
"""simple docstring"""






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     ,						_lowerCamelCase     ,						_lowerCamelCase=14     ,						_lowerCamelCase=7     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=False     ,						_lowerCamelCase=True     ,						_lowerCamelCase=99     ,						_lowerCamelCase=32     ,						_lowerCamelCase=5     ,						_lowerCamelCase=4     ,						_lowerCamelCase=37     ,						_lowerCamelCase="gelu"     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=512     ,						_lowerCamelCase=0.02     ,						) ->       Optional[int]:




										'''simple docstring'''
										__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					) ->       List[Any]:




										'''simple docstring'''
										__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=_lowerCamelCase     ,						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					) ->       str:




										'''simple docstring'''
										__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					) ->       Dict:




										'''simple docstring'''
										__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     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					20
										__lowercase						      =					model_class_name(_lowerCamelCase					)

										__lowercase						      =					model.init_cache(input_ids.shape[0]     ,						_lowerCamelCase					)
										__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=_lowerCamelCase     ,						past_key_values=_lowerCamelCase     ,						position_ids=_lowerCamelCase     ,						)

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

										__lowercase						      =					model(_lowerCamelCase					)

										__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     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					20
										__lowercase						      =					model_class_name(_lowerCamelCase					)

										__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]     ,						_lowerCamelCase					)
										__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=_lowerCamelCase     ,						past_key_values=_lowerCamelCase     ,						position_ids=_lowerCamelCase     ,						)
										__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=_lowerCamelCase     ,						position_ids=_lowerCamelCase     ,						)

										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase					)

										__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    (   __a				,      __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: Tuple					      =      (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
							_lowerCamelCase				: str					      =      (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					FlaxGPTaModelTester(self					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[Any]:




										'''simple docstring'''
										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(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										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(
													    _lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					GPTaTokenizer.from_pretrained("gpt2"     ,						pad_token="</s>"     ,						padding_side="left"					)
										__lowercase						      =					tokenizer(["Hello this is a long string", "Hey"]     ,						return_tensors="np"     ,						padding=_lowerCamelCase     ,						truncation=_lowerCamelCase					)

										__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(_lowerCamelCase     ,						skip_special_tokens=_lowerCamelCase					)

										__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(_lowerCamelCase     ,						_lowerCamelCase					)
							@is_pt_flax_cross_test
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__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(_lowerCamelCase     ,						_lowerCamelCase					)
																__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(_lowerCamelCase     ,						_lowerCamelCase					)

																__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(_lowerCamelCase					):
																			__lowercase						      =					0
																			__lowercase						      =					1
																			__lowercase						      =					0
																			__lowercase						      =					1
																__lowercase						      =					pt_model_class(_lowerCamelCase					).eval()
																__lowercase						      =					model_class(_lowerCamelCase     ,						dtype=jnp.floataa					)

																__lowercase						      =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,						_lowerCamelCase					)
																__lowercase						      =					fx_state

																with torch.no_grad():
																			__lowercase						      =					pt_model(**_lowerCamelCase					).to_tuple()

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

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

																__lowercase						      =					fx_model_loaded(**_lowerCamelCase					).to_tuple()
																self.assertEqual(
																    len(_lowerCamelCase					)     ,						len(_lowerCamelCase					)     ,						"Output lengths differ between Flax and PyTorch"					)
																for fx_output_loaded, pt_output in zip(_lowerCamelCase     ,						_lowerCamelCase					):
																			self.assert_almost_equals(fx_output_loaded[:, -1]     ,						pt_output[:, -1].numpy()     ,						4e-2					)
							@is_pt_flax_cross_test
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										__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(_lowerCamelCase     ,						_lowerCamelCase					)
																__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(_lowerCamelCase     ,						_lowerCamelCase					)

																__lowercase						      =					pt_model_class(_lowerCamelCase					).eval()
																__lowercase						      =					model_class(_lowerCamelCase     ,						dtype=jnp.floataa					)

																__lowercase						      =					load_flax_weights_in_pytorch_model(_lowerCamelCase     ,						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(_lowerCamelCase					):
																			__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(**_lowerCamelCase					).to_tuple()

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

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

																with torch.no_grad():
																			__lowercase						      =					pt_model_loaded(**_lowerCamelCase					).to_tuple()

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





							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										for model_class_name in self.all_model_classes:
													__lowercase						      =					model_class_name.from_pretrained("gpt2"     ,						from_pt=_lowerCamelCase					)
													__lowercase						      =					model(np.ones((1, 1)					)					)
													self.assertIsNotNone(_lowerCamelCase					)







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





