


"""simple docstring"""




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'''







		SCREAMING_SNAKE_CASE_						:     Dict								=     XGLMConfig
		SCREAMING_SNAKE_CASE_						:     int								=     {}
		SCREAMING_SNAKE_CASE_						:     List[str]								=     """gelu"""







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







		def 							__A						(	self				)     ->							Tuple:
								return XGLMConfig.from_pretrained('facebook/xglm-564M'				)







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

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

								SCREAMING_SNAKE_CASE													=					self.get_config()

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

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								)







		def 							__A						(	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 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.prepare_config_and_inputs()

								(
								    (
								    SCREAMING_SNAKE_CASE
								)							,				(
								    SCREAMING_SNAKE_CASE
								)							,				(
								    SCREAMING_SNAKE_CASE
								)							,				(
								    SCREAMING_SNAKE_CASE
								)							,				
								)													=					config_and_inputs

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

								return config, inputs_dict





@require_tf
class 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







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







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					TFXGLMModelTester(self				)
								SCREAMING_SNAKE_CASE													=					ConfigTester(self						,       config_class=lowerCAmelCase__						,       n_embd=37				)







		def 							__A						(	self				)     ->							List[Any]:
								self.config_tester.run_common_tests()







		@slow
		def 							__A						(	self				)     ->							str:
								for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE													=					TFXGLMModel.from_pretrained(lowerCAmelCase__				)
														self.assertIsNotNone(lowerCAmelCase__				)







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





@require_tf
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@slow
		def 							__A						(	self						,       lowerCAmelCase__=True				)     ->							int:
								SCREAMING_SNAKE_CASE													=					TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
								SCREAMING_SNAKE_CASE													=					tf.convert_to_tensor([[2, 268, 9_865]]						,       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
								SCREAMING_SNAKE_CASE													=					[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
								# fmt: on
								SCREAMING_SNAKE_CASE													=					model.generate(lowerCAmelCase__						,       do_sample=lowerCAmelCase__						,       num_beams=1				)
								if verify_outputs:
														self.assertListEqual(output_ids[0].numpy().tolist()						,       lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					XGLMTokenizer.from_pretrained('facebook/xglm-564M'				)
								SCREAMING_SNAKE_CASE													=					TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)

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

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







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
								SCREAMING_SNAKE_CASE													=					XGLMTokenizer.from_pretrained('facebook/xglm-564M'				)

								SCREAMING_SNAKE_CASE													=					'left'

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

								SCREAMING_SNAKE_CASE													=					tokenizer(lowerCAmelCase__						,       return_tensors='tf'						,       padding=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					inputs['input_ids']

								SCREAMING_SNAKE_CASE													=					model.generate(input_ids=lowerCAmelCase__						,       attention_mask=inputs['attention_mask']						,       max_new_tokens=12				)

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

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

								SCREAMING_SNAKE_CASE													=					tokenizer.batch_decode(lowerCAmelCase__						,       skip_special_tokens=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					tokenizer.decode(output_non_padded[0]						,       skip_special_tokens=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					tokenizer.decode(output_padded[0]						,       skip_special_tokens=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					[
								    '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"""




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__UpperCamelCase						=      HfApi()

__UpperCamelCase						=      {}
# fmt: off
__UpperCamelCase						=      torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
__UpperCamelCase						=      torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
__UpperCamelCase						=      torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
__UpperCamelCase						=      torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
__UpperCamelCase						=      torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
__UpperCamelCase						=      torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
__UpperCamelCase						=      torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
__UpperCamelCase						=      torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
__UpperCamelCase						=      torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
__UpperCamelCase						=      torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
__UpperCamelCase						=      torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
__UpperCamelCase						=      torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
__UpperCamelCase						=      torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
__UpperCamelCase						=      torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
__UpperCamelCase						=      torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

__UpperCamelCase						=      api.list_models(filter='''diffusers''')
for mod in models:
	if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
		__UpperCamelCase						=      '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

		print(f'''Started running {mod.modelId}!!!''')

		if mod.modelId.startswith('''CompVis'''):
			__UpperCamelCase						=      UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
		else:
			__UpperCamelCase						=      UNetaDModel.from_pretrained(local_checkpoint)

		torch.manual_seed(0)
		random.seed(0)

		__UpperCamelCase						=      torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
		__UpperCamelCase						=      torch.tensor([10] * noise.shape[0])
		with torch.no_grad():
			__UpperCamelCase						=      model(noise, time_step).sample

		assert torch.allclose(
		    logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
		)
		print(f'''{mod.modelId} has passed successfully!!!''')


