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        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=14				,       lowerCAmelCase__=7				,       lowerCAmelCase__=True				,       lowerCAmelCase__=True				,       lowerCAmelCase__=True				,       lowerCAmelCase__=99				,       lowerCAmelCase__=32				,       lowerCAmelCase__=5				,       lowerCAmelCase__=4				,       lowerCAmelCase__=37				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=None				,       )					-> Dict:

							'''simple docstring'''


							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							seq_length
							__lowercase			      =							is_training
							__lowercase			      =							use_input_mask
							__lowercase			      =							use_labels
							__lowercase			      =							vocab_size
							__lowercase			      =							d_model
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							ffn_dim
							__lowercase			      =							activation_function
							__lowercase			      =							activation_dropout
							__lowercase			      =							attention_dropout
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							initializer_range
							__lowercase			      =							None
							__lowercase			      =							0
							__lowercase			      =							2
							__lowercase			      =							1





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							np.clip(ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size						)				,       3				,       self.vocab_size						)

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

							__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''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						)					-> int:

							'''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__						)					-> int:

							'''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__						)					-> Union[str, Any]:

							'''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_sentencepiece
@require_flax
class        _UpperCamelCase				(						_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase			):


			"""simple docstring"""






			__a  :			Union[str, Any]      			=						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			__a  :			str      			=						(FlaxXGLMForCausalLM,) if is_flax_available() else ()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							FlaxXGLMModelTester(self						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''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						)					-> List[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_with_attn_mask(
											    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							XGLMTokenizer.from_pretrained('''XGLM'''				,       padding_side='''left'''						)
							__lowercase			      =							tokenizer(['''Hello this is a long string''', '''Hey''']				,       return_tensors='''np'''				,       padding=lowerCAmelCase__				,       truncation=lowerCAmelCase__						)

							__lowercase			      =							FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
							__lowercase			      =							1
							__lowercase			      =							False

							__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 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 	_SCREAMING_SNAKE_CASE      (       self						)					-> 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()
															# 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.
															__lowercase			      =							False
															__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[Any]:

							'''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			      =							False
															__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						)					-> Optional[int]:

							'''simple docstring'''


							for model_class_name in self.all_model_classes:
											__lowercase			      =							model_class_name.from_pretrained('''facebook/xglm-564M'''						)
											__lowercase			      =							model(np.ones((1, 1)						)						)
											self.assertIsNotNone(lowerCAmelCase__						)
from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


__a  :					Union[str, Any]          =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      lowercase							):
								return _split_by_node_map_style_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)
				else:
								return _split_by_node_iterable_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)