

'''simple docstring'''





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    GPTaTokenizer
				lowerCamelCase_		       =    GPTaTokenizerFast
				lowerCamelCase_		       =    True
				lowerCamelCase_		       =    {'add_prefix_space': True}
				lowerCamelCase_		       =    False







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase :							Any    		=[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										    '''<|endoftext|>''',
										]
										lowercase :							Optional[Any]    		=dict(zip(UpperCAmelCase__		,					range(len(UpperCAmelCase__   )   )   )   )
										lowercase :							Tuple    		=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowercase :							Dict    		={'''unk_token''': '''<unk>'''}

										lowercase :							Optional[Any]    		=os.path.join(self.tmpdirname		,					VOCAB_FILES_NAMES['''vocab_file''']   )
										lowercase :							str    		=os.path.join(self.tmpdirname		,					VOCAB_FILES_NAMES['''merges_file''']   )
										with open(self.vocab_file		,					'''w'''		,					encoding='''utf-8'''   ) as fp:
																fp.write(json.dumps(UpperCAmelCase__   ) + '''\n'''   )
										with open(self.merges_file		,					'''w'''		,					encoding='''utf-8'''   ) as fp:
																fp.write('''\n'''.join(UpperCAmelCase__   )   )







				def  lowerCamelCase_	(       self     :		str		,					**UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map   )
										return GPTaTokenizer.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str		,					**UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map   )
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		='''lower newer'''
										lowercase :							Any    		='''lower newer'''
										return input_text, output_text







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Any    		=GPTaTokenizer(self.vocab_file		,					self.merges_file		,					**self.special_tokens_map   )
										lowercase :							Tuple    		='''lower newer'''
										lowercase :							List[str]    		=['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowercase :							Tuple    		=tokenizer.tokenize(UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										lowercase :							Tuple    		=tokens + [tokenizer.unk_token]
										lowercase :							Any    		=[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										if not self.test_rust_tokenizer:
																return

										lowercase :							Optional[int]    		=self.get_tokenizer()
										lowercase :							List[Any]    		=self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		='''lower newer'''

										# Testing tokenization
										lowercase :							Any    		=tokenizer.tokenize(UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=rust_tokenizer.tokenize(UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										# Testing conversion to ids without special tokens
										lowercase :							Optional[Any]    		=tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										lowercase :							List[str]    		=rust_tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										# Testing conversion to ids with special tokens
										lowercase :							int    		=self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=tokenizer.encode(UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										lowercase :							Any    		=rust_tokenizer.encode(UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										# Testing the unknown token
										lowercase :							Any    		=tokens + [rust_tokenizer.unk_token]
										lowercase :							int    		=[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					*UpperCAmelCase__     :		List[str]		,					**UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										# It's very difficult to mix/test pretokenization with byte-level
										# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
										pass







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		List[Any]=15   ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
																						lowercase :							Optional[int]    		=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,					**UpperCAmelCase__   )

																						# Simple input
																						lowercase :							Union[str, Any]    		='''This is a simple input'''
																						lowercase :							Dict    		=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase :							Dict    		=('''This is a simple input''', '''This is a pair''')
																						lowercase :							Optional[Any]    		=[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,					tokenizer_r.batch_encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''		,					)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,					tokenizer_r.batch_encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''		,					)







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							List[str]    		=GPTaTokenizer.from_pretrained(self.tmpdirname		,					pad_token='''<pad>'''   )

										# Simple input
										lowercase :							Dict    		='''This is a simple input'''
										lowercase :							Optional[int]    		=['''This is a simple input looooooooong''', '''This is a simple input''']
										lowercase :							int    		=('''This is a simple input''', '''This is a pair''')
										lowercase :							Optional[int]    		=[
										    ('''This is a simple input loooooong''', '''This is a simple input'''),
										    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
										]

										lowercase :							str    		=tokenizer.pad_token_id

										lowercase :							int    		=tokenizer(UpperCAmelCase__		,					padding='''max_length'''		,					max_length=30		,					return_tensors='''np'''   )
										lowercase :							List[Any]    		=tokenizer(UpperCAmelCase__		,					padding=UpperCAmelCase__		,					truncate=UpperCAmelCase__		,					return_tensors='''np'''   )
										lowercase :							Optional[int]    		=tokenizer(*UpperCAmelCase__		,					padding='''max_length'''		,					max_length=60		,					return_tensors='''np'''   )
										lowercase :							Union[str, Any]    		=tokenizer(UpperCAmelCase__		,					padding=UpperCAmelCase__		,					truncate=UpperCAmelCase__		,					return_tensors='''np'''   )

										# s
										# test single string max_length padding
										self.assertEqual(out_s['''input_ids'''].shape[-1]		,					30   )
										self.assertTrue(pad_token_id in out_s['''input_ids''']   )
										self.assertTrue(0 in out_s['''attention_mask''']   )

										# s2
										# test automatic padding
										self.assertEqual(out_sa['''input_ids'''].shape[-1]		,					33   )
										# long slice doesn't have padding
										self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]   )
										self.assertFalse(0 in out_sa['''attention_mask'''][0]   )
										# short slice does have padding
										self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]   )
										self.assertTrue(0 in out_sa['''attention_mask'''][1]   )

										# p
										# test single pair max_length padding
										self.assertEqual(out_p['''input_ids'''].shape[-1]		,					60   )
										self.assertTrue(pad_token_id in out_p['''input_ids''']   )
										self.assertTrue(0 in out_p['''attention_mask''']   )

										# p2
										# test automatic padding pair
										self.assertEqual(out_pa['''input_ids'''].shape[-1]		,					52   )
										# long slice pair doesn't have padding
										self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]   )
										self.assertFalse(0 in out_pa['''attention_mask'''][0]   )
										# short slice pair does have padding
										self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]   )
										self.assertTrue(0 in out_pa['''attention_mask'''][1]   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		='''$$$'''
										lowercase :							List[Any]    		=GPTaTokenizer.from_pretrained(self.tmpdirname		,					bos_token=UpperCAmelCase__		,					add_bos_token=UpperCAmelCase__   )

										lowercase :							List[Any]    		='''This is a simple input'''
										lowercase :							Optional[Any]    		=['''This is a simple input 1''', '''This is a simple input 2''']

										lowercase :							Union[str, Any]    		=tokenizer.bos_token_id

										lowercase :							Optional[Any]    		=tokenizer(UpperCAmelCase__   )
										lowercase :							List[str]    		=tokenizer(UpperCAmelCase__   )

										self.assertEqual(out_s.input_ids[0]		,					UpperCAmelCase__   )
										self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids   )   )

										lowercase :							str    		=tokenizer.decode(out_s.input_ids   )
										lowercase :							List[str]    		=tokenizer.batch_decode(out_sa.input_ids   )

										self.assertEqual(decode_s.split()[0]		,					UpperCAmelCase__   )
										self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa   )   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										# TODO: change to self.get_tokenizers() when the fast version is implemented
										lowercase :							List[str]    		=[self.get_tokenizer(do_lower_case=UpperCAmelCase__		,					add_bos_token=UpperCAmelCase__   )]
										for tokenizer in tokenizers:
																with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
																						lowercase :							int    		='''Encode this.'''
																						lowercase :							List[str]    		='''This one too please.'''
																						lowercase :							Dict    		=tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
																						encoded_sequence += tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
																						lowercase :							Tuple    		=tokenizer.encode_plus(
																						    UpperCAmelCase__		,					UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__		,					return_special_tokens_mask=UpperCAmelCase__		,					)
																						lowercase :							Dict    		=encoded_sequence_dict['''input_ids''']
																						lowercase :							str    		=encoded_sequence_dict['''special_tokens_mask''']
																						self.assertEqual(len(UpperCAmelCase__   )		,					len(UpperCAmelCase__   )   )

																						lowercase :							str    		=[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(UpperCAmelCase__   )
																						]
																						lowercase :							Any    		=[x for x in filtered_sequence if x is not None]
																						self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# More context:
										# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
										# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
										# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

										lowercase :							List[Any]    		=AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,					from_slow=UpperCAmelCase__   )
										lowercase :							str    		='''A photo of a cat'''

										lowercase :							Optional[int]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										self.assertEqual(UpperCAmelCase__		,					[2, 250, 1345, 9, 10, 4758]   )
										tokenizer.save_pretrained('''test_opt'''   )

										lowercase :							int    		=AutoTokenizer.from_pretrained('''./test_opt'''   )
										lowercase :							Optional[Any]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										self.assertEqual(UpperCAmelCase__		,					[2, 250, 1345, 9, 10, 4758]   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							int    		=AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,					use_slow=UpperCAmelCase__   )
										lowercase :							Any    		='''A photo of a cat'''

										lowercase :							List[str]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										# Same as above
										self.assertEqual(UpperCAmelCase__		,					[2, 250, 1345, 9, 10, 4758]   )







				@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''   )
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,					from_slow=UpperCAmelCase__   )

										lowercase :							List[str]    		='''bos'''
										lowercase :							List[Any]    		=tokenizer.get_vocab()['''bos''']

										lowercase :							Optional[int]    		='''A photo of a cat'''
										lowercase :							Tuple    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										# We changed the bos token
										self.assertEqual(UpperCAmelCase__		,					[31957, 250, 1345, 9, 10, 4758]   )
										tokenizer.save_pretrained('''./tok'''   )
										lowercase :							Tuple    		=AutoTokenizer.from_pretrained('''./tok'''   )
										self.assertTrue(tokenizer.is_fast   )
										lowercase :							Optional[int]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										self.assertEqual(UpperCAmelCase__		,					[31957, 250, 1345, 9, 10, 4758]   )








'''simple docstring'''





import math

import flax.linen as nn
import jax.numpy as jnp






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 1.0   , )				->  jnp.ndarray:
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

						if flip_sin_to_cos:
												lowercase :							Dict    		=jnp.concatenate([jnp.cos(__magic_name__						), jnp.sin(__magic_name__						)]   , axis=1						)
						else:
												lowercase :							Any    		=jnp.concatenate([jnp.sin(__magic_name__						), jnp.cos(__magic_name__						)]   , axis=1						)
						lowercase :							List[str]    		=jnp.reshape(__magic_name__   , [jnp.shape(__magic_name__						)[0], embedding_dim]						)
						return signal





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return get_sinusoidal_embeddings(
										    UpperCAmelCase__		,					embedding_dim=self.dim		,					flip_sin_to_cos=self.flip_sin_to_cos		,					freq_shift=self.freq_shift   )





