




"""simple docstring"""

import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY
@is_pipeline_test
class      A__   (   unittest.TestCase  ):
	A_				:					Tuple      =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	A_				:					str      =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




	def 				a_		( self	:						str   ,     lowercase	:						Any   ,     lowercase	:						str   ,     lowercase	:						Optional[Any]						)       ->			List[str]:






						if isinstance(model.config   ,     lowercase						):
											__snake_case   ,   __snake_case        =						list(tokenizer.lang_code_to_id.keys()						)[:2]
											__snake_case        =						TranslationPipeline(model=lowercase   ,     tokenizer=lowercase   ,     src_lang=lowercase   ,     tgt_lang=lowercase						)
						else:
											__snake_case        =						TranslationPipeline(model=lowercase   ,     tokenizer=lowercase						)
						return translator, ["Some string", "Some other text"]




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						str   ,     lowercase	:						str						)       ->			List[Any]:
						__snake_case        =						translator("""Some string"""						)
						self.assertEqual(lowercase   ,     [{"""translation_text""": ANY(lowercase						)}]						)

						__snake_case        =						translator(["""Some string"""]						)
						self.assertEqual(lowercase   ,     [{"""translation_text""": ANY(lowercase						)}]						)

						__snake_case        =						translator(["""Some string""", """other string"""]						)
						self.assertEqual(lowercase   ,     [{"""translation_text""": ANY(lowercase						)}, {"""translation_text""": ANY(lowercase						)}]						)




	@require_torch
	def 				a_		( self	:						List[Any]						)       ->			List[Any]:
						__snake_case        =						pipeline("""translation_en_to_ro"""   ,     model="""patrickvonplaten/t5-tiny-random"""   ,     framework="""pt"""						)
						__snake_case        =						translator("""This is a test string"""   ,     max_length=20						)
						self.assertEqual(
						    lowercase   ,     [
						        {
						            """translation_text""": (
						                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
						                """ Beide Beide"""
						            )
						        }
						    ]   ,     )




	@require_tf
	def 				a_		( self	:						Optional[Any]						)       ->			str:
						__snake_case        =						pipeline("""translation_en_to_ro"""   ,     model="""patrickvonplaten/t5-tiny-random"""   ,     framework="""tf"""						)
						__snake_case        =						translator("""This is a test string"""   ,     max_length=20						)
						self.assertEqual(
						    lowercase   ,     [
						        {
						            """translation_text""": (
						                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
						                """ Beide Beide"""
						            )
						        }
						    ]   ,     )




	@require_torch
	def 				a_		( self	:						Dict						)       ->			Union[str, Any]:
						__snake_case        =						pipeline("""translation_en_to_de"""   ,     model="""patrickvonplaten/t5-tiny-random"""   ,     framework="""pt"""						)
						__snake_case        =						translator("""This is a test string"""   ,     max_length=20						)
						self.assertEqual(
						    lowercase   ,     [
						        {
						            """translation_text""": (
						                """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
						                """ urine urine urine urine urine urine urine"""
						            )
						        }
						    ]   ,     )




	@require_tf
	def 				a_		( self	:						str						)       ->			int:
						__snake_case        =						pipeline("""translation_en_to_de"""   ,     model="""patrickvonplaten/t5-tiny-random"""   ,     framework="""tf"""						)
						__snake_case        =						translator("""This is a test string"""   ,     max_length=20						)
						self.assertEqual(
						    lowercase   ,     [
						        {
						            """translation_text""": (
						                """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
						                """ urine urine urine urine urine urine urine"""
						            )
						        }
						    ]   ,     )

class      A__   (   unittest.TestCase  ):




	@require_torch
	@slow
	def 				a_		( self	:						Union[str, Any]						)       ->			int:






						with self.assertRaises(lowercase						):
											pipeline(task="""translation_cn_to_ar"""						)

						# but we do for this one
						__snake_case        =						pipeline(task="""translation_en_to_de"""						)
						self.assertEqual(translator._preprocess_params["""src_lang"""]   ,     """en"""						)
						self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,     """de"""						)




	@require_torch
	@slow
	def 				a_		( self	:						Tuple						)       ->			Optional[int]:
						__snake_case        =						MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""						)
						__snake_case        =						MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""						)

						__snake_case        =						pipeline(task="""translation"""   ,     model=lowercase   ,     tokenizer=lowercase						)
						# Missing src_lang, tgt_lang
						with self.assertRaises(lowercase						):
											translator("""This is a test"""						)

						__snake_case        =						translator("""This is a test"""   ,     src_lang="""en_XX"""   ,     tgt_lang="""ar_AR"""						)
						self.assertEqual(lowercase   ,     [{"""translation_text""": """هذا إختبار"""}]						)

						__snake_case        =						translator("""This is a test"""   ,     src_lang="""en_XX"""   ,     tgt_lang="""hi_IN"""						)
						self.assertEqual(lowercase   ,     [{"""translation_text""": """यह एक परीक्षण है"""}]						)

						# src_lang, tgt_lang can be defined at pipeline call time
						__snake_case        =						pipeline(task="""translation"""   ,     model=lowercase   ,     tokenizer=lowercase   ,     src_lang="""en_XX"""   ,     tgt_lang="""ar_AR"""						)
						__snake_case        =						translator("""This is a test"""						)
						self.assertEqual(lowercase   ,     [{"""translation_text""": """هذا إختبار"""}]						)




	@require_torch
	def 				a_		( self	:						Dict						)       ->			int:
						__snake_case        =						"""patrickvonplaten/t5-tiny-random"""
						__snake_case        =						pipeline(task="""translation_cn_to_ar"""   ,     model=lowercase						)
						self.assertEqual(translator._preprocess_params["""src_lang"""]   ,     """cn"""						)
						self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,     """ar"""						)




	@require_torch
	def 				a_		( self	:						str						)       ->			List[str]:
						__snake_case        =						"""patrickvonplaten/t5-tiny-random"""
						with pytest.warns(lowercase   ,     match=r""".*translation_en_to_de.*"""						):
											__snake_case        =						pipeline(task="""translation"""   ,     model=lowercase						)
						self.assertEqual(translator.task   ,     """translation_en_to_de"""						)
						self.assertEqual(translator._preprocess_params["""src_lang"""]   ,     """en"""						)
						self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,     """de"""						)




	@require_torch
	def 				a_		( self	:						Dict						)       ->			str:






						with self.assertRaises(lowercase						):
											pipeline(task="""translation"""						)







import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
