




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 		lowercase_       :




					def __init__(   self:		Dict,     _lowercase:		List[str],     _lowercase:		List[Any]=14,     _lowercase:		Tuple=7,     _lowercase:		Union[str, Any]=True,     _lowercase:		Any=True,     _lowercase:		Tuple=False,     _lowercase:		Optional[Any]=True,     _lowercase:		List[str]=99,     _lowercase:		Optional[Any]=32,     _lowercase:		Any=4,     _lowercase:		Optional[int]=4,     _lowercase:		int=[[["global", "local"], 2]],     _lowercase:		int=37,     _lowercase:		Any="gelu",     _lowercase:		Optional[Any]=0.1,     _lowercase:		Tuple=0.1,     _lowercase:		Any=512,     _lowercase:		List[Any]=7,     _lowercase:		str=0.02,     ):


									'''simple docstring'''


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




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


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

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

									__lowerCAmelCase					=  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:		List[str]):


									'''simple docstring'''


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




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		Optional[Any],     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

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

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

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

									__lowerCAmelCase					=  model(_lowercase)

									__lowerCAmelCase					=  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:		Tuple,     _lowercase:		Dict,     _lowercase:		Any,     _lowercase:		Any,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

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

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

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

									__lowerCAmelCase					=  model(_lowercase,     attention_mask=_lowercase)

									__lowerCAmelCase					=  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 		lowercase_       (	__UpperCAmelCase						,							__UpperCAmelCase						,							unittest.TestCase				):
					__UpperCamelCase     	= (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					__UpperCamelCase     	= (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxGPTNeoModelTester(self)




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									for model_class_name in self.all_model_classes:
													__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(_lowercase,     _lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									for model_class_name in self.all_model_classes:
													__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  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:		Any):


									'''simple docstring'''


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

									__lowerCAmelCase					=  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""")
									__lowerCAmelCase					=  False
									__lowerCAmelCase					=  model.config.eos_token_id

									__lowerCAmelCase					=  jax.jit(model.generate)

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

									__lowerCAmelCase					=  tokenizer.batch_decode(_lowercase,     skip_special_tokens=_lowercase)

									__lowerCAmelCase					=  [
									    """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:		str):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  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					=  self._prepare_for_class(_lowercase,     _lowercase)
																	__lowerCAmelCase					=  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

																	__lowerCAmelCase					=  convert_pytorch_state_dict_to_flax(pt_model.state_dict(),     _lowercase)
																	__lowerCAmelCase					=  fx_state

																	with torch.no_grad():
																					__lowerCAmelCase					=  pt_model(**_lowercase).to_tuple()

																	__lowerCAmelCase					=  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					=  model_class.from_pretrained(_lowercase,     from_pt=_lowercase)

																	__lowerCAmelCase					=  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:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  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					=  self._prepare_for_class(_lowercase,     _lowercase)
																	__lowerCAmelCase					=  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

																	__lowerCAmelCase					=  pt_model_class(_lowercase).eval()
																	__lowerCAmelCase					=  model_class(_lowercase,     dtype=jnp.floataa)

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

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

																	with torch.no_grad():
																					__lowerCAmelCase					=  pt_model(**_lowercase).to_tuple()

																	__lowerCAmelCase					=  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					=  pt_model_class.from_pretrained(_lowercase,     from_flax=_lowercase)

																	with torch.no_grad():
																					__lowerCAmelCase					=  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:		Optional[Any]):


									'''simple docstring'''


									for model_class_name in self.all_model_classes:
													__lowerCAmelCase					=  model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
													__lowerCAmelCase					=  model(np.ones((1, 1)))
													self.assertIsNotNone(_lowercase)







'''simple docstring'''







import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						import torch

						from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase			(					unittest.TestCase      ):




		@slow
		@unittest.skip("Model is not available."   )
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"nvidia/megatron-gpt2-345m/"
								if "MYDIR" in os.environ:
														_SCREAMING_SNAKE_CASE					=			os.path.join(os.environ["MYDIR"]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			GPTaLMHeadModel.from_pretrained(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.half()

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       ,    device=UpperCamelCase       ,    dtype=torch.long       ,    )

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   ).logits

								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 9, 50_257)   )
								self.assertEqual(output.shape       ,    UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [
								        4.94_14,
								        -0.29_20,
								        -1.21_48,
								        -4.02_73,
								        -0.51_61,
								        -5.21_09,
								        -1.24_12,
								        -1.83_01,
								        -1.77_34,
								        -4.71_48,
								        -0.23_17,
								        -1.08_11,
								        -2.17_77,
								        0.41_41,
								        -3.79_69,
								        -4.05_86,
								        -2.53_32,
								        -3.38_09,
								        4.38_67,
								    ]       ,    device=UpperCamelCase       ,    dtype=torch.half       ,    )

								for i in range(19   ):
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			8 * i // 17, 2_792 * i  # along the diagonal
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			output[0, r, c], expected_diag[i]
														_SCREAMING_SNAKE_CASE					=			F'row={r} col={c} computed={computed} expected={expected}'
														self.assertAlmostEqual(UpperCamelCase       ,    UpperCamelCase       ,    delta=1e-4       ,    msg=UpperCamelCase   )