


import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
		import numpy as np

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
		import torch


@require_flax
class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    List[Any]	,							lowerCamelCase_     :    int	,							lowerCamelCase_     :    int=14	,							lowerCamelCase_     :    Optional[int]=7	,							lowerCamelCase_     :    List[Any]=True	,							lowerCamelCase_     :    str=True	,							lowerCamelCase_     :    int=True	,							lowerCamelCase_     :    Any=99	,							lowerCamelCase_     :    Any=32	,							lowerCamelCase_     :    Tuple=5	,							lowerCamelCase_     :    str=4	,							lowerCamelCase_     :    Tuple=37	,							lowerCamelCase_     :    str="gelu"	,							lowerCamelCase_     :    Tuple=0.1	,							lowerCamelCase_     :    Any=0.1	,							lowerCamelCase_     :    Dict=512	,							lowerCamelCase_     :    Any=0.02	,							lowerCamelCase_     :    int=None	,							):
								_lowerCAmelCase  						=parent
								_lowerCAmelCase  						=batch_size
								_lowerCAmelCase  						=seq_length
								_lowerCAmelCase  						=is_training
								_lowerCAmelCase  						=use_input_mask
								_lowerCAmelCase  						=use_labels
								_lowerCAmelCase  						=vocab_size
								_lowerCAmelCase  						=d_model
								_lowerCAmelCase  						=num_hidden_layers
								_lowerCAmelCase  						=num_attention_heads
								_lowerCAmelCase  						=ffn_dim
								_lowerCAmelCase  						=activation_function
								_lowerCAmelCase  						=activation_dropout
								_lowerCAmelCase  						=attention_dropout
								_lowerCAmelCase  						=max_position_embeddings
								_lowerCAmelCase  						=initializer_range
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=0
								_lowerCAmelCase  						=2
								_lowerCAmelCase  						=1






				def        lowerCAmelCase__			(				self     :    Any					):
								_lowerCAmelCase  						=np.clip(ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size					)	,							3	,							self.vocab_size					)

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

								_lowerCAmelCase  						=XGLMConfig(
								    vocab_size=self.vocab_size	,							d_model=self.hidden_size	,							num_layers=self.num_hidden_layers	,							attention_heads=self.num_attention_heads	,							ffn_dim=self.ffn_dim	,							activation_function=self.activation_function	,							activation_dropout=self.activation_dropout	,							attention_dropout=self.attention_dropout	,							max_position_embeddings=self.max_position_embeddings	,							initializer_range=self.initializer_range	,							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        lowerCAmelCase__			(				self     :    str					):
								_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        lowerCAmelCase__			(				self     :    Optional[Any]					):
								_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase  						=self.prepare_config_and_inputs()

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

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






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Union[str, Any]	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    Tuple					):
								_lowerCAmelCase  						=20
								_lowerCAmelCase  						=model_class_name(lowerCamelCase_					)

								_lowerCAmelCase  						=model.init_cache(input_ids.shape[0]	,							lowerCamelCase_					)
								_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=lowerCamelCase_	,							past_key_values=lowerCamelCase_	,							position_ids=lowerCamelCase_	,							)

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

								_lowerCAmelCase  						=model(lowerCamelCase_					)

								_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        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Optional[int]					):
								_lowerCAmelCase  						=20
								_lowerCAmelCase  						=model_class_name(lowerCamelCase_					)

								_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]	,							lowerCamelCase_					)
								_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=lowerCamelCase_	,							past_key_values=lowerCamelCase_	,							position_ids=lowerCamelCase_	,							)
								_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=lowerCamelCase_	,							position_ids=lowerCamelCase_	,							)

								_lowerCAmelCase  						=model(lowerCamelCase_	,							attention_mask=lowerCamelCase_					)
								_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_sentencepiece
@require_flax
class        __lowerCamelCase      (				lowerCamelCase_				,		lowerCamelCase_				,		unittest.TestCase ):


				"""simple docstring"""







				a_:		Dict					      =	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				a_:		Tuple					      =	(FlaxXGLMForCausalLM,) if is_flax_available() else ()






				def        lowerCAmelCase__			(				self     :    Tuple					):
								_lowerCAmelCase  						=FlaxXGLMModelTester(self					)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								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(lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_					)






				def        lowerCAmelCase__			(				self     :    Tuple					):
								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(
												    lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_					)






				@slow
				def        lowerCAmelCase__			(				self     :    Dict					):
								_lowerCAmelCase  						=XGLMTokenizer.from_pretrained("""XGLM"""	,							padding_side="""left"""					)
								_lowerCAmelCase  						=tokenizer(["""Hello this is a long string""", """Hey"""]	,							return_tensors="""np"""	,							padding=lowerCamelCase_	,							truncation=lowerCamelCase_					)

								_lowerCAmelCase  						=FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""					)
								_lowerCAmelCase  						=1
								_lowerCAmelCase  						=False

								_lowerCAmelCase  						=jax.jit(model.generate					)

								_lowerCAmelCase  						=jit_generate(inputs["""input_ids"""]	,							attention_mask=inputs["""attention_mask"""]					).sequences

								_lowerCAmelCase  						=tokenizer.batch_decode(lowerCamelCase_	,							skip_special_tokens=lowerCamelCase_					)

								_lowerCAmelCase  						=[
								    """Hello this is a long string of questions, but I'm not sure if I'm""",
								    """Hey, I'm a newbie to the forum and I'""",
								]

								self.assertListEqual(lowerCamelCase_	,							lowerCamelCase_					)






				@is_pt_flax_cross_test
				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								_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(lowerCamelCase_	,							lowerCamelCase_					)
																_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(lowerCamelCase_	,							lowerCamelCase_					)

																_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(lowerCamelCase_					):
																				_lowerCAmelCase  						=0
																				_lowerCAmelCase  						=1
																				_lowerCAmelCase  						=0
																				_lowerCAmelCase  						=1
																_lowerCAmelCase  						=pt_model_class(lowerCamelCase_					).eval()
																# Flax models don't use the `use_cache` option and cache is not returned as a default.
																# So we disable `use_cache` here for PyTorch model.
																_lowerCAmelCase  						=False
																_lowerCAmelCase  						=model_class(lowerCamelCase_	,							dtype=jnp.floataa					)
																_lowerCAmelCase  						=convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,							lowerCamelCase_					)
																_lowerCAmelCase  						=fx_state

																with torch.no_grad():
																				_lowerCAmelCase  						=pt_model(**lowerCamelCase_					).to_tuple()

																_lowerCAmelCase  						=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_					)
																				_lowerCAmelCase  						=model_class.from_pretrained(lowerCamelCase_	,							from_pt=lowerCamelCase_					)

																_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    Dict					):
								_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(lowerCamelCase_	,							lowerCamelCase_					)
																_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(lowerCamelCase_	,							lowerCamelCase_					)

																_lowerCAmelCase  						=pt_model_class(lowerCamelCase_					).eval()
																_lowerCAmelCase  						=False
																_lowerCAmelCase  						=model_class(lowerCamelCase_	,							dtype=jnp.floataa					)

																_lowerCAmelCase  						=load_flax_weights_in_pytorch_model(lowerCamelCase_	,							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(lowerCamelCase_					):
																				_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(**lowerCamelCase_					).to_tuple()

																_lowerCAmelCase  						=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_					)
																				_lowerCAmelCase  						=pt_model_class.from_pretrained(lowerCamelCase_	,							from_flax=lowerCamelCase_					)

																with torch.no_grad():
																				_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								for model_class_name in self.all_model_classes:
												_lowerCAmelCase  						=model_class_name.from_pretrained("""facebook/xglm-564M"""					)
												_lowerCAmelCase  						=model(np.ones((1, 1)					)					)
												self.assertIsNotNone(lowerCamelCase_					)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''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

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

