



import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


_lowercase						  =		logging.get_logger(__name__)


_lowercase						  =		R"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"



class     _UpperCAmelCase			(       A__     ):




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__ ,						**a__):
         raise NotImplementedError('''StoppingCriteria needs to be subclassed''')







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ = None):
         A__		 =					max_length
         A__		 =					max_position_embeddings




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__ ,						**a__):
         A__		 =					input_ids.shape[-1]
         A__		 =					cur_len >= self.max_length
         if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
             logger.warning_once(
                 '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
                 F"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
                 '''exceptions, performance degradation, or nothing at all.''')
         return is_done







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         warnings.warn(
             '''The class `MaxNewTokensCriteria` is deprecated. '''
             F"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
             '''with `max_length = start_length + max_new_tokens` instead.''' ,						a__ ,						)
         A__		 =					start_length
         A__		 =					max_new_tokens
         A__		 =					start_length + max_new_tokens




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__ ,						**a__):
         return input_ids.shape[-1] >= self.max_length







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ = None):
         A__		 =					max_time
         A__		 =					time.time() if initial_timestamp is None else initial_timestamp




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__ ,						**a__):
         return time.time() - self.initial_timestamp > self.max_time







class     _UpperCAmelCase			(       A__     ):




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__ ,						**a__):
         return any(criteria(a__ ,						a__) for criteria in self)




     @property
     def 		snake_case_		(				self):
         for stopping_criterium in self:
             if isinstance(a__ ,						a__):
                 return stopping_criterium.max_length
             elif isinstance(a__ ,						a__):
                 return stopping_criterium.max_length
         return None







def 		lowerCAmelCase__    (					UpperCamelCase_				:       StoppingCriteriaList ,       UpperCamelCase_				:       int	)->  StoppingCriteriaList:
    A__		 =					stopping_criteria.max_length
    A__		 =					deepcopy(UpperCamelCase_	)
    if stopping_max_length is not None and stopping_max_length != max_length:
        warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter''' ,       UpperCamelCase_	)
    elif stopping_max_length is None:
        new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCamelCase_	)	)
    return new_stopping_criteria





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       FlaubertConfig,
       TFFlaubertForMultipleChoice,
       TFFlaubertForQuestionAnsweringSimple,
       TFFlaubertForSequenceClassification,
       TFFlaubertForTokenClassification,
       TFFlaubertModel,
       TFFlaubertWithLMHeadModel,
   )



class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						):
         A__		 =					parent
         A__		 =					1_3
         A__		 =					7
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					False
         A__		 =					False
         A__		 =					False
         A__		 =					2
         A__		 =					9_9
         A__		 =					0
         A__		 =					3_2
         A__		 =					2
         A__		 =					4
         A__		 =					0.1
         A__		 =					0.1
         A__		 =					5_1_2
         A__		 =					1_6
         A__		 =					2
         A__		 =					0.0_2
         A__		 =					3
         A__		 =					4
         A__		 =					'''last'''
         A__		 =					True
         A__		 =					None
         A__		 =					0




     def 		snake_case_		(				self):
         A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)
         A__		 =					random_attention_mask([self.batch_size, self.seq_length] ,						dtype=tf.floataa)

         A__		 =					None
         if self.use_input_lengths:
             A__		 =					(
                 ids_tensor([self.batch_size] ,						vocab_size=2) + self.seq_length - 2
             )  # small variation of seq_length

         A__		 =					None
         if self.use_token_type_ids:
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.n_langs)

         A__		 =					None
         A__		 =					None
         A__		 =					None
         if self.use_labels:
             A__		 =					ids_tensor([self.batch_size] ,						self.type_sequence_label_size)
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels)
             A__		 =					ids_tensor([self.batch_size] ,						2 ,						dtype=tf.floataa)
             A__		 =					ids_tensor([self.batch_size] ,						self.num_choices)

         A__		 =					FlaubertConfig(
             vocab_size=self.vocab_size ,						n_special=self.n_special ,						emb_dim=self.hidden_size ,						n_layers=self.num_hidden_layers ,						n_heads=self.num_attention_heads ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						gelu_activation=self.gelu_activation ,						sinusoidal_embeddings=self.sinusoidal_embeddings ,						asm=self.asm ,						causal=self.causal ,						n_langs=self.n_langs ,						max_position_embeddings=self.max_position_embeddings ,						initializer_range=self.initializer_range ,						summary_type=self.summary_type ,						use_proj=self.use_proj ,						bos_token_id=self.bos_token_id ,						)

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertModel(config=a__)
         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         A__		 =					model(a__)

         A__		 =					[input_ids, input_mask]
         A__		 =					model(a__)
         self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertWithLMHeadModel(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         A__		 =					model(a__)

         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertForQuestionAnsweringSimple(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths}

         A__		 =					model(a__)

         self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertForSequenceClassification(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths}

         A__		 =					model(a__)

         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					self.num_labels
         A__		 =					TFFlaubertForTokenClassification(config=a__)
         A__		 =					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         A__		 =					model(a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					self.num_choices
         A__		 =					TFFlaubertForMultipleChoice(config=a__)
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					{
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         A__		 =					model(a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices))




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         (
             (
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	
         )		 =					config_and_inputs
         A__		 =					{
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''langs''': token_type_ids,
             '''lengths''': input_lengths,
         }
         return config, inputs_dict







@require_tf
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		    =				(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     UpperCamelCase__		    =				(
         {
             '''feature-extraction''': TFFlaubertModel,
             '''fill-mask''': TFFlaubertWithLMHeadModel,
             '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
             '''text-classification''': TFFlaubertForSequenceClassification,
             '''token-classification''': TFFlaubertForTokenClassification,
             '''zero-shot''': TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__):
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith('''Fast''')
         ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

         return False




     def 		snake_case_		(				self):
         A__		 =					TFFlaubertModelTester(self)
         A__		 =					ConfigTester(self ,						config_class=a__ ,						emb_dim=3_7)




     def 		snake_case_		(				self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_token_classification(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_multiple_choice(*a__)




     @slow
     def 		snake_case_		(				self):
         for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__		 =					TFFlaubertModel.from_pretrained(a__)
             self.assertIsNotNone(a__)







@require_tf
@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase			(       unittest.TestCase     ):




     @slow
     def 		snake_case_		(				self):
         A__		 =					TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')

         A__		 =					tf.convert_to_tensor(
             [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]] ,						dtype=tf.intaa ,						)  # "J'aime flaubert !"

         A__		 =					model(a__)[0]
         A__		 =					tf.TensorShape((1, 8, 5_1_2))
         self.assertEqual(output.shape ,						a__)
         # compare the actual values for a slice.
         A__		 =					tf.convert_to_tensor(
             [
                 [
                     [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                     [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                     [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
                 ]
             ] ,						dtype=tf.floataa ,						)

         self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1e-4))
