

"""simple docstring"""




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 			__UpperCamelCase :


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple=1_4     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=7     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=True     ,     __SCREAMING_SNAKE_CASE :		str=True     ,     __SCREAMING_SNAKE_CASE :		Dict=False     ,     __SCREAMING_SNAKE_CASE :		List[Any]=True     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=9_9     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=3_2     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=4     ,     __SCREAMING_SNAKE_CASE :		Tuple=4     ,     __SCREAMING_SNAKE_CASE :		Dict=[[["global", "local"], 2]]     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=3_7     ,     __SCREAMING_SNAKE_CASE :		List[Any]="gelu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0.1     ,     __SCREAMING_SNAKE_CASE :		List[str]=0.1     ,     __SCREAMING_SNAKE_CASE :		Dict=5_1_2     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=7     ,     __SCREAMING_SNAKE_CASE :		int=0.0_2     ,     ):
							A			=					parent
							A			=					batch_size
							A			=					seq_length
							A			=					is_training
							A			=					use_input_mask
							A			=					use_token_type_ids
							A			=					use_labels
							A			=					vocab_size
							A			=					hidden_size
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					attention_types
							A			=					intermediate_size
							A			=					hidden_act
							A			=					hidden_dropout_prob
							A			=					attention_probs_dropout_prob
							A			=					max_position_embeddings
							A			=					window_size
							A			=					initializer_range
							A			=					None
							A			=					vocab_size - 1
							A			=					vocab_size - 1
							A			=					vocab_size - 1


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size)

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

							A			=					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=_SCREAMING_SNAKE_CASE     ,     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 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					self.prepare_config_and_inputs()
							A			,    A			,    A			=					config_and_inputs
							A			=					{"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					2_0
							A			=					model_class_name(_SCREAMING_SNAKE_CASE)

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

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

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

							A			=					model(_SCREAMING_SNAKE_CASE)

							A			=					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 :		Any     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		int):
							A			=					2_0
							A			=					model_class_name(_SCREAMING_SNAKE_CASE)

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

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

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

							A			=					model(_SCREAMING_SNAKE_CASE     ,     attention_mask=_SCREAMING_SNAKE_CASE)

							A			=					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						):
		SCREAMING_SNAKE_CASE								=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		SCREAMING_SNAKE_CASE								=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					FlaxGPTNeoModelTester(self)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							for model_class_name in self.all_model_classes:
												A			,    A			,    A			=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							for model_class_name in self.all_model_classes:
												A			,    A			,    A			=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE)


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					GPTaTokenizer.from_pretrained("gpt2"     ,     pad_token="<|endoftext|>"     ,     padding_side="left")
							A			=					tokenizer(["Hello this is a long string", "Hey"]     ,     return_tensors="np"     ,     padding=_SCREAMING_SNAKE_CASE     ,     truncation=_SCREAMING_SNAKE_CASE)

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

							A			=					jax.jit(model.generate)

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

							A			=					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     ,     skip_special_tokens=_SCREAMING_SNAKE_CASE)

							A			=					[
							    "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(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE)


		@is_pt_flax_cross_test
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			,    A			=					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
																	A			=					self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE)
																	A			=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

																	A			=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,     _SCREAMING_SNAKE_CASE)
																	A			=					fx_state

																	with torch.no_grad():
																						A			=					pt_model(**_SCREAMING_SNAKE_CASE).to_tuple()

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

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

																	A			=					fx_model_loaded(**_SCREAMING_SNAKE_CASE).to_tuple()
																	self.assertEqual(
																	    len(_SCREAMING_SNAKE_CASE)     ,     len(_SCREAMING_SNAKE_CASE)     ,     "Output lengths differ between Flax and PyTorch")
																	for fx_output_loaded, pt_output in zip(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_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 :		int):
							A			,    A			=					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
																	A			=					self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE)
																	A			=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

																	A			=					pt_model_class(_SCREAMING_SNAKE_CASE).eval()
																	A			=					model_class(_SCREAMING_SNAKE_CASE     ,     dtype=jnp.floataa)

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

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

																	with torch.no_grad():
																						A			=					pt_model(**_SCREAMING_SNAKE_CASE).to_tuple()

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

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

																	with torch.no_grad():
																						A			=					pt_model_loaded(**_SCREAMING_SNAKE_CASE).to_tuple()

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


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							for model_class_name in self.all_model_classes:
												A			=					model_class_name.from_pretrained("EleutherAI/gpt-neo-125M")
												A			=					model(np.ones((1, 1)))
												self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__snake_case   :      Optional[int]						    =  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Optional[Any]						    =  ['MBart50Tokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Dict						    =  ['MBart50TokenizerFast']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  __snake_case   :      Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






