
"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					MODEL_FOR_CAUSAL_LM_MAPPING
			snake_case						   =					TF_MODEL_FOR_CAUSAL_LM_MAPPING




			@require_torch
			def   lowerCamelCase__  (					self    :       Any						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    pipeline(task="text-generation"							, model="sshleifer/tiny-ctrl"							, framework="pt"						)
								# Using `do_sample=False` to force deterministic output
								A_								=    text_generator("This is a test"							, do_sample=_snake_case						)
								self.assertEqual(
								    _snake_case							, [
								        {
								            "generated_text": (
								                "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
								                " oscope. FiliFili@@"
								            )
								        }
								    ]							, )

								A_								=    text_generator(["This is a test", "This is a second test"]						)
								self.assertEqual(
								    _snake_case							, [
								        [
								            {
								                "generated_text": (
								                    "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
								                    " oscope. FiliFili@@"
								                )
								            }
								        ],
								        [
								            {
								                "generated_text": (
								                    "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
								                    " oscope. oscope. FiliFili@@"
								                )
								            }
								        ],
								    ]							, )

								A_								=    text_generator("This is a test"							, do_sample=_snake_case							, num_return_sequences=2							, return_tensors=_snake_case						)
								self.assertEqual(
								    _snake_case							, [
								        {"generated_token_ids": ANY(_snake_case						)},
								        {"generated_token_ids": ANY(_snake_case						)},
								    ]							, )
								A_								=    text_generator.model.config.eos_token_id
								A_								=    "<pad>"
								A_								=    text_generator(
								    ["This is a test", "This is a second test"]							, do_sample=_snake_case							, num_return_sequences=2							, batch_size=2							, return_tensors=_snake_case							, )
								self.assertEqual(
								    _snake_case							, [
								        [
								            {"generated_token_ids": ANY(_snake_case						)},
								            {"generated_token_ids": ANY(_snake_case						)},
								        ],
								        [
								            {"generated_token_ids": ANY(_snake_case						)},
								            {"generated_token_ids": ANY(_snake_case						)},
								        ],
								    ]							, )




			@require_tf
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    pipeline(task="text-generation"							, model="sshleifer/tiny-ctrl"							, framework="tf"						)

								# Using `do_sample=False` to force deterministic output
								A_								=    text_generator("This is a test"							, do_sample=_snake_case						)
								self.assertEqual(
								    _snake_case							, [
								        {
								            "generated_text": (
								                "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
								                " please,"
								            )
								        }
								    ]							, )

								A_								=    text_generator(["This is a test", "This is a second test"]							, do_sample=_snake_case						)
								self.assertEqual(
								    _snake_case							, [
								        [
								            {
								                "generated_text": (
								                    "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
								                    " please,"
								                )
								            }
								        ],
								        [
								            {
								                "generated_text": (
								                    "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
								                    " Cannes 閲閲Cannes Cannes Cannes 攵 please,"
								                )
								            }
								        ],
								    ]							, )




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       List[Any]							, _snake_case    :       Any							, _snake_case    :       Optional[Any]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    TextGenerationPipeline(model=_snake_case							, tokenizer=_snake_case						)
								return text_generator, ["This is a test", "Another test"]




			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    "Hello I believe in"
								A_								=    pipeline("text-generation"							, model="hf-internal-testing/tiny-random-gpt2"						)
								A_								=    text_generator(_snake_case						)
								self.assertEqual(
								    _snake_case							, [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}]							, )

								A_								=    text_generator(_snake_case							, stop_sequence=" fe"						)
								self.assertEqual(_snake_case							, [{"generated_text": "Hello I believe in fe"}]						)




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       str							, _snake_case    :       str						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    text_generator.model
								A_								=    text_generator.tokenizer

								A_								=    text_generator("This is a test"						)
								self.assertEqual(_snake_case							, [{"generated_text": ANY(_snake_case						)}]						)
								self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"						)						)

								A_								=    text_generator("This is a test"							, return_full_text=_snake_case						)
								self.assertEqual(_snake_case							, [{"generated_text": ANY(_snake_case						)}]						)
								self.assertNotIn("This is a test"							, outputs[0]["generated_text"]						)

								A_								=    pipeline(task="text-generation"							, model=_snake_case							, tokenizer=_snake_case							, return_full_text=_snake_case						)
								A_								=    text_generator("This is a test"						)
								self.assertEqual(_snake_case							, [{"generated_text": ANY(_snake_case						)}]						)
								self.assertNotIn("This is a test"							, outputs[0]["generated_text"]						)

								A_								=    text_generator("This is a test"							, return_full_text=_snake_case						)
								self.assertEqual(_snake_case							, [{"generated_text": ANY(_snake_case						)}]						)
								self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"						)						)

								A_								=    text_generator(["This is great !", "Something else"]							, num_return_sequences=2							, do_sample=_snake_case						)
								self.assertEqual(
								    _snake_case							, [
								        [{"generated_text": ANY(_snake_case						)}, {"generated_text": ANY(_snake_case						)}],
								        [{"generated_text": ANY(_snake_case						)}, {"generated_text": ANY(_snake_case						)}],
								    ]							, )

								if text_generator.tokenizer.pad_token is not None:
													A_								=    text_generator(
													    ["This is great !", "Something else"]							, num_return_sequences=2							, batch_size=2							, do_sample=_snake_case						)
													self.assertEqual(
													    _snake_case							, [
													        [{"generated_text": ANY(_snake_case						)}, {"generated_text": ANY(_snake_case						)}],
													        [{"generated_text": ANY(_snake_case						)}, {"generated_text": ANY(_snake_case						)}],
													    ]							, )

								with self.assertRaises(_snake_case						):
													A_								=    text_generator("test"							, return_full_text=_snake_case							, return_text=_snake_case						)
								with self.assertRaises(_snake_case						):
													A_								=    text_generator("test"							, return_full_text=_snake_case							, return_tensors=_snake_case						)
								with self.assertRaises(_snake_case						):
													A_								=    text_generator("test"							, return_text=_snake_case							, return_tensors=_snake_case						)

								# Empty prompt is slighly special
								# it requires BOS token to exist.
								# Special case for Pegasus which will always append EOS so will
								# work even without BOS.
								if (
								    text_generator.tokenizer.bos_token_id is not None
								    or "Pegasus" in tokenizer.__class__.__name__
								    or "Git" in model.__class__.__name__
								):
													A_								=    text_generator(""						)
													self.assertEqual(_snake_case							, [{"generated_text": ANY(_snake_case						)}]						)
								else:
													with self.assertRaises((ValueError, AssertionError)						):
																		A_								=    text_generator(""						)

								if text_generator.framework == "tf":
													# TF generation does not support max_new_tokens, and it's impossible
													# to control long generation with only max_length without
													# fancy calculation, dismissing tests for now.
													return
								# We don't care about infinite range models.
								# They already work.
								# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
								A_								=    ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
								if (
								    tokenizer.model_max_length < 10_000
								    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
								):
													# Handling of large generations
													with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)						):
																		text_generator("This is a test" * 500							, max_new_tokens=20						)

													A_								=    text_generator("This is a test" * 500							, handle_long_generation="hole"							, max_new_tokens=20						)
													# Hole strategy cannot work
													with self.assertRaises(_snake_case						):
																		text_generator(
																		    "This is a test" * 500							, handle_long_generation="hole"							, max_new_tokens=tokenizer.model_max_length + 10							, )




			@require_torch
			@require_accelerate
			@require_torch_gpu
			def   lowerCamelCase__  (					self    :       int						)   ->			Optional[int]:







								"""simple docstring"""
								import torch

								# Classic `model_kwargs`
								A_								=    pipeline(
								    model="hf-internal-testing/tiny-random-bloom"							, model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa}							, )
								self.assertEqual(pipe.model.device							, torch.device(0						)						)
								self.assertEqual(pipe.model.lm_head.weight.dtype							, torch.bfloataa						)
								A_								=    pipe("This is a test"						)
								self.assertEqual(
								    _snake_case							, [
								        {
								            "generated_text": (
								                "This is a test test test test test test test test test test test test test test test test"
								                " test"
								            )
								        }
								    ]							, )

								# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
								A_								=    pipeline(model="hf-internal-testing/tiny-random-bloom"							, device_map="auto"							, torch_dtype=torch.bfloataa						)
								self.assertEqual(pipe.model.device							, torch.device(0						)						)
								self.assertEqual(pipe.model.lm_head.weight.dtype							, torch.bfloataa						)
								A_								=    pipe("This is a test"						)
								self.assertEqual(
								    _snake_case							, [
								        {
								            "generated_text": (
								                "This is a test test test test test test test test test test test test test test test test"
								                " test"
								            )
								        }
								    ]							, )

								# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
								A_								=    pipeline(model="hf-internal-testing/tiny-random-bloom"							, device_map="auto"						)
								self.assertEqual(pipe.model.device							, torch.device(0						)						)
								self.assertEqual(pipe.model.lm_head.weight.dtype							, torch.floataa						)
								A_								=    pipe("This is a test"						)
								self.assertEqual(
								    _snake_case							, [
								        {
								            "generated_text": (
								                "This is a test test test test test test test test test test test test test test test test"
								                " test"
								            )
								        }
								    ]							, )




			@require_torch
			@require_torch_gpu
			def   lowerCamelCase__  (					self    :       Any						)   ->			List[Any]:







								"""simple docstring"""
								import torch

								A_								=    pipeline(model="hf-internal-testing/tiny-random-bloom"							, device=0							, torch_dtype=torch.floataa						)
								pipe("This is a test"						)




			@require_torch
			@require_accelerate
			@require_torch_gpu
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Any:







								"""simple docstring"""
								import torch

								A_								=    pipeline(model="hf-internal-testing/tiny-random-bloom"							, device_map="auto"							, torch_dtype=torch.floataa						)
								pipe("This is a test"							, do_sample=_snake_case							, top_p=0.5						)






			def   lowerCamelCase__  (					self    :       Any						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    "Hello world"
								A_								=    pipeline("text-generation"							, model="hf-internal-testing/tiny-random-gpt2"						)
								if text_generator.model.framework == "tf":
													A_								=    logging.get_logger("transformers.generation.tf_utils"						)
								else:
													A_								=    logging.get_logger("transformers.generation.utils"						)
								A_								=    "Both `max_new_tokens`"  # The beggining of the message to be checked in this test

								# Both are set by the user -> log warning
								with CaptureLogger(_snake_case						) as cl:
													A_								=    text_generator(_snake_case							, max_length=10							, max_new_tokens=1						)
								self.assertIn(_snake_case							, cl.out						)

								# The user only sets one -> no warning
								with CaptureLogger(_snake_case						) as cl:
													A_								=    text_generator(_snake_case							, max_new_tokens=1						)
								self.assertNotIn(_snake_case							, cl.out						)

								with CaptureLogger(_snake_case						) as cl:
													A_								=    text_generator(_snake_case							, max_length=10						)
								self.assertNotIn(_snake_case							, cl.out						)








'''simple docstring'''
import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							A_			    =		MobileNetVaConfig(layer_norm_eps=0.001 )

							if "_quant" in model_name:
														raise ValueError("Quantized models are not supported." )

							A_			    =		re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"						,  UpperCamelCase_ )
							if matches:
														A_			    =		float(matches[1] )
														A_			    =		int(matches[2] )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							A_			    =		1_0_0_1
							A_			    =		"imagenet-1k-id2label.json"
							A_			    =		"huggingface/label-files"
							A_			    =		json.load(open(hf_hub_download(UpperCamelCase_						,  UpperCamelCase_						,  repo_type="dataset" )						,  "r" ) )
							A_			    =		{int(UpperCamelCase_ ) + 1: v for k, v in idalabel.items()}
							A_			    =		"background"
							A_			    =		idalabel
							A_			    =		{v: k for k, v in idalabel.items()}

							return config







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )
							return im







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							A_			    =		get_mobilenet_va_config(UpperCamelCase_ )

							# Load 🤗 model
							A_			    =		MobileNetVaForImageClassification(UpperCamelCase_ ).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							A_			    =		MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}						,  size={"shortest_edge": config.image_size + 3_2}						,  )
							A_			    =		image_processor(images=prepare_img()						,  return_tensors="pt" )
							A_			    =		model(**UpperCamelCase_ )
							A_			    =		outputs.logits

							assert logits.shape == (1, 1_0_0_1)

							if model_name == "mobilenet_v1_1.0_224":
														A_			    =		torch.tensor([-4.1739, -1.1233, 3.1205] )
							elif model_name == "mobilenet_v1_0.75_192":
														A_			    =		torch.tensor([-3.9440, -2.3141, -0.3333] )
							else:
														A_			    =		None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]						,  UpperCamelCase_						,  atol=1e-4 )

							Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
							print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
							model.save_pretrained(UpperCamelCase_ )
							print(f"Saving image processor to {pytorch_dump_folder_path}" )
							image_processor.save_pretrained(UpperCamelCase_ )

							if push_to_hub:
														print("Pushing to the hub..." )
														A_			    =		"google/" + model_name
														image_processor.push_to_hub(UpperCamelCase_ )
														model.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				int									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''mobilenet_v1_1.0_224''',
							    type=str,
							    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)

							__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)





