



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
lowerCamelCase__     =	logging.getLogger(__name__)

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[Any]      ):
				"""simple docstring"""







				np.random.seed(args.seed      )
				torch.manual_seed(args.seed      )
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed      )

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						argparse.ArgumentParser()
				parser.add_argument(
				    """--model_name_or_path"""			,       default=_SCREAMING_SNAKE_CASE			,       type=_SCREAMING_SNAKE_CASE			,       required=_SCREAMING_SNAKE_CASE			,       )
				parser.add_argument("""--prompt"""			,       type=_SCREAMING_SNAKE_CASE			,       default=""""""      )
				parser.add_argument("""--length"""			,       type=_SCREAMING_SNAKE_CASE			,       default=20      )
				parser.add_argument("""--stop_token"""			,       type=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       help="""Token at which text generation is stopped"""      )
				parser.add_argument(
				    """--temperature"""			,       type=_SCREAMING_SNAKE_CASE			,       default=1.0			,       help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""			,       )
				parser.add_argument(
				    """--repetition_penalty"""			,       type=_SCREAMING_SNAKE_CASE			,       default=1.0			,       help="""primarily useful for CTRL model; in that case, use 1.2"""      )
				parser.add_argument("""--k"""			,       type=_SCREAMING_SNAKE_CASE			,       default=0      )
				parser.add_argument("""--penalty_alpha"""			,       type=_SCREAMING_SNAKE_CASE			,       default=0.0      )
				parser.add_argument("""--p"""			,       type=_SCREAMING_SNAKE_CASE			,       default=0.9      )

				parser.add_argument("""--prefix"""			,       type=_SCREAMING_SNAKE_CASE			,       default=""""""			,       help="""Text added prior to input."""      )
				parser.add_argument("""--padding_text"""			,       type=_SCREAMING_SNAKE_CASE			,       default=""""""			,       help="""Deprecated, the use of `--prefix` is preferred."""      )
				parser.add_argument("""--xlm_language"""			,       type=_SCREAMING_SNAKE_CASE			,       default=""""""			,       help="""Optional language when used with the XLM model."""      )

				parser.add_argument("""--seed"""			,       type=_SCREAMING_SNAKE_CASE			,       default=42			,       help="""random seed for initialization"""      )
				parser.add_argument("""--no_cuda"""			,       action="""store_true"""			,       help="""Avoid using CUDA when available"""      )
				parser.add_argument(
				    """--fp16"""			,       action="""store_true"""			,       help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""			,       )
				__a					       =						parser.parse_args()

				__a					       =						torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
				__a					       =						0 if args.no_cuda else torch.cuda.device_count()

				logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"      )

				set_seed(_SCREAMING_SNAKE_CASE      )

				# Initialize the model and tokenizer
				__a					       =						AutoTokenizer.from_pretrained(args.model_name_or_path      )
				__a					       =						AutoModelForCausalLM.from_pretrained(args.model_name_or_path      )

				# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
				# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
				model.to(args.device      )

				if args.fpaa:
								model.half()

				logger.info(_SCREAMING_SNAKE_CASE      )
				__a					       =						args.prompt if args.prompt else input("""Model prompt >>> """      )

				__a					       =						tokenizer(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       add_special_tokens=_SCREAMING_SNAKE_CASE      )
				__a					       =						{key: value.to(args.device      ) for key, value in inputs.items()}

				__a					       =						model.generate(
				    **_SCREAMING_SNAKE_CASE			,       max_length=args.length + len(inputs["""input_ids"""][0]      )			,       penalty_alpha=args.penalty_alpha			,       top_k=args.k			,       )

				__a					       =						[]
				for generated_sequence_idx, generated_sequence in enumerate(_SCREAMING_SNAKE_CASE      ):
								print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="      )
								__a					       =						generated_sequence.tolist()

								# Decode text
								__a					       =						tokenizer.decode(_SCREAMING_SNAKE_CASE			,       clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE			,       add_special_tokens=_SCREAMING_SNAKE_CASE      )

								# Remove all text after the stop token
								__a					       =						text[: text.find(args.stop_token      ) if args.stop_token else None]

								# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
								__a					       =						(
								    prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]			,       clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE      )      ) :]
								)

								generated_sequences.append(_SCREAMING_SNAKE_CASE      )
								print(_SCREAMING_SNAKE_CASE      )

				return generated_sequences


if __name__ == "__main__":
						main()





import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str ,			__lowercase						:						str=3 ,			__lowercase						:						Optional[int]=32 ,			__lowercase						:						Union[str, Any]=3 ,			__lowercase						:						str=10 ,			__lowercase						:						int=[10, 20, 30, 40] ,			__lowercase						:						Any=[1, 1, 2, 1] ,			__lowercase						:						Optional[int]=True ,			__lowercase						:						Optional[int]=True ,			__lowercase						:						int="relu" ,			__lowercase						:						Union[str, Any]=3 ,			__lowercase						:						List[str]=None ,			):

											'''simple docstring'''
											__a					       =						parent
											__a					       =						batch_size
											__a					       =						image_size
											__a					       =						num_channels
											__a					       =						embeddings_size
											__a					       =						hidden_sizes
											__a					       =						depths
											__a					       =						is_training
											__a					       =						use_labels
											__a					       =						hidden_act
											__a					       =						num_labels
											__a					       =						scope
											__a					       =						len(__lowercase		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

											__a					       =						self.get_config()

											return config, pixel_values





							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''
											return ResNetConfig(
											    num_channels=self.num_channels ,			embeddings_size=self.embeddings_size ,			hidden_sizes=self.hidden_sizes ,			depths=self.depths ,			hidden_act=self.hidden_act ,			num_labels=self.num_labels ,			image_size=self.image_size ,			)





							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						FlaxResNetModel(config=__lowercase		)
											__a					       =						model(__lowercase		)

											# Output shape (b, c, h, w)
											self.parent.assertEqual(
											    result.last_hidden_state.shape ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,			)





							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						Dict ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						self.num_labels
											__a					       =						FlaxResNetForImageClassification(config=__lowercase		)
											__a					       =						model(__lowercase		)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.prepare_config_and_inputs()
											__a       ,   __a					       =						config_and_inputs
											__a					       =						{"""pixel_values""": pixel_values}
											return config, inputs_dict






@require_flax
class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__     , unittest.TestCase   ):
							__lowerCamelCase		:		int      =(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							__lowerCamelCase		:		Union[str, Any]      =False
							__lowerCamelCase		:		Optional[Any]      =False
							__lowerCamelCase		:		str      =False





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						FlaxResNetModelTester(self		)
											__a					       =						ConfigTester(self ,			config_class=__lowercase ,			has_text_modality=__lowercase		)





							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()





							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											return





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase		)





							@unittest.skip(reason="""ResNet does not use inputs_embeds"""		)
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											pass





							@unittest.skip(reason="""ResNet does not support input and output embeddings"""		)
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											pass





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a       ,   __a					       =						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__a					       =						model_class(__lowercase		)
															__a					       =						inspect.signature(model.__call__		)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__a					       =						[*signature.parameters.keys()]

															__a					       =						["""pixel_values"""]
															self.assertListEqual(arg_names[:1] ,			__lowercase		)





							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											def check_hidden_states_output(__lowercase						:						Tuple ,			__lowercase						:						List[str] ,			__lowercase						:						str		):
															__a					       =						model_class(__lowercase		)

															__a					       =						model(**self._prepare_for_class(__lowercase ,			__lowercase		)		)

															__a					       =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__a					       =						self.model_tester.num_stages
															self.assertEqual(len(__lowercase		) ,			expected_num_stages + 1		)





							@unittest.skip(reason="""ResNet does not use feedforward chunking"""		)
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											pass







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__		):
																			__a					       =						self._prepare_for_class(__lowercase ,			__lowercase		)
																			__a					       =						model_class(__lowercase		)

																			@jax.jit
																			def model_jitted(__lowercase						:						List[str] ,			**__lowercase						:						Optional[int]		):
																							return model(pixel_values=__lowercase ,			**__lowercase		)

																			with self.subTest("""JIT Enabled"""		):
																							__a					       =						model_jitted(**__lowercase		).to_tuple()

																			with self.subTest("""JIT Disabled"""		):
																							with jax.disable_jit():
																											__a					       =						model_jitted(**__lowercase		).to_tuple()

																			self.assertEqual(len(__lowercase		) ,			len(__lowercase		)		)
																			for jitted_output, output in zip(__lowercase ,			__lowercase		):
																							self.assertEqual(jitted_output.shape ,			output.shape		)



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
				return image






@require_flax
class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							@cached_property
							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""		) if is_vision_available() else None







							@slow
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""		)

											__a					       =						self.default_image_processor
											__a					       =						prepare_img()
											__a					       =						image_processor(images=__lowercase ,			return_tensors="""np"""		)

											__a					       =						model(**__lowercase		)

											# verify the logits
											__a					       =						(1, 1000)
											self.assertEqual(outputs.logits.shape ,			__lowercase		)

											__a					       =						jnp.array([-11.1069, -9.7877, -8.3777]		)

											self.assertTrue(jnp.allclose(outputs.logits[0, :3] ,			__lowercase ,			atol=1E-4		)		)
