



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 				_a				:
			A      		=	XGLMConfig
			A      		=	{}
			A      		=	'''gelu'''

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=14,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=37,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=0.0_2,   )       ->   Optional[int]:
						UpperCAmelCase_:		Union[str, Any]							      =     parent
						UpperCAmelCase_:		Tuple							      =     batch_size
						UpperCAmelCase_:		Any							      =     seq_length
						UpperCAmelCase_:		Any							      =     is_training
						UpperCAmelCase_:		int							      =     use_input_mask
						UpperCAmelCase_:		List[Any]							      =     use_labels
						UpperCAmelCase_:		Union[str, Any]							      =     vocab_size
						UpperCAmelCase_:		List[str]							      =     d_model
						UpperCAmelCase_:		List[Any]							      =     num_hidden_layers
						UpperCAmelCase_:		Optional[Any]							      =     num_attention_heads
						UpperCAmelCase_:		str							      =     ffn_dim
						UpperCAmelCase_:		Any							      =     activation_function
						UpperCAmelCase_:		int							      =     activation_dropout
						UpperCAmelCase_:		Union[str, Any]							      =     attention_dropout
						UpperCAmelCase_:		List[Any]							      =     max_position_embeddings
						UpperCAmelCase_:		List[Any]							      =     initializer_range
						UpperCAmelCase_:		Dict							      =     None
						UpperCAmelCase_:		int							      =     0
						UpperCAmelCase_:		Any							      =     2
						UpperCAmelCase_:		List[str]							      =     1

			def      __snake_case       (self					)       ->   int:
						return XGLMConfig.from_pretrained("""facebook/xglm-564M"""					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		List[Any]							      =     tf.clip_by_value(
						    ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					),   clip_value_min=0,   clip_value_max=3					)

						UpperCAmelCase_:		Optional[int]							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		Any							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		Dict							      =     self.get_config()

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

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)

			def      __snake_case       (self					)       ->   List[str]:
						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      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Tuple							      =     self.prepare_config_and_inputs()

						(
						    UpperCAmelCase_
						):		Any							      =     config_and_inputs

						UpperCAmelCase_:		List[str]							      =     {
						    """input_ids""": input_ids,
						    """head_mask""": head_mask,
						}

						return config, inputs_dict







@require_tf
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			A      		=	(TFXGLMForCausalLM,) if is_tf_available() else ()
			A      		=	(
			    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
			)
			A      		=	False
			A      		=	False
			A      		=	False

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     TFXGLMModelTester(self					)
						UpperCAmelCase_:		str							      =     ConfigTester(self,   config_class=A_,   n_embd=37					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						self.config_tester.run_common_tests()

			@slow
			def      __snake_case       (self					)       ->   List[str]:
						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		Tuple							      =     TFXGLMModel.from_pretrained(A_					)
									self.assertIsNotNone(A_					)

			@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""					)
			def      __snake_case       (self					)       ->   Optional[Any]:
						super().test_resize_token_embeddings()







@require_tf
class 				_a				(   unittest.TestCase					):

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

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		str							      =     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""					)
						UpperCAmelCase_:		str							      =     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""					)

						tf.random.set_seed(0					)
						UpperCAmelCase_:		List[Any]							      =     tokenizer("""Today is a nice day and""",   return_tensors="""tf"""					)
						UpperCAmelCase_:		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"""					):
									UpperCAmelCase_:		List[str]							      =     model.generate(A_,   do_sample=A_,   seed=[7, 0]					)
						UpperCAmelCase_:		Optional[int]							      =     tokenizer.decode(output_ids[0],   skip_special_tokens=A_					)

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

			@slow
			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[int]							      =     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""					)
						UpperCAmelCase_:		int							      =     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""					)

						UpperCAmelCase_:		str							      =     """left"""

						# use different length sentences to test batching
						UpperCAmelCase_:		Optional[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""",
						    """Hello, my dog is a little""",
						]

						UpperCAmelCase_:		Optional[int]							      =     tokenizer(A_,   return_tensors="""tf""",   padding=A_					)
						UpperCAmelCase_:		Optional[Any]							      =     inputs["""input_ids"""]

						UpperCAmelCase_:		Dict							      =     model.generate(input_ids=A_,   attention_mask=inputs["""attention_mask"""],   max_new_tokens=12					)

						UpperCAmelCase_:		Optional[int]							      =     tokenizer(sentences[0],   return_tensors="""tf"""					).input_ids
						UpperCAmelCase_:		Tuple							      =     model.generate(input_ids=A_,   max_new_tokens=12					)

						UpperCAmelCase_:		int							      =     tokenizer(sentences[1],   return_tensors="""tf"""					).input_ids
						UpperCAmelCase_:		Dict							      =     model.generate(input_ids=A_,   max_new_tokens=12					)

						UpperCAmelCase_:		Optional[int]							      =     tokenizer.batch_decode(A_,   skip_special_tokens=A_					)
						UpperCAmelCase_:		Optional[int]							      =     tokenizer.decode(output_non_padded[0],   skip_special_tokens=A_					)
						UpperCAmelCase_:		Any							      =     tokenizer.decode(output_padded[0],   skip_special_tokens=A_					)

						UpperCAmelCase_:		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 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]					)










from __future__ import annotations




def    a__    (		snake_case__					:							list[int]     ):
  if len(snake_case__     ) == 0:
    return array

  _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     min(snake_case__     ), max(snake_case__     )

  # Compute the variables
  _UpperCAmelCase		:							Tuple							=     _max - _min + 1
  _UpperCAmelCase,_UpperCAmelCase		:							Union[str, Any]							=     [0] * holes_range, [0] * holes_range

  # Make the sorting.
  for i in array:
    _UpperCAmelCase		:							Optional[int]							=     i - _min
    _UpperCAmelCase		:							Any							=     i
    holes_repeat[index] += 1

  # Makes the array back by replacing the numbers.
  _UpperCAmelCase		:							List[Any]							=     0
  for i in range(snake_case__     ):
    while holes_repeat[i] > 0:
      _UpperCAmelCase		:							Optional[Any]							=     holes[i]
      index += 1
      holes_repeat[i] -= 1

    # Returns the sorted array.
  return array


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE__					:     Dict       =     input('Enter numbers separated by comma:\n')
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     [int(x) for x in user_input.split(',')]
 print(pigeon_sort(unsorted))





