		,   device=input_ids.device	), input_ids
			else:
				return input_ids






def 							UpperCAmelCase_		(							):
	lowercase					  =   HfArgumentParser((TrainingArguments,)							)
	sys.argv += ["--output_dir", "./examples"]
	lowercase					  =   parser.parse_args_into_dataclasses()[0]

	logger.warning(
	    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
	    F'''tpu_num_cores: {training_args.tpu_num_cores}'''			,							)

	# Essentially, what we want to verify in the distributed case is
	# that we get all samples back, in the right order.
	# (this is crucial for prediction for instance)
	for dataset_length in [1001, 256, 15]:
		lowercase					  =   DummyDataset(__SCREAMING_SNAKE_CASE							)

		def compute_metrics(__SCREAMING_SNAKE_CASE							) -> Dict:
			lowercase					  =   list(range(len(__SCREAMING_SNAKE_CASE							)							)							)
			lowercase					  =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

		lowercase					  =   Trainer(
		    model=DummyModel()			,							args=__SCREAMING_SNAKE_CASE			,							data_collator=DummyDataCollator()			,							eval_dataset=__SCREAMING_SNAKE_CASE			,							compute_metrics=__SCREAMING_SNAKE_CASE			,							)
		lowercase					  =   trainer.evaluate()
		logger.info(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   2

		lowercase					  =   trainer.evaluate()
		logger.info(__SCREAMING_SNAKE_CASE							)
		if metrics["eval_success"] is not True:
			logger.error(__SCREAMING_SNAKE_CASE							)
			exit(1							)

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   None

	logger.info('🔥 All distributed tests successful'							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()

