




"""simple docstring"""

import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class      lowerCamelCase__      (					unittest.TestCase     ):


			@patch("sys.argv"			,	["fakeprogrampath", "env"]							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					import transformers.commands.transformers_cli

					with CaptureStd() as cs:
							transformers.commands.transformers_cli.main()
					self.assertIn("Python version"			,	cs.out							)
					self.assertIn("Platform"			,	cs.out							)
					self.assertIn("Using distributed or parallel set-up in script?"			,	cs.out							)


			@is_pt_tf_cross_test
			@patch(
			    "sys.argv"			,	["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					import transformers.commands.transformers_cli

					shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"			,	ignore_errors=SCREAMING_SNAKE_CASE							)  # cleans potential past runs
					transformers.commands.transformers_cli.main()

					# The original repo has no TF weights -- if they exist, they were created by the CLI
					self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"							)							)


			@require_torch
			@patch("sys.argv"			,	["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					import transformers.commands.transformers_cli

					# # remove any previously downloaded model to start clean
					shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"			,	ignore_errors=SCREAMING_SNAKE_CASE							)

					# run the command
					transformers.commands.transformers_cli.main()

					# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
					self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"							)							)
					self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"							)							)
					self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"							)							)



			@require_torch
			@patch(
			    "sys.argv"			,	[
			        "fakeprogrampath",
			        "download",
			        "hf-internal-testing/test_dynamic_model_with_tokenizer",
			        "--trust-remote-code",
			        "--cache-dir",
			        "/tmp",
			    ]			,	)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					import transformers.commands.transformers_cli

					# # remove any previously downloaded model to start clean
					shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"			,	ignore_errors=SCREAMING_SNAKE_CASE							)

					# run the command
					transformers.commands.transformers_cli.main()

					# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
					self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"							)							)
					self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"							)							)
					self.assertTrue(
					    os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"							)							)







"""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      lowerCamelCase__      (					unittest.TestCase     ):
			a__	: List[str]        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			a__	: List[Any]        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(model.config			,	SCREAMING_SNAKE_CASE							):
							snake_case     ,			snake_case						:  Optional[Any]        =			list(tokenizer.lang_code_to_id.keys()							)[:2]
							snake_case						:  Tuple        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang=SCREAMING_SNAKE_CASE			,	tgt_lang=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  List[Any]        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					return translator, ["Some string", "Some other text"]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			translator("Some string"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  Optional[Any]        =			translator(["Some string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  List[Any]        =			translator(["Some string", "other string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  Union[str, Any]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  int        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  List[str]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)



			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  Tuple        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)







class      lowerCamelCase__      (					unittest.TestCase     ):


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					# but we do for this one
					snake_case						:  List[str]        =			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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
					snake_case						:  Union[str, Any]        =			MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

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

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

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

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


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"patrickvonplaten/t5-tiny-random"
					snake_case						:  Tuple        =			pipeline(task="translation_cn_to_ar"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"cn"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"ar"							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			"patrickvonplaten/t5-tiny-random"
					with pytest.warns(SCREAMING_SNAKE_CASE			,	match=r".*translation_en_to_de.*"							):
							snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE							)
					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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

