




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 		__lowercase						:




      """simple docstring"""





      _snake_case					=		XGLMConfig
      _snake_case					=		{}
      _snake_case					=		"""gelu"""

      def __init__( self							, A							, A=1_4							, A=7							, A=True							, A=True							, A=True							, A=9_9							, A=3_2							, A=2							, A=4							, A=3_7							, A="gelu"							, A=0.1							, A=0.1							, A=5_1_2							, A=0.02							, )						->							Optional[Any]:
         snake_case :							str           =			parent
         snake_case :							Dict           =			batch_size
         snake_case :							Optional[Any]           =			seq_length
         snake_case :							Any           =			is_training
         snake_case :							List[Any]           =			use_input_mask
         snake_case :							Any           =			use_labels
         snake_case :							int           =			vocab_size
         snake_case :							int           =			d_model
         snake_case :							Tuple           =			num_hidden_layers
         snake_case :							Dict           =			num_attention_heads
         snake_case :							str           =			ffn_dim
         snake_case :							List[Any]           =			activation_function
         snake_case :							Tuple           =			activation_dropout
         snake_case :							Tuple           =			attention_dropout
         snake_case :							Optional[int]           =			max_position_embeddings
         snake_case :							Union[str, Any]           =			initializer_range
         snake_case :							int           =			None
         snake_case :							Dict           =			0
         snake_case :							Dict           =			2
         snake_case :							str           =			1

      def       UpperCAmelCase ( self      )						->							List[str]:
         return XGLMConfig.from_pretrained("""facebook/xglm-564M"""      )

      def       UpperCAmelCase ( self      )						->							str:
         snake_case :							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      )

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

         snake_case :							List[Any]           =			self.get_config()

         snake_case :							Optional[Any]           =			floats_tensor([self.num_hidden_layers, self.num_attention_heads]							, 2      )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )

      def       UpperCAmelCase ( self      )						->							Any:
         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=__lowerCamelCase							, bos_token_id=self.bos_token_id							, eos_token_id=self.eos_token_id							, pad_token_id=self.pad_token_id							, return_dict=__lowerCamelCase							, )

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case :							Tuple           =			self.prepare_config_and_inputs()

         (
             snake_case			
         ) :							str           =			config_and_inputs

         snake_case :							Dict           =			{
             '''input_ids''': input_ids,
             '''head_mask''': head_mask,
         }

         return config, inputs_dict




@require_tf
class 		__lowercase						(SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						unittest.TestCase				):




      """simple docstring"""





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

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case :							Optional[int]           =			TFXGLMModelTester(self      )
         snake_case :							Any           =			ConfigTester(self							, config_class=__lowerCamelCase							, n_embd=3_7      )

      def       UpperCAmelCase ( self      )						->							str:
         self.config_tester.run_common_tests()

      @slow
      def       UpperCAmelCase ( self      )						->							int:




         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            snake_case :							Any           =			TFXGLMModel.from_pretrained(__lowerCamelCase      )
            self.assertIsNotNone(__lowerCamelCase      )

      @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""      )
      def       UpperCAmelCase ( self      )						->							str:
         super().test_resize_token_embeddings()




@require_tf
class 		__lowercase						(unittest.TestCase				):




      """simple docstring"""





      @slow
      def       UpperCAmelCase ( self							, A=True      )						->							Tuple:
         snake_case :							Any           =			TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""      )
         snake_case :							Optional[int]           =			tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]							, 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
         snake_case :							Dict           =			[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
         # fmt: on
         snake_case :							int           =			model.generate(__lowerCamelCase							, do_sample=__lowerCamelCase							, num_beams=1      )
         if verify_outputs:
            self.assertListEqual(output_ids[0].numpy().tolist()							, __lowerCamelCase      )

      @slow
      def       UpperCAmelCase ( self      )						->							List[Any]:
         snake_case :							str           =			XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""      )
         snake_case :							List[str]           =			TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""      )

         tf.random.set_seed(0      )
         snake_case :							str           =			tokenizer("""Today is a nice day and"""							, return_tensors="""tf"""      )
         snake_case :							Union[str, Any]           =			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"""      ):
            snake_case :							Dict           =			model.generate(__lowerCamelCase							, do_sample=__lowerCamelCase							, seed=[7, 0]      )
         snake_case :							str           =			tokenizer.decode(output_ids[0]							, skip_special_tokens=__lowerCamelCase      )

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

      @slow
      def       UpperCAmelCase ( self      )						->							Optional[int]:
         snake_case :							Union[str, Any]           =			TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""      )
         snake_case :							int           =			XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""      )

         snake_case :							List[str]           =			'''left'''

         # use different length sentences to test batching
         snake_case :							Tuple           =			[
             '''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''',
         ]

         snake_case :							str           =			tokenizer(__lowerCamelCase							, return_tensors="""tf"""							, padding=__lowerCamelCase      )
         snake_case :							Optional[Any]           =			inputs['''input_ids''']

         snake_case :							Optional[Any]           =			model.generate(input_ids=__lowerCamelCase							, attention_mask=inputs["""attention_mask"""]							, max_new_tokens=1_2      )

         snake_case :							int           =			tokenizer(sentences[0]							, return_tensors="""tf"""      ).input_ids
         snake_case :							Dict           =			model.generate(input_ids=__lowerCamelCase							, max_new_tokens=1_2      )

         snake_case :							str           =			tokenizer(sentences[1]							, return_tensors="""tf"""      ).input_ids
         snake_case :							Dict           =			model.generate(input_ids=__lowerCamelCase							, max_new_tokens=1_2      )

         snake_case :							int           =			tokenizer.batch_decode(__lowerCamelCase							, skip_special_tokens=__lowerCamelCase      )
         snake_case :							List[str]           =			tokenizer.decode(output_non_padded[0]							, skip_special_tokens=__lowerCamelCase      )
         snake_case :							str           =			tokenizer.decode(output_padded[0]							, skip_special_tokens=__lowerCamelCase      )

         snake_case :							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(__lowerCamelCase							, __lowerCamelCase      )
         self.assertListEqual(__lowerCamelCase							, [non_padded_sentence, padded_sentence]      )












"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->list:



    '''simple docstring'''







    for i in range(len(snake_case_		) - 1							,0							,-1		):
        __A       :		Union[str, Any]							=	False

        for j in range(snake_case_							,0							,-1		):
            if unsorted[j] < unsorted[j - 1]:
                __A					,			__A       :		Union[str, Any]							=	unsorted[j - 1], unsorted[j]
                __A       :		Optional[int]							=	True

        for j in range(snake_case_		):
            if unsorted[j] > unsorted[j + 1]:
                __A					,			__A       :		Optional[Any]							=	unsorted[j + 1], unsorted[j]
                __A       :		Union[str, Any]							=	True

        if not swapped:
            break
    return unsorted


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       a_					=     input("""Enter numbers separated by a comma:\n""").strip()
       a_					=     [int(item) for item in user_input.split(""",""")]
       print(f'''{cocktail_shaker_sort(unsorted) = }''')




