

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__			:		Optional[Any]             =       XGLMConfig
							UpperCAmelCase__			:		int             =       {}
							UpperCAmelCase__			:		Optional[Any]             =       "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			,				)       ->       Dict:
												_a      :       List[str]   =						parent
												_a      :       Optional[Any]   =						batch_size
												_a      :       Dict   =						seq_length
												_a      :       Tuple   =						is_training
												_a      :       List[str]   =						use_input_mask
												_a      :       Optional[Any]   =						use_labels
												_a      :       Dict   =						vocab_size
												_a      :       List[Any]   =						d_model
												_a      :       Any   =						num_hidden_layers
												_a      :       List[Any]   =						num_attention_heads
												_a      :       Tuple   =						ffn_dim
												_a      :       Any   =						activation_function
												_a      :       List[str]   =						activation_dropout
												_a      :       Tuple   =						attention_dropout
												_a      :       Union[str, Any]   =						max_position_embeddings
												_a      :       int   =						initializer_range
												_a      :       Union[str, Any]   =						None
												_a      :       Any   =						0
												_a      :       Tuple   =						2
												_a      :       Union[str, Any]   =						1


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


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

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

												_a      :       Dict   =						self.get_config()

												_a      :       Tuple   =						floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,				2			)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												)


							def 							__lowercase							(     self			)       ->       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 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						self.prepare_config_and_inputs()

												(
												    (
												    _a
												) ,   (
												    _a
												) ,   (
												    _a
												) ,   (
												    _a
												) ,   
												)      :       Dict   =						config_and_inputs

												_a      :       List[str]   =						{
												    '''input_ids''': input_ids,
												    '''head_mask''': head_mask,
												}

												return config, inputs_dict



@require_tf
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







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


							def 							__lowercase							(     self			)       ->       str:
												_a      :       str   =						TFXGLMModelTester(self			)
												_a      :       List[str]   =						ConfigTester(self			,				config_class=_a			,				n_embd=3_7			)


							def 							__lowercase							(     self			)       ->       Tuple:
												self.config_tester.run_common_tests()


							@slow
							def 							__lowercase							(     self			)       ->       List[str]:
												for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Union[str, Any]   =						TFXGLMModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)




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



@require_tf
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@slow
							def 							__lowercase							(     self			,				_a=True			)       ->       Dict:
												_a      :       Any   =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''			)
												_a      :       Any   =						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
												_a      :       Union[str, Any]   =						[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
												_a      :       Any   =						model.generate(_a			,				do_sample=_a			,				num_beams=1			)
												if verify_outputs:
																	self.assertListEqual(output_ids[0].numpy().tolist()			,				_a			)


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

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

												_a      :       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 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''			)
												_a      :       Union[str, Any]   =						XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''			)

												_a      :       Tuple   =						'''left'''

												# use different length sentences to test batching
												_a      :       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''',
												]

												_a      :       Optional[int]   =						tokenizer(_a			,				return_tensors='''tf'''			,				padding=_a			)
												_a      :       int   =						inputs['''input_ids''']

												_a      :       Optional[Any]   =						model.generate(input_ids=_a			,				attention_mask=inputs['''attention_mask''']			,				max_new_tokens=1_2			)

												_a      :       Optional[int]   =						tokenizer(sentences[0]			,				return_tensors='''tf'''			).input_ids
												_a      :       Optional[Any]   =						model.generate(input_ids=_a			,				max_new_tokens=1_2			)

												_a      :       Dict   =						tokenizer(sentences[1]			,				return_tensors='''tf'''			).input_ids
												_a      :       int   =						model.generate(input_ids=_a			,				max_new_tokens=1_2			)

												_a      :       Union[str, Any]   =						tokenizer.batch_decode(_a			,				skip_special_tokens=_a			)
												_a      :       str   =						tokenizer.decode(output_non_padded[0]			,				skip_special_tokens=_a			)
												_a      :       Dict   =						tokenizer.decode(output_padded[0]			,				skip_special_tokens=_a			)

												_a      :       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(_a			,				_a			)
												self.assertListEqual(_a			,				[non_padded_sentence, padded_sentence]			)






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a__												=						numpy.array([0, 0])
a__												=						numpy.array([0.5, 0.8660254])
a__												=						numpy.array([1, 0])
a__												=						[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]						,__a :     int      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						initial_vectors
					for _ in range(__a      ):
										_a      :       int   =						iteration_step(__a      )
					return vectors



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						[]
					for i, start_vector in enumerate(vectors[:-1]      ):
										_a      :       str   =						vectors[i + 1]
										new_vectors.append(__a      )
										_a      :       Optional[int]   =						end_vector - start_vector
										new_vectors.append(start_vector + difference_vector / 3      )
										new_vectors.append(
										    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,60      )      )
										new_vectors.append(start_vector + difference_vector * 2 / 3      )
					new_vectors.append(vectors[-1]      )
					return new_vectors



def   __UpperCAmelCase					(      __a :     numpy.ndarray						,__a :     float      )     ->	numpy.ndarray:

					"""simple docstring"""

					_a      :       Tuple   =						numpy.radians(__a      )
					_a ,   _a      :       List[Any]   =						numpy.cos(__a      ), numpy.sin(__a      )
					_a      :       Dict   =						numpy.array(((c, -s), (s, c))      )
					return numpy.dot(__a						,__a      )



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	None:

					"""simple docstring"""

					_a      :       str   =						plt.gca()
					axes.set_aspect('''equal'''      )

					# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
					# y-coordinates as inputs, which are constructed from the vector-list using
					# zip()
					_a ,   _a      :       Optional[int]   =						zip(*__a      )
					plt.plot(__a						,__a      )
					plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			a__												=						iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)



