
'''simple docstring'''
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        SCREAMING_SNAKE_CASE__	:







				def __init__(    self		,							A_		,							A_=14		,							A_=7		,							A_=True		,							A_=True		,							A_=True		,							A_=99		,							A_=32		,							A_=5		,							A_=4		,							A_=37		,							A_="gelu"		,							A_=0.1		,							A_=0.1		,							A_=512		,							A_=0.02		,							A_=None		,							)->						str:







											'''simple docstring'''






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







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






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

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

											UpperCamelCase					       =			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=A_		,							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 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






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







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.prepare_config_and_inputs()

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

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







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							A_			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			20
											UpperCamelCase					       =			model_class_name(A_			)

											UpperCamelCase					       =			model.init_cache(input_ids.shape[0]		,							A_			)
											UpperCamelCase					       =			jnp.ones((input_ids.shape[0], max_decoder_length)		,							dtype='i4'			)

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

											UpperCamelCase					       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,							dtype='i4'			)
											UpperCamelCase					       =			model(
											    input_ids[:, -1:]		,							attention_mask=A_		,							past_key_values=outputs_cache.past_key_values		,							position_ids=A_		,							)

											UpperCamelCase					       =			model(A_			)

											UpperCamelCase					       =			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 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							A_			)->						Tuple:







											'''simple docstring'''






											UpperCamelCase					       =			20
											UpperCamelCase					       =			model_class_name(A_			)

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

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

											UpperCamelCase					       =			model(
											    input_ids[:, :-1]		,							attention_mask=A_		,							past_key_values=A_		,							position_ids=A_		,							)
											UpperCamelCase					       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,							dtype='i4'			)
											UpperCamelCase					       =			model(
											    input_ids[:, -1:]		,							past_key_values=outputs_cache.past_key_values		,							attention_mask=A_		,							position_ids=A_		,							)

											UpperCamelCase					       =			model(A_		,							attention_mask=A_			)
											UpperCamelCase					       =			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        SCREAMING_SNAKE_CASE__	(			snake_case_				,    snake_case_				,    unittest.TestCase):
				lowerCAmelCase_			       =				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				lowerCAmelCase_			       =				(FlaxXGLMForCausalLM,) if is_flax_available() else ()







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			FlaxXGLMModelTester(self			)







				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											for model_class_name in self.all_model_classes:
																		UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward(A_		,							A_		,							A_		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											for model_class_name in self.all_model_classes:
																		UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward_with_attn_mask(
																		    A_		,							A_		,							A_		,							A_			)







				@slow
				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






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

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

											UpperCamelCase					       =			jax.jit(model.generate			)

											UpperCamelCase					       =			jit_generate(inputs['input_ids']		,							attention_mask=inputs['attention_mask']			).sequences

											UpperCamelCase					       =			tokenizer.batch_decode(A_		,							skip_special_tokens=A_			)

											UpperCamelCase					       =			[
											    '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(A_		,							A_			)







				@is_pt_flax_cross_test
				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






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

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

																									UpperCamelCase       ,     UpperCamelCase					       =			pt_inputs['input_ids'].shape
																									UpperCamelCase					       =			np.random.randint(0		,							seq_length - 1		,							size=(batch_size,)			)
																									for batch_idx, start_index in enumerate(A_			):
																																UpperCamelCase					       =			0
																																UpperCamelCase					       =			1
																																UpperCamelCase					       =			0
																																UpperCamelCase					       =			1
																									UpperCamelCase					       =			pt_model_class(A_			).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.
																									UpperCamelCase					       =			False
																									UpperCamelCase					       =			model_class(A_		,							dtype=jnp.floataa			)
																									UpperCamelCase					       =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,							A_			)
																									UpperCamelCase					       =			fx_state

																									with torch.no_grad():
																																UpperCamelCase					       =			pt_model(**A_			).to_tuple()

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

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

																									UpperCamelCase					       =			fx_model_loaded(**A_			).to_tuple()
																									self.assertEqual(
																									    len(A_			)		,							len(A_			)		,							'Output lengths differ between Flax and PyTorch'			)
																									for fx_output_loaded, pt_output in zip(A_		,							A_			):
																																self.assert_almost_equals(fx_output_loaded[:, -1]		,							pt_output[:, -1].numpy()		,							4e-2			)







				@is_pt_flax_cross_test
				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






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

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

																									UpperCamelCase					       =			pt_model_class(A_			).eval()
																									UpperCamelCase					       =			False
																									UpperCamelCase					       =			model_class(A_		,							dtype=jnp.floataa			)

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

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

																									with torch.no_grad():
																																UpperCamelCase					       =			pt_model(**A_			).to_tuple()

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

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

																									with torch.no_grad():
																																UpperCamelCase					       =			pt_model_loaded(**A_			).to_tuple()

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







				@slow
				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											for model_class_name in self.all_model_classes:
																		UpperCamelCase					       =			model_class_name.from_pretrained('facebook/xglm-564M'			)
																		UpperCamelCase					       =			model(np.ones((1, 1)			)			)
																		self.assertIsNotNone(A_			)






'''simple docstring'''
import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		EsmTokenizer



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






				"""simple docstring"""







				super().setUp()
				snake_case   :    Optional[int]			       =		tempfile.mkdtemp()
				# fmt: off
				snake_case   :    List[str]			       =		["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
				# fmt: on
				snake_case   :    List[Any]			       =		os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['''vocab_file''']	)
				with open(self.vocab_file   ,     '''w'''   ,     encoding='''utf-8'''	) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)



		def 	lowerCAmelCase							(	self							:       str   ,     **UpperCamelCase__							:       Union[str, Any]	)		->       List[PreTrainedTokenizerBase]:






				"""simple docstring"""







				return [self.get_tokenizer(**UpperCamelCase__	)]



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       Tuple	)		->       PreTrainedTokenizer:






				"""simple docstring"""







				return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    str			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    Optional[int]			       =		tokenizer.tokenize('''LAGVS'''	)
				self.assertListEqual(UpperCamelCase__   ,     ['''L''', '''A''', '''G''', '''V''', '''S''']	)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__	)   ,     [4, 5, 6, 7, 8]	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    List[Any]			       =		'''LAGVS'''
				self.assertListEqual(tokenizer.encode(UpperCamelCase__	)   ,     [0, 4, 5, 6, 7, 8, 2]	)



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       int:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    int			       =		['''LAGVS''', '''WCB''']
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__   ,     padding=UpperCamelCase__	)['''input_ids''']

				self.assertListEqual(UpperCamelCase__   ,     [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)



		def 	lowerCAmelCase							(	self							:       int	)		->       List[str]:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    List[str]			       =		['''LAGVS''', '''WCB''']
				snake_case   :    Any			       =		tokenizer(UpperCamelCase__   ,     padding=UpperCamelCase__	)['''input_ids''']

				self.assertListEqual(UpperCamelCase__   ,     [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       int:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_tokenizers(fast=UpperCamelCase__	)
				for tokenizer in tokenizers:
						with self.subTest(f'{tokenizer.__class__.__name__}'	):
								snake_case   :    int			       =		'''<unk>'''
								snake_case   :    int			       =		'''<mask>'''

								snake_case   :    Any			       =		tokenizer.tokenize(UpperCamelCase__	)
								snake_case   :    Any			       =		tokenizer.tokenize(UpperCamelCase__	)

								self.assertEqual(len(UpperCamelCase__	)   ,     1	)
								self.assertEqual(len(UpperCamelCase__	)   ,     1	)
								self.assertEqual(token_a[0]   ,     UpperCamelCase__	)
								self.assertEqual(token_a[0]   ,     UpperCamelCase__	)


