import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_keras_nlp_available():
							from transformers.models.gpta import TFGPTaTokenizer


_lowerCamelCase   :				Dict     			=						['''gpt2''']
_lowerCamelCase   :				str     			=						'''gpt2'''

if is_tf_available():


							class   lowercase      (       tf.Module       ):




														def __init__(  self				:   Tuple		,							_UpperCamelCase				:   str )   ->			int:



																			'''simple docstring'''

																			super().__init__()
																			SCREAMING_SNAKE_CASE           =    tokenizer
																			SCREAMING_SNAKE_CASE           =    AutoConfig.from_pretrained(_UpperCamelCase )
																			SCREAMING_SNAKE_CASE           =    TFGPTaLMHeadModel.from_config(_UpperCamelCase )





														@tf.function(input_signature=(tf.TensorSpec((None,)		,							tf.string		,							name="text" ),) )
														def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Dict )   ->			List[str]:



																			'''simple docstring'''

																			SCREAMING_SNAKE_CASE           =    self.tokenizer(_UpperCamelCase )
																			SCREAMING_SNAKE_CASE           =    tokenized["input_ids"].to_tensor()

																			SCREAMING_SNAKE_CASE           =    tf.cast(input_ids_dense > 0		,							tf.intaa )
																			# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																			SCREAMING_SNAKE_CASE           =    self.model(input_ids=_UpperCamelCase		,							attention_mask=_UpperCamelCase )["logits"]

																			return outputs


@require_tf
@require_keras_nlp
class   lowercase      (       unittest.TestCase       ):




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



												'''simple docstring'''

												super().setUp()

												SCREAMING_SNAKE_CASE           =    [GPTaTokenizer.from_pretrained(_UpperCamelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												SCREAMING_SNAKE_CASE           =    [TFGPTaTokenizer.from_pretrained(_UpperCamelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers ) == len(self.tf_tokenizers )

												SCREAMING_SNAKE_CASE           =    [
												    "This is a straightforward English test sentence.",
												    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
												    "Now we're going to add some Chinese: 一 二 三 一二三",
												    "And some much more rare Chinese: 齉 堃 齉堃",
												    "Je vais aussi écrire en français pour tester les accents",
												    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
												]
												SCREAMING_SNAKE_CASE           =    list(zip(self.test_sentences		,							self.test_sentences[::-1] ) )




							def 		__snake_case(  self				:   str )   ->			int:



												'''simple docstring'''

												for tokenizer, tf_tokenizer in zip(self.tokenizers		,							self.tf_tokenizers ):
																	for test_inputs in self.test_sentences:
																						SCREAMING_SNAKE_CASE           =    tokenizer([test_inputs]		,							return_tensors="tf" )
																						SCREAMING_SNAKE_CASE           =    tf_tokenizer([test_inputs] )

																						for key in python_outputs.keys():
																											# convert them to numpy to avoid messing with ragged tensors
																											SCREAMING_SNAKE_CASE           =    python_outputs[key].numpy()
																											SCREAMING_SNAKE_CASE           =    tf_outputs[key].numpy()

																											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
																											self.assertTrue(tf.reduce_all(tf.cast(_UpperCamelCase		,							tf.intaa ) == tf_outputs_values ) )




							@slow
							def 		__snake_case(  self				:   Dict )   ->			Tuple:



												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	SCREAMING_SNAKE_CASE           =    tf.function(_UpperCamelCase )
																	for test_inputs in self.test_sentences:
																						SCREAMING_SNAKE_CASE           =    tf.constant(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    compiled_tokenizer(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    tf_tokenizer(_UpperCamelCase )

																						for key in eager_outputs.keys():
																											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )




							@slow
							def 		__snake_case(  self				:   List[str] )   ->			Optional[Any]:



												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	SCREAMING_SNAKE_CASE           =    ModelToSave(tokenizer=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor([self.test_sentences[0]] )
																	SCREAMING_SNAKE_CASE           =    model.serving(_UpperCamelCase )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																						SCREAMING_SNAKE_CASE           =    Path(_UpperCamelCase ) / "saved.model"
																						tf.saved_model.save(_UpperCamelCase		,							_UpperCamelCase		,							signatures={"serving_default": model.serving} )
																						SCREAMING_SNAKE_CASE           =    tf.saved_model.load(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    loaded_model.signatures["serving_default"](_UpperCamelCase )["output_0"]
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertTrue(tf.reduce_all(out == loaded_output ) )




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



												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor([self.test_sentences[0]] )
																	SCREAMING_SNAKE_CASE           =    tf_tokenizer(_UpperCamelCase )  # Build model with some sample inputs

																	SCREAMING_SNAKE_CASE           =    tf_tokenizer.get_config()
																	SCREAMING_SNAKE_CASE           =    TFGPTaTokenizer.from_config(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model_from_config(_UpperCamelCase )

																	for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )





							@slow
							def 		__snake_case(  self				:   int )   ->			List[Any]:



												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	SCREAMING_SNAKE_CASE           =    123_123

																	for max_length in [3, 5, 1_024]:
																						SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor([self.test_sentences[0]] )
																						SCREAMING_SNAKE_CASE           =    tf_tokenizer(_UpperCamelCase		,							max_length=_UpperCamelCase )

																						SCREAMING_SNAKE_CASE           =    out["input_ids"].numpy().shape[1]

																						assert out_length == max_length





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Dict     			=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				List[Any]     			=						[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

_lowerCamelCase   :				Optional[Any]     			=						{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
_lowerCamelCase   :				Any     			=						{f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowerCamelCase   :				Optional[Any]     			=						{f"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							VOCAB_FILES_NAMES
							lowercase__  :    Optional[Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Union[str, Any]									=							FunnelTokenizer
							lowercase__  :    List[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    int									=							2




							def __init__(  self				:   str		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   str="<unk>"		,							_UpperCamelCase				:   Optional[Any]="<sep>"		,							_UpperCamelCase				:   Optional[int]="<pad>"		,							_UpperCamelCase				:   int="<cls>"		,							_UpperCamelCase				:   Dict="<mask>"		,							_UpperCamelCase				:   Union[str, Any]="<s>"		,							_UpperCamelCase				:   Optional[int]="</s>"		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Dict="##"		,							**_UpperCamelCase				:   Dict		,							)   ->			Optional[int]:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							do_lower_case=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							clean_text=_UpperCamelCase		,							tokenize_chinese_chars=_UpperCamelCase		,							strip_accents=_UpperCamelCase		,							wordpieces_prefix=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
												if (
												    normalizer_state.get("lowercase"		,							_UpperCamelCase ) != do_lower_case
												    or normalizer_state.get("strip_accents"		,							_UpperCamelCase ) != strip_accents
												    or normalizer_state.get("handle_chinese_chars"		,							_UpperCamelCase ) != tokenize_chinese_chars
												):
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							normalizer_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    do_lower_case
																	SCREAMING_SNAKE_CASE           =    strip_accents
																	SCREAMING_SNAKE_CASE           =    tokenize_chinese_chars
																	SCREAMING_SNAKE_CASE           =    normalizer_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_lower_case




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Dict=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												if token_ids_a is None:
																	return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
												return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




