

from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    AutoTokenizer,
			    TFAutoModelForCausalLM,
			    TFAutoModelForSeqaSeqLM,
			    TFAutoModelForSpeechSeqaSeq,
			    TFAutoModelForVisionaSeq,
			    TFBartForConditionalGeneration,
			    TFLogitsProcessorList,
			    TFMinLengthLogitsProcessor,
			    tf_top_k_top_p_filtering,
			)

if is_tensorflow_text_available():
			import tensorflow_text as text






@require_tf
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							tf.convert_to_tensor(
				    [
				        [
				            8.222_0991,  # 3rd highest value; idx. 0
				            -0.562_0044,
				            5.2322_9752,
				            4.038_6393,
				            -6.879_8378,
				            -0.5478_5802,
				            -3.201_2153,
				            2.9277_7176,
				            1.8817_1953,
				            7.3534_1276,  # 5th highest value; idx. 9
				            8.4320_7833,  # 2nd highest value; idx. 10
				            -9.8571_1836,
				            -5.9620_9236,
				            -1.1303_9161,
				            -7.111_5294,
				            -0.836_9633,
				            -5.318_6408,
				            7.0642_7407,
				            0.8136_9344,
				            -0.8202_3817,
				            -5.917_9796,
				            0.5881_3443,
				            -6.9977_8438,
				            4.7155_1189,
				            -0.1877_1637,
				            7.4402_0759,  # 4th highest value; idx. 25
				            9.3845_0987,  # 1st highest value; idx. 26
				            2.1266_2941,
				            -9.3256_2038,
				            2.3565_2522,
				        ],  # cummulative prob of 5 highest values <= 0.6
				        [
				            0.5842_5518,
				            4.5313_9238,
				            -5.5751_0464,
				            -6.2803_0699,
				            -7.1952_9503,
				            -4.0212_2551,
				            1.3933_7037,
				            -6.0670_7057,
				            1.5948_0517,
				            -9.64_3119,
				            0.0390_7799,
				            0.6723_1762,
				            -8.8820_6726,
				            6.2711_5922,  # 4th highest value; idx. 13
				            2.2852_0723,
				            4.8276_7506,
				            4.3042_1368,
				            8.827_5313,  # 2nd highest value; idx. 17
				            5.4402_9958,  # 5th highest value; idx. 18
				            -4.473_5794,
				            7.3857_9536,  # 3rd highest value; idx. 20
				            -2.9105_1663,
				            2.6194_6077,
				            -2.567_4762,
				            -9.4895_9302,
				            -4.0292_2645,
				            -1.3541_6918,
				            9.6770_2323,  # 1st highest value; idx. 27
				            -5.8947_8553,
				            1.8537_0467,
				        ],  # cummulative prob of 5 highest values <= 0.6
				    ]   ,   dtype=tf.floataa   ,   )

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							tf.convert_to_tensor(
				    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]   ,   dtype=tf.intaa   ,   )  # expected non filtered idx as noted above

				SCREAMING_SNAKE_CASE :   List[Any]					  =							tf.convert_to_tensor(
				    [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023]   ,   dtype=tf.floataa   ,   )  # expected non filtered values as noted above

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							tf_top_k_top_p_filtering(a   ,   top_k=10   ,   top_p=0.6   ,   min_tokens_to_keep=4						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output[output != -float("inf"						)]
				SCREAMING_SNAKE_CASE :   Tuple					  =							tf.cast(
				    tf.where(tf.not_equal(a   ,   tf.constant(-float("inf"						)   ,   dtype=tf.floataa						)						)						)   ,   dtype=tf.intaa   ,   )

				tf.debugging.assert_near(a   ,   a   ,   rtol=1e-12						)
				tf.debugging.assert_equal(a   ,   a						)






@require_tf
class _UpperCamelCase				(				unittest.TestCase				,						__A ):
			'''simple docstring'''







			if is_tf_available():
						lowerCamelCase__							     ={
						    'AutoModelForCausalLM': TFAutoModelForCausalLM,
						    'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
						    'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
						    'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
						    'LogitsProcessorList': TFLogitsProcessorList,
						    'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
						    'create_tensor_fn': tf.convert_to_tensor,
						    'floats_tensor': floats_tensor,
						    'return_tensors': 'tf',
						}
			@slow
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
				SCREAMING_SNAKE_CASE :   str					  =							2
				SCREAMING_SNAKE_CASE :   List[str]					  =							2






				class _UpperCamelCase				(				tf.Module ):
							'''simple docstring'''







							def __init__(      self				:			List[str]   ,   a				:			Optional[int]						)				->							str:

								"""simple docstring"""

								super(a   ,   self						).__init__()
								SCREAMING_SNAKE_CASE :   Dict					  =							model






							@tf.function(
							    input_signature=(
							        tf.TensorSpec((None, input_length)   ,   tf.intaa   ,   name="input_ids"						),
							        tf.TensorSpec((None, input_length)   ,   tf.intaa   ,   name="attention_mask"						),
							    )   ,   jit_compile=a   ,   )
							def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			List[str]   ,   a				:			Any						)				->							Tuple:

								"""simple docstring"""

								SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model.generate(
								    input_ids=a   ,   attention_mask=a   ,   max_new_tokens=a   ,   return_dict_in_generate=a   ,   )
								return {"sequences": outputs["sequences"]}

				SCREAMING_SNAKE_CASE :   Dict					  =							[[2, 0], [102, 103]]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[[1, 0], [1, 1]]
				SCREAMING_SNAKE_CASE :   Any					  =							DummyModel(model=a						)
				with tempfile.TemporaryDirectory() as tmp_dir:
					tf.saved_model.save(a   ,   a   ,   signatures={"serving_default": dummy_model.serving}						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							tf.saved_model.load(a						).signatures["serving_default"]
					for batch_size in range(1   ,   len(a						) + 1						):
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							{
						    "input_ids": tf.constant(dummy_input_ids[:batch_size]						),
						    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]						),
						}
						SCREAMING_SNAKE_CASE :   Dict					  =							serving_func(**a						)["sequences"]
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							test_model.generate(**a   ,   max_new_tokens=a						)
						tf.debugging.assert_equal(a   ,   a						)
			@slow
			def 				__UpperCamelCase			(      self				:			Any						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
				SCREAMING_SNAKE_CASE :   int					  =							1
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							2






				class _UpperCamelCase				(				tf.Module ):
							'''simple docstring'''







							def __init__(      self				:			List[str]   ,   a				:			List[Any]						)				->							Optional[int]:

								"""simple docstring"""

								super(a   ,   self						).__init__()
								SCREAMING_SNAKE_CASE :   List[Any]					  =							model






							@tf.function(
							    input_signature=(
							        tf.TensorSpec((batch_size, None)   ,   tf.intaa   ,   name="input_ids"						),
							        tf.TensorSpec((batch_size, None)   ,   tf.intaa   ,   name="attention_mask"						),
							    )   ,   jit_compile=a   ,   )
							def 				__UpperCamelCase			(      self				:			str   ,   a				:			List[str]   ,   a				:			str						)				->							Any:

								"""simple docstring"""

								SCREAMING_SNAKE_CASE :   Any					  =							self.model.generate(
								    input_ids=a   ,   attention_mask=a   ,   max_new_tokens=a   ,   return_dict_in_generate=a   ,   )
								return {"sequences": outputs["sequences"]}

				SCREAMING_SNAKE_CASE :   str					  =							[[2], [102, 103]]
				SCREAMING_SNAKE_CASE :   str					  =							[[1], [1, 1]]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DummyModel(model=a						)
				with tempfile.TemporaryDirectory() as tmp_dir:
					tf.saved_model.save(a   ,   a   ,   signatures={"serving_default": dummy_model.serving}						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							tf.saved_model.load(a						).signatures["serving_default"]
					for input_row in range(len(a						)						):
						SCREAMING_SNAKE_CASE :   List[str]					  =							{
						    "input_ids": tf.constant([dummy_input_ids[input_row]]						),
						    "attention_mask": tf.constant([dummy_attention_masks[input_row]]						),
						}
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							serving_func(**a						)["sequences"]
						SCREAMING_SNAKE_CASE :   str					  =							test_model.generate(**a   ,   max_new_tokens=a						)
						tf.debugging.assert_equal(a   ,   a						)
			@slow
			@require_tensorflow_text
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				with tempfile.TemporaryDirectory() as tmp_dir:
					# file needed to load the TF tokenizer
					hf_hub_download(repo_id="google/flan-t5-small"   ,   filename="spiece.model"   ,   local_dir=a						)






					class _UpperCamelCase				(				tf.keras.layers.Layer ):
								'''simple docstring'''







								def __init__(      self				:			Tuple						)				->							Optional[Any]:

									"""simple docstring"""

									super().__init__()
									SCREAMING_SNAKE_CASE :   List[str]					  =							text.SentencepieceTokenizer(
									    model=tf.io.gfile.GFile(os.path.join(a   ,   "spiece.model"						)   ,   "rb"						).read()						)
									SCREAMING_SNAKE_CASE :   str					  =							TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5"						)






								def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			Optional[Any]   ,   *a				:			Union[str, Any]   ,   **a				:			int						)				->							List[str]:

									"""simple docstring"""

									SCREAMING_SNAKE_CASE :   List[Any]					  =							self.tokenizer.tokenize(a						)
									SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							text.pad_model_inputs(
									    a   ,   max_seq_length=64   ,   pad_value=self.model.config.pad_token_id						)
									SCREAMING_SNAKE_CASE :   str					  =							self.model.generate(input_ids=a   ,   attention_mask=a						)
									return self.tokenizer.detokenize(a						)

					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							CompleteSentenceTransformer()
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tf.keras.layers.Input(shape=(1,)   ,   dtype=tf.string   ,   name="inputs"						)
					SCREAMING_SNAKE_CASE :   Dict					  =							complete_model(a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							tf.keras.Model(a   ,   a						)
					keras_model.save(a						)
			def 				__UpperCamelCase			(      self				:			int						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							{
				    "do_sample": True,
				    "num_beams": 1,
				    "top_p": 0.7,
				    "top_k": 10,
				    "temperature": 0.7,
				}
				SCREAMING_SNAKE_CASE :   Dict					  =							14

				SCREAMING_SNAKE_CASE :   int					  =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
				SCREAMING_SNAKE_CASE :   int					  =							"Hello, my dog is cute and"
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tokenizer(a   ,   return_tensors="tf"						)
				SCREAMING_SNAKE_CASE :   str					  =							TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)

				SCREAMING_SNAKE_CASE :   Dict					  =							638
				# forces the generation to happen on CPU, to avoid GPU-related quirks
				with tf.device(":/CPU:0"						):
					tf.random.set_seed(0						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model.generate(**a   ,   eos_token_id=a   ,   **a						)
				self.assertTrue(expectation == len(generated_tokens[0]						)						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[638, 198]
				with tf.device(":/CPU:0"						):
					tf.random.set_seed(0						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model.generate(**a   ,   eos_token_id=a   ,   **a						)
				self.assertTrue(expectation == len(generated_tokens[0]						)						)






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart"						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"Hugging Face is a technology company based in New York and Paris."
				SCREAMING_SNAKE_CASE :   List[Any]					  =							bart_tokenizer(a   ,   return_tensors="tf"						).input_ids
				SCREAMING_SNAKE_CASE :   Dict					  =							TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"						)
				SCREAMING_SNAKE_CASE :   Dict					  =							bart_model.generate(a						).numpy()






				class _UpperCamelCase				(				__A ):
							'''simple docstring'''







							def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			int   ,   a				:			List[str]=None   ,   **a				:			List[str]						)				->							Optional[int]:

								"""simple docstring"""

								return super().call(a   ,   **a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							bart_model.generate(a   ,   foo="bar"						).numpy()
				self.assertTrue(np.array_equal(a   ,   a						)						)






				class _UpperCamelCase				(				bart_model.model.encoder.__class__ ):
							'''simple docstring'''







							def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Any   ,   **a				:			List[Any]						)				->							Optional[Any]:

								"""simple docstring"""

								return super().call(a   ,   **a						)

				SCREAMING_SNAKE_CASE :   int					  =							FakeEncoder(bart_model.config   ,   bart_model.model.shared						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							fake_encoder

				# Normal generation still works (the output will be different because the encoder weights are different)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							bart_model.generate(a						).numpy()
				with self.assertRaises(a						):
					# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
					bart_model.generate(a   ,   foo="bar"						)


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     None

SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE		:int						=     {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE		:List[str]						=     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE		:Optional[Any]						=     """▁"""






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:List[Any]										=      BarthezTokenizer






     def __init__(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase="<s>"		,						_lowercase="</s>"		,						_lowercase="</s>"		,						_lowercase="<s>"		,						_lowercase="<unk>"		,						_lowercase="<pad>"		,						_lowercase="<mask>"		,						**_lowercase		,						)->     Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_			  =						AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase	) if isinstance(_lowercase		,						_lowercase	) else mask_token

         super().__init__(
             _lowercase		,						tokenizer_file=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						**_lowercase		,						)

         UpperCamelCase_			  =						vocab_file
         UpperCamelCase_			  =						False if not self.vocab_file else True






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]
         UpperCamelCase_			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         UpperCamelCase_			  =						[self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."	)

         if not os.path.isdir(_lowercase	):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
             return
         UpperCamelCase_			  =						os.path.join(
             _lowercase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	):
             copyfile(self.vocab_file		,						_lowercase	)

         return (out_vocab_file,)
