


'''simple docstring'''







from abc import ABC, abstractmethod
from typing import List, Optional
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self ):

            '''simple docstring'''

            self.test()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							0
            _lowerCAmelCase     :				Optional[Any]       =							False
            while not completed:
                   if counter == 1:
                          self.reset()
                   _lowerCAmelCase     :				Optional[int]       =							self.advance()
                   if not self.does_advance(snake_case__ ):
                          raise Exception(
                              'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.' )

                   _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							self.update(snake_case__ )
                   counter += 1

                   if counter > 1_0000:
                          raise Exception('update() does not fulfill the constraint.' )

            if self.remaining() != 0:
                   raise Exception('Custom Constraint is not defined correctly.' )







     @abstractmethod
     def a						(			self ):

            '''simple docstring'''

            raise NotImplementedError(
                F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )







     @abstractmethod
     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            raise NotImplementedError(
                F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )







     @abstractmethod
     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            raise NotImplementedError(
                F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )







     @abstractmethod
     def a						(			self ):

            '''simple docstring'''

            raise NotImplementedError(
                F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )







     @abstractmethod
     def a						(			self ):

            '''simple docstring'''

            raise NotImplementedError(
                F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )







     @abstractmethod
     def a						(			self   ,						snake_case__=False ):

            '''simple docstring'''

            raise NotImplementedError(
                F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            super(snake_case__   ,						self ).__init__()

            if not isinstance(snake_case__   ,						snake_case__ ) or len(snake_case__ ) == 0:
                   raise ValueError(F'`token_ids` has to be a non-empty list, but is {token_ids}.' )
            if any((not isinstance(snake_case__   ,						snake_case__ ) or token_id < 0) for token_id in token_ids ):
                   raise ValueError(F'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )

            _lowerCAmelCase     :				Any       =							token_ids

            _lowerCAmelCase     :				int       =							len(self.token_ids )
            _lowerCAmelCase     :				Any       =							-1  # the index of the currently fulfilled step
            _lowerCAmelCase     :				List[str]       =							False







     def a						(			self ):

            '''simple docstring'''

            if self.completed:
                   return None
            return self.token_ids[self.fulfilled_idx + 1]







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if not isinstance(snake_case__   ,						snake_case__ ):
                   raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case__ )}' )

            if self.completed:
                   return False

            return token_id == self.token_ids[self.fulfilled_idx + 1]







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if not isinstance(snake_case__   ,						snake_case__ ):
                   raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case__ )}' )

            _lowerCAmelCase     :				int       =							False
            _lowerCAmelCase     :				Tuple       =							False
            _lowerCAmelCase     :				Tuple       =							False

            if self.does_advance(snake_case__ ):
                   self.fulfilled_idx += 1
                   _lowerCAmelCase     :				int       =							True
                   if self.fulfilled_idx == (self.seqlen - 1):
                          _lowerCAmelCase     :				Optional[Any]       =							True
                   _lowerCAmelCase     :				List[str]       =							completed
            else:
                   # failed to make progress.
                   _lowerCAmelCase     :				Optional[int]       =							True
                   self.reset()
            return stepped, completed, reset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							False
            _lowerCAmelCase     :				Dict       =							0







     def a						(			self ):

            '''simple docstring'''

            return self.seqlen - (self.fulfilled_idx + 1)







     def a						(			self   ,						snake_case__=False ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							PhrasalConstraint(self.token_ids )

            if stateful:
                   _lowerCAmelCase     :				List[Any]       =							self.seqlen
                   _lowerCAmelCase     :				Optional[int]       =							self.fulfilled_idx
                   _lowerCAmelCase     :				Optional[int]       =							self.completed

            return new_constraint
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							max([len(snake_case__ ) for one in nested_token_ids] )

            _lowerCAmelCase     :				Any       =							{}
            for token_ids in nested_token_ids:
                   _lowerCAmelCase     :				List[str]       =							root
                   for tidx, token_id in enumerate(snake_case__ ):
                          if token_id not in level:
                                 _lowerCAmelCase     :				int       =							{}

                          _lowerCAmelCase     :				Dict       =							level[token_id]

            if no_subsets and self.has_subsets(snake_case__   ,						snake_case__ ):
                   raise ValueError(
                       'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
                       F' {nested_token_ids}.' )

            _lowerCAmelCase     :				List[str]       =							root







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.trie

            for current_token in current_seq:
                   _lowerCAmelCase     :				Dict       =							start[current_token]

            _lowerCAmelCase     :				Optional[Any]       =							list(start.keys() )

            return next_tokens







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.next_tokens(snake_case__ )

            return len(snake_case__ ) == 0







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							list(root.values() )
            if len(snake_case__ ) == 0:
                   return 1
            else:
                   return sum([self.count_leaves(snake_case__ ) for nn in next_nodes] )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.count_leaves(snake_case__ )
            return len(snake_case__ ) != leaf_count
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            super(snake_case__   ,						self ).__init__()

            if not isinstance(snake_case__   ,						snake_case__ ) or len(snake_case__ ) == 0:
                   raise ValueError(F'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
            if any(not isinstance(snake_case__   ,						snake_case__ ) for token_ids in nested_token_ids ):
                   raise ValueError(F'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
            if any(
                any((not isinstance(snake_case__   ,						snake_case__ ) or token_id < 0) for token_id in token_ids )
                for token_ids in nested_token_ids ):
                   raise ValueError(
                       F'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )

            _lowerCAmelCase     :				Dict       =							DisjunctiveTrie(snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							nested_token_ids

            _lowerCAmelCase     :				Optional[int]       =							self.trie.max_height
            _lowerCAmelCase     :				List[Any]       =							[]
            _lowerCAmelCase     :				Tuple       =							False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.trie.next_tokens(self.current_seq )

            if len(snake_case__ ) == 0:
                   return None
            else:
                   return token_list







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if not isinstance(snake_case__   ,						snake_case__ ):
                   raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case__ )}' )

            _lowerCAmelCase     :				str       =							self.trie.next_tokens(self.current_seq )

            return token_id in next_tokens







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if not isinstance(snake_case__   ,						snake_case__ ):
                   raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case__ )}' )

            _lowerCAmelCase     :				str       =							False
            _lowerCAmelCase     :				int       =							False
            _lowerCAmelCase     :				Tuple       =							False

            if self.does_advance(snake_case__ ):
                   self.current_seq.append(snake_case__ )
                   _lowerCAmelCase     :				Tuple       =							True
            else:
                   _lowerCAmelCase     :				str       =							True
                   self.reset()

            _lowerCAmelCase     :				str       =							self.trie.reached_leaf(self.current_seq )
            _lowerCAmelCase     :				List[str]       =							completed

            return stepped, completed, reset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							False
            _lowerCAmelCase     :				List[str]       =							[]







     def a						(			self ):

            '''simple docstring'''

            if self.completed:
                   # since this can be completed without reaching max height
                   return 0
            else:
                   return self.seqlen - len(self.current_seq )







     def a						(			self   ,						snake_case__=False ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							DisjunctiveConstraint(self.token_ids )

            if stateful:
                   _lowerCAmelCase     :				Any       =							self.seqlen
                   _lowerCAmelCase     :				List[str]       =							self.current_seq
                   _lowerCAmelCase     :				Optional[Any]       =							self.completed

            return new_constraint
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							constraints

            # max # of steps required to fulfill a given constraint
            _lowerCAmelCase     :				str       =							max([c.seqlen for c in constraints] )
            _lowerCAmelCase     :				Tuple       =							len(snake_case__ )
            _lowerCAmelCase     :				str       =							False

            self.init_state()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							[]
            _lowerCAmelCase     :				int       =							None
            _lowerCAmelCase     :				Any       =							[constraint.copy(stateful=snake_case__ ) for constraint in self.constraints]







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							0
            if self.inprogress_constraint:
                   # extra points for having a constraint mid-fulfilled
                   add += self.max_seqlen - self.inprogress_constraint.remaining()

            return (len(self.complete_constraints ) * self.max_seqlen) + add







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							[]
            if self.inprogress_constraint is None:
                   for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                          _lowerCAmelCase     :				List[Any]       =							constraint.advance()
                          if isinstance(snake_case__   ,						snake_case__ ):
                                 token_list.append(snake_case__ )
                          elif isinstance(snake_case__   ,						snake_case__ ):
                                 token_list.extend(snake_case__ )
            else:
                   _lowerCAmelCase     :				Union[str, Any]       =							self.inprogress_constraint.advance()
                   if isinstance(snake_case__   ,						snake_case__ ):
                          token_list.append(snake_case__ )
                   elif isinstance(snake_case__   ,						snake_case__ ):
                          token_list.extend(snake_case__ )

            if len(snake_case__ ) == 0:
                   return None
            else:
                   return token_list







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            self.init_state()

            if token_ids is not None:
                   for token in token_ids:
                          # completes or steps **one** constraint
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							self.add(snake_case__ )

                          # the entire list of constraints are fulfilled
                          if self.completed:
                                 break







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if not isinstance(snake_case__   ,						snake_case__ ):
                   raise ValueError(F'`token_id` should be an `int`, but is `{token_id}`.' )

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							False, False

            if self.completed:
                   _lowerCAmelCase     :				str       =							True
                   _lowerCAmelCase     :				Tuple       =							False
                   return complete, stepped

            if self.inprogress_constraint is not None:
                   # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
                   # job, simply update the state

                   _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							self.inprogress_constraint.update(snake_case__ )
                   if reset:
                          # 1. If the next token breaks the progress, then we must restart.
                          #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                          #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                          #     constraint, not the full list of constraints.

                          self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case__ ) )
                          _lowerCAmelCase     :				List[str]       =							None

                   if complete:
                          # 2. If the next token completes the constraint, move it to completed list, set
                          #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                          #     is complete.

                          self.complete_constraints.append(self.inprogress_constraint )
                          _lowerCAmelCase     :				Dict       =							None

                          if len(self.pending_constraints ) == 0:
                                 # we're done!
                                 _lowerCAmelCase     :				List[Any]       =							True

            else:
                   # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
                   # of constraints?

                   for cidx, pending_constraint in enumerate(self.pending_constraints ):
                          if pending_constraint.does_advance(snake_case__ ):
                                 _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							pending_constraint.update(snake_case__ )

                                 if not stepped:
                                        raise Exception(
                                            '`constraint.update(token_id)` is not yielding incremental progress, '
                                            'even though `constraint.does_advance(token_id)` is true.' )

                                 if complete:
                                        self.complete_constraints.append(snake_case__ )
                                        _lowerCAmelCase     :				str       =							None

                                 if not complete and stepped:
                                        _lowerCAmelCase     :				Optional[int]       =							pending_constraint

                                 if complete or stepped:
                                        # If we made any progress at all, then it's at least not a "pending constraint".

                                        _lowerCAmelCase     :				Tuple       =							(
                                            self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                                        )

                                        if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
                                               # If there's no longer any pending after this and no inprogress either, then we must be
                                               # complete.

                                               _lowerCAmelCase     :				Dict       =							True

                                        break  # prevent accidentally stepping through multiple constraints with just one token.

            return complete, stepped







     def a						(			self   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							ConstraintListState(self.constraints )  # we actually never though self.constraints objects
            # throughout this process. So it's at initialization state.

            if stateful:
                   _lowerCAmelCase     :				Optional[int]       =							[
                       constraint.copy(stateful=snake_case__ ) for constraint in self.complete_constraints
                   ]
                   if self.inprogress_constraint is not None:
                          _lowerCAmelCase     :				int       =							self.inprogress_constraint.copy(stateful=snake_case__ )
                   _lowerCAmelCase     :				str       =							[constraint.copy() for constraint in self.pending_constraints]

            return new_state





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
       import tensorflow as tf

       from transformers.models.xglm.modeling_tf_xglm import (
           TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFXGLMForCausalLM,
           TFXGLMModel,
       )
@require_tf
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     XGLMConfig
     __magic_name__           =     {}
     __magic_name__           =     "gelu"







     def __init__(			self   ,						snake_case__   ,						snake_case__=14   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=2   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=0.02   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							parent
            _lowerCAmelCase     :				Tuple       =							batch_size
            _lowerCAmelCase     :				Optional[Any]       =							seq_length
            _lowerCAmelCase     :				Dict       =							is_training
            _lowerCAmelCase     :				str       =							use_input_mask
            _lowerCAmelCase     :				Tuple       =							use_labels
            _lowerCAmelCase     :				Union[str, Any]       =							vocab_size
            _lowerCAmelCase     :				Any       =							d_model
            _lowerCAmelCase     :				Dict       =							num_hidden_layers
            _lowerCAmelCase     :				Optional[Any]       =							num_attention_heads
            _lowerCAmelCase     :				Tuple       =							ffn_dim
            _lowerCAmelCase     :				Tuple       =							activation_function
            _lowerCAmelCase     :				int       =							activation_dropout
            _lowerCAmelCase     :				Optional[Any]       =							attention_dropout
            _lowerCAmelCase     :				Union[str, Any]       =							max_position_embeddings
            _lowerCAmelCase     :				List[Any]       =							initializer_range
            _lowerCAmelCase     :				List[str]       =							None
            _lowerCAmelCase     :				Any       =							0
            _lowerCAmelCase     :				Any       =							2
            _lowerCAmelCase     :				Optional[Any]       =							1







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig.from_pretrained('facebook/xglm-564M' )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							tf.clip_by_value(
                ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )   ,						clip_value_min=0   ,						clip_value_max=3 )

            _lowerCAmelCase     :				str       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				int       =							random_attention_mask([self.batch_size, self.seq_length] )

            _lowerCAmelCase     :				str       =							self.get_config()

            _lowerCAmelCase     :				Union[str, Any]       =							floats_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2 )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
            )







     def a						(			self ):

            '''simple docstring'''

            return 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=snake_case__   ,						bos_token_id=self.bos_token_id   ,						eos_token_id=self.eos_token_id   ,						pad_token_id=self.pad_token_id   ,						return_dict=snake_case__   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.prepare_config_and_inputs()

            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Dict       =							config_and_inputs

            _lowerCAmelCase     :				Optional[int]       =							{
                'input_ids': input_ids,
                'head_mask': head_mask,
            }

            return config, inputs_dict
@require_tf
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     __magic_name__           =     (TFXGLMForCausalLM,) if is_tf_available() else ()
     __magic_name__           =     (
         {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							TFXGLMModelTester(self )
            _lowerCAmelCase     :				Dict       =							ConfigTester(self   ,						config_class=snake_case__   ,						n_embd=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Optional[Any]       =							TFXGLMModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )







     @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
     def a						(			self ):

            '''simple docstring'''

            super().test_resize_token_embeddings()
@require_tf
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     def a						(			self   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				str       =							tf.convert_to_tensor([[2, 268, 9865]]   ,						dtype=tf.intaa )  # The dog
            # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
            # fmt: off
            _lowerCAmelCase     :				str       =							[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
            # fmt: on
            _lowerCAmelCase     :				Optional[int]       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						num_beams=1 )
            if verify_outputs:
                   self.assertListEqual(output_ids[0].numpy().tolist()   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

            tf.random.set_seed(0 )
            _lowerCAmelCase     :				Optional[int]       =							tokenizer('Today is a nice day and'   ,						return_tensors='tf' )
            _lowerCAmelCase     :				Dict       =							tokenized.input_ids
            # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
            with tf.device(':/CPU:0' ):
                   _lowerCAmelCase     :				Dict       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						seed=[7, 0] )
            _lowerCAmelCase     :				List[Any]       =							tokenizer.decode(output_ids[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				Tuple       =							(
                'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
            )
            self.assertEqual(snake_case__   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

            _lowerCAmelCase     :				Tuple       =							'left'

            # use different length sentences to test batching
            _lowerCAmelCase     :				Optional[int]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When',
                'Hello, my dog is a little',
            ]

            _lowerCAmelCase     :				Tuple       =							tokenizer(snake_case__   ,						return_tensors='tf'   ,						padding=snake_case__ )
            _lowerCAmelCase     :				Any       =							inputs['input_ids']

            _lowerCAmelCase     :				Any       =							model.generate(input_ids=snake_case__   ,						attention_mask=inputs['attention_mask']   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Dict       =							tokenizer(sentences[0]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				int       =							tokenizer(sentences[1]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Optional[Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.batch_decode(snake_case__   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Any       =							tokenizer.decode(output_non_padded[0]   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.decode(output_padded[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
                'a single',
                'Hello, my dog is a little bit of a shy one, but he is very friendly',
            ]
            self.assertListEqual(snake_case__   ,						snake_case__ )
            self.assertListEqual(snake_case__   ,						[non_padded_sentence, padded_sentence] )

