




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       lowerCAmelCase__       :


				"""simple docstring"""





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



				def __init__(     self					:	Dict    ,   lowercase__					:	Tuple    ,   lowercase__					:	int=1_4    ,   lowercase__					:	List[Any]=7    ,   lowercase__					:	str=True    ,   lowercase__					:	Optional[Any]=True    ,   lowercase__					:	Optional[int]=True    ,   lowercase__					:	Union[str, Any]=9_9    ,   lowercase__					:	List[str]=3_2    ,   lowercase__					:	List[str]=2    ,   lowercase__					:	Union[str, Any]=4    ,   lowercase__					:	List[str]=3_7    ,   lowercase__					:	List[str]="gelu"    ,   lowercase__					:	List[str]=0.1    ,   lowercase__					:	Tuple=0.1    ,   lowercase__					:	int=5_1_2    ,   lowercase__					:	Dict=0.0_2    ,   ):
						__lowercase				:		List[Any]												=				parent
						__lowercase				:		int												=				batch_size
						__lowercase				:		Optional[Any]												=				seq_length
						__lowercase				:		List[Any]												=				is_training
						__lowercase				:		List[str]												=				use_input_mask
						__lowercase				:		Any												=				use_labels
						__lowercase				:		int												=				vocab_size
						__lowercase				:		Union[str, Any]												=				d_model
						__lowercase				:		int												=				num_hidden_layers
						__lowercase				:		Tuple												=				num_attention_heads
						__lowercase				:		List[Any]												=				ffn_dim
						__lowercase				:		Optional[Any]												=				activation_function
						__lowercase				:		Dict												=				activation_dropout
						__lowercase				:		Any												=				attention_dropout
						__lowercase				:		Optional[int]												=				max_position_embeddings
						__lowercase				:		Any												=				initializer_range
						__lowercase				:		Union[str, Any]												=				None
						__lowercase				:		Any												=				0
						__lowercase				:		Any												=				2
						__lowercase				:		Optional[Any]												=				1



				def       snake_case    (     self					:	Tuple						):
						return XGLMConfig.from_pretrained("facebook/xglm-564M"						)



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Any												=				tf.clip_by_value(
						    ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size						)    ,   clip_value_min=0    ,   clip_value_max=3						)

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

						__lowercase				:		Tuple												=				self.get_config()

						__lowercase				:		Optional[int]												=				floats_tensor([self.num_hidden_layers, self.num_attention_heads]    ,   2						)

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)



				def       snake_case    (     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=lowercase__    ,   bos_token_id=self.bos_token_id    ,   eos_token_id=self.eos_token_id    ,   pad_token_id=self.pad_token_id    ,   return_dict=lowercase__    ,   )



				def       snake_case    (     self					:	int						):
						__lowercase				:		Union[str, Any]												=				self.prepare_config_and_inputs()

						(
						    __lowercase
						)				:		Any												=				config_and_inputs

						__lowercase				:		Dict												=				{
						    "input_ids": input_ids,
						    "head_mask": head_mask,
						}

						return config, inputs_dict





@require_tf
class       lowerCAmelCase__       (			lowerCAmelCase_  ,			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





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



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Optional[int]												=				TFXGLMModelTester(self						)
						__lowercase				:		List[Any]												=				ConfigTester(self    ,   config_class=lowercase__    ,   n_embd=3_7						)



				def       snake_case    (     self					:	Any						):
						self.config_tester.run_common_tests()



				@slow
				def       snake_case    (     self					:	Optional[int]						):
						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase				:		Optional[Any]												=				TFXGLMModel.from_pretrained(lowercase__						)
								self.assertIsNotNone(lowercase__						)



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





@require_tf
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				@slow
				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	int=True						):
						__lowercase				:		Optional[Any]												=				TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"						)
						__lowercase				:		Dict												=				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
						__lowercase				:		Tuple												=				[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
						__lowercase				:		List[Any]												=				model.generate(lowercase__    ,   do_sample=lowercase__    ,   num_beams=1						)
						if verify_outputs:
								self.assertListEqual(output_ids[0].numpy().tolist()    ,   lowercase__						)



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

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

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



				@slow
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		int												=				TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"						)
						__lowercase				:		Any												=				XGLMTokenizer.from_pretrained("facebook/xglm-564M"						)

						__lowercase				:		Optional[int]												=				"left"

						# use different length sentences to test batching
						__lowercase				:		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",
						]

						__lowercase				:		Optional[int]												=				tokenizer(lowercase__    ,   return_tensors="tf"    ,   padding=lowercase__						)
						__lowercase				:		List[Any]												=				inputs["input_ids"]

						__lowercase				:		str												=				model.generate(input_ids=lowercase__    ,   attention_mask=inputs["attention_mask"]    ,   max_new_tokens=1_2						)

						__lowercase				:		List[str]												=				tokenizer(sentences[0]    ,   return_tensors="tf"						).input_ids
						__lowercase				:		Any												=				model.generate(input_ids=lowercase__    ,   max_new_tokens=1_2						)

						__lowercase				:		Optional[Any]												=				tokenizer(sentences[1]    ,   return_tensors="tf"						).input_ids
						__lowercase				:		Optional[Any]												=				model.generate(input_ids=lowercase__    ,   max_new_tokens=1_2						)

						__lowercase				:		Tuple												=				tokenizer.batch_decode(lowercase__    ,   skip_special_tokens=lowercase__						)
						__lowercase				:		int												=				tokenizer.decode(output_non_padded[0]    ,   skip_special_tokens=lowercase__						)
						__lowercase				:		List[str]												=				tokenizer.decode(output_padded[0]    ,   skip_special_tokens=lowercase__						)

						__lowercase				:		List[str]												=				[
						    "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(lowercase__    ,   lowercase__						)
						self.assertListEqual(lowercase__    ,   [non_padded_sentence, padded_sentence]						)






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
