






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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							A_						  =					MODEL_FOR_CAUSAL_LM_MAPPING
							A_						  =					TF_MODEL_FOR_CAUSAL_LM_MAPPING

							@require_torch
							def       __A (  self:   List[Any]   )    ->				List[str]:
												_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=__A   )
												self.assertEqual(
												    __A		,     [
												        {
												            '''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(
												    __A		,     [
												        [
												            {
												                '''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=__A		,     num_return_sequences=2		,     return_tensors=__A   )
												self.assertEqual(
												    __A		,     [
												        {'''generated_token_ids''': ANY(__A   )},
												        {'''generated_token_ids''': ANY(__A   )},
												    ]		,     )
												_A								= text_generator.model.config.eos_token_id
												_A								= '''<pad>'''
												_A								= text_generator(
												    ['''This is a test''', '''This is a second test''']		,     do_sample=__A		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=__A		,     )
												self.assertEqual(
												    __A		,     [
												        [
												            {'''generated_token_ids''': ANY(__A   )},
												            {'''generated_token_ids''': ANY(__A   )},
												        ],
												        [
												            {'''generated_token_ids''': ANY(__A   )},
												            {'''generated_token_ids''': ANY(__A   )},
												        ],
												    ]		,     )

							@require_tf
							def       __A (  self:   int   )    ->				List[Any]:
												_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=__A   )
												self.assertEqual(
												    __A		,     [
												        {
												            '''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=__A   )
												self.assertEqual(
												    __A		,     [
												        [
												            {
												                '''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       __A (  self:   Dict		,     __A:   Tuple		,     __A:   Union[str, Any]		,     __A:   List[Any]   )    ->				Any:
												_A								= TextGenerationPipeline(model=__A		,     tokenizer=__A   )
												return text_generator, ["This is a test", "Another test"]

							def       __A (  self:   Any   )    ->				Any:
												_A								= '''Hello I believe in'''
												_A								= pipeline('''text-generation'''		,     model='''hf-internal-testing/tiny-random-gpt2'''   )
												_A								= text_generator(__A   )
												self.assertEqual(
												    __A		,     [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]		,     )

												_A								= text_generator(__A		,     stop_sequence=''' fe'''   )
												self.assertEqual(__A		,     [{'''generated_text''': '''Hello I believe in fe'''}]   )

							def       __A (  self:   int		,     __A:   List[str]		,     __A:   List[str]   )    ->				str:
												_A								= text_generator.model
												_A								= text_generator.tokenizer

												_A								= text_generator('''This is a test'''   )
												self.assertEqual(__A		,     [{'''generated_text''': ANY(__A   )}]   )
												self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''   )   )

												_A								= text_generator('''This is a test'''		,     return_full_text=__A   )
												self.assertEqual(__A		,     [{'''generated_text''': ANY(__A   )}]   )
												self.assertNotIn('''This is a test'''		,     outputs[0]['''generated_text''']   )

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

												_A								= text_generator('''This is a test'''		,     return_full_text=__A   )
												self.assertEqual(__A		,     [{'''generated_text''': ANY(__A   )}]   )
												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=__A   )
												self.assertEqual(
												    __A		,     [
												        [{'''generated_text''': ANY(__A   )}, {'''generated_text''': ANY(__A   )}],
												        [{'''generated_text''': ANY(__A   )}, {'''generated_text''': ANY(__A   )}],
												    ]		,     )

												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=__A   )
																	self.assertEqual(
																	    __A		,     [
																	        [{'''generated_text''': ANY(__A   )}, {'''generated_text''': ANY(__A   )}],
																	        [{'''generated_text''': ANY(__A   )}, {'''generated_text''': ANY(__A   )}],
																	    ]		,     )

												with self.assertRaises(__A   ):
																	_A								= text_generator('''test'''		,     return_full_text=__A		,     return_text=__A   )
												with self.assertRaises(__A   ):
																	_A								= text_generator('''test'''		,     return_full_text=__A		,     return_tensors=__A   )
												with self.assertRaises(__A   ):
																	_A								= text_generator('''test'''		,     return_text=__A		,     return_tensors=__A   )

												# 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(__A		,     [{'''generated_text''': ANY(__A   )}]   )
												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 < 1_00_00
												    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''' * 5_00		,     max_new_tokens=20   )

																	_A								= text_generator('''This is a test''' * 5_00		,     handle_long_generation='''hole'''		,     max_new_tokens=20   )
																	# Hole strategy cannot work
																	with self.assertRaises(__A   ):
																						text_generator(
																						    '''This is a test''' * 5_00		,     handle_long_generation='''hole'''		,     max_new_tokens=tokenizer.model_max_length + 10		,     )

							@require_torch
							@require_accelerate
							@require_torch_gpu
							def       __A (  self:   Union[str, Any]   )    ->				Union[str, Any]:
												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(
												    __A		,     [
												        {
												            '''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(
												    __A		,     [
												        {
												            '''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(
												    __A		,     [
												        {
												            '''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       __A (  self:   int   )    ->				List[str]:
												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       __A (  self:   Optional[int]   )    ->				List[Any]:
												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=__A		,     top_p=0.5   )



							def       __A (  self:   str   )    ->				int:
												_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(__A   ) as cl:
																	_A								= text_generator(__A		,     max_length=10		,     max_new_tokens=1   )
												self.assertIn(__A		,     cl.out   )

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

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











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).text					,			'''html.parser''' )
					_A								= soup.find_all('''td'''					,			attrs='''titleColumn''' )
					_A								= soup.find_all('''td'''					,			class_='''ratingColumn imdbRating''' )
					return {
					    title.a.text: float(rating.strong.text )
					    for title, rating in zip(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



