


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"""






    UpperCAmelCase__						:		List[Any]      =  XGLMConfig
    UpperCAmelCase__						:		List[str]      =  {}
    UpperCAmelCase__						:		Any      =  "gelu"






    def __init__(			self					,   A_					,   A_=14					,   A_=7					,   A_=True					,   A_=True					,   A_=True					,   A_=99					,   A_=32					,   A_=2					,   A_=4					,   A_=37					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=0.02					,   )  ->       Optional[Any]:
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=seq_length
         __UpperCamelCase    							=is_training
         __UpperCamelCase    							=use_input_mask
         __UpperCamelCase    							=use_labels
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=ffn_dim
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=None
         __UpperCamelCase    							=0
         __UpperCamelCase    							=2
         __UpperCamelCase    							=1






    def _a					(			self    )  ->       Optional[Any]:
         return XGLMConfig.from_pretrained('facebook/xglm-564M'    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )					,   clip_value_min=0					,   clip_value_max=3    )

         __UpperCamelCase    							=None
         if self.use_input_mask:
              __UpperCamelCase    							=random_attention_mask([self.batch_size, self.seq_length]    )

         __UpperCamelCase    							=self.get_config()

         __UpperCamelCase    							=floats_tensor([self.num_hidden_layers, self.num_attention_heads]					,   2    )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )






    def _a					(			self    )  ->       Optional[int]:
         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=A_					,   bos_token_id=self.bos_token_id					,   eos_token_id=self.eos_token_id					,   pad_token_id=self.pad_token_id					,   return_dict=A_					,   )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.prepare_config_and_inputs()

         (
             (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      
         )    							=config_and_inputs

         __UpperCamelCase    							={
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict






@require_tf
class     UpperCAmelCase__       (					A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		int      =  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__						:		Optional[Any]      =  (TFXGLMForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__						:		Dict      =  (
        {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__						:		Tuple      =  False
    UpperCAmelCase__						:		Optional[int]      =  False
    UpperCAmelCase__						:		Optional[Any]      =  False






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=TFXGLMModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   n_embd=37    )






    def _a					(			self    )  ->       Any:
         self.config_tester.run_common_tests()






    @slow
    def _a					(			self    )  ->       Optional[int]:
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase    							=TFXGLMModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )






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






@require_tf
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    @slow
    def _a					(			self					,   A_=True    )  ->       int:
         __UpperCamelCase    							=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
         __UpperCamelCase    							=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
         __UpperCamelCase    							=[2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
         # fmt: on
         __UpperCamelCase    							=model.generate(A_					,   do_sample=A_					,   num_beams=1    )
         if verify_outputs:
              self.assertListEqual(output_ids[0].numpy().tolist()					,   A_    )






    @slow
    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )
         __UpperCamelCase    							=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )

         tf.random.set_seed(0    )
         __UpperCamelCase    							=tokenizer('Today is a nice day and'					,   return_tensors='tf'    )
         __UpperCamelCase    							=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'    ):
              __UpperCamelCase    							=model.generate(A_					,   do_sample=A_					,   seed=[7, 0]    )
         __UpperCamelCase    							=tokenizer.decode(output_ids[0]					,   skip_special_tokens=A_    )

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






    @slow
    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
         __UpperCamelCase    							=XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )

         __UpperCamelCase    							='left'

         # use different length sentences to test batching
         __UpperCamelCase    							=[
             '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',
         ]

         __UpperCamelCase    							=tokenizer(A_					,   return_tensors='tf'					,   padding=A_    )
         __UpperCamelCase    							=inputs['input_ids']

         __UpperCamelCase    							=model.generate(input_ids=A_					,   attention_mask=inputs['attention_mask']					,   max_new_tokens=12    )

         __UpperCamelCase    							=tokenizer(sentences[0]					,   return_tensors='tf'    ).input_ids
         __UpperCamelCase    							=model.generate(input_ids=A_					,   max_new_tokens=12    )

         __UpperCamelCase    							=tokenizer(sentences[1]					,   return_tensors='tf'    ).input_ids
         __UpperCamelCase    							=model.generate(input_ids=A_					,   max_new_tokens=12    )

         __UpperCamelCase    							=tokenizer.batch_decode(A_					,   skip_special_tokens=A_    )
         __UpperCamelCase    							=tokenizer.decode(output_non_padded[0]					,   skip_special_tokens=A_    )
         __UpperCamelCase    							=tokenizer.decode(output_padded[0]					,   skip_special_tokens=A_    )

         __UpperCamelCase    							=[
             '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(A_					,   A_    )
         self.assertListEqual(A_					,   [non_padded_sentence, padded_sentence]    )








def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Tuple:





    '''simple docstring'''






    A__          =   0
    A__          =   len(SCREAMING_SNAKE_CASE__      ) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
            return None

        A__          =   sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A__          =   left
                A__          =   point
            elif point > right:
                A__          =   right
                A__          =   point
            else:
                if item < current_item:
                    A__          =   point - 1
                else:
                    A__          =   point + 1
    return None
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      str:





    '''simple docstring'''






    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    elif point > right:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point - 1      )
        else:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point + 1    ,			SCREAMING_SNAKE_CASE__      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    if collection != sorted(SCREAMING_SNAKE_CASE__      ):
        raise ValueError('Collection must be ascending sorted'      )
    return True


if __name__ == "__main__":
     import sys
     lowercase_       		=  0
     if debug == 1:
          lowercase_       		=  [10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("Sequence must be ascending sorted to apply interpolation search")
          lowercase_       		=  67

     lowercase_       		=  interpolation_search(collection, target)
     if result is not None:
          print(f"""{target} found at positions: {result}""")
     else:
          print("Not found")

