

import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptTokenizer,
      )
      from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class   _a					:




      """simple docstring"""

      def __init__(   self							:				str						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				List[Any]=13						,			UpperCAmelCase							:				List[str]=7						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				List[Any]=True						,			UpperCAmelCase							:				Dict=False						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				int=99						,			UpperCAmelCase							:				int=32						,			UpperCAmelCase							:				str=5						,			UpperCAmelCase							:				int=4						,			UpperCAmelCase							:				Optional[int]=37						,			UpperCAmelCase							:				Union[str, Any]="gelu"						,			UpperCAmelCase							:				List[Any]=0.1						,			UpperCAmelCase							:				Union[str, Any]=0.1						,			UpperCAmelCase							:				Optional[int]=512						,			UpperCAmelCase							:				List[Any]=16						,			UpperCAmelCase							:				Optional[Any]=2						,			UpperCAmelCase							:				Optional[Any]=0.02						,			UpperCAmelCase							:				Optional[Any]=3						,			UpperCAmelCase							:				Optional[Any]=4						,			UpperCAmelCase							:				Optional[Any]=None						,			):
            A_						  =				parent
            A_						  =				batch_size
            A_						  =				seq_length
            A_						  =				is_training
            A_						  =				use_input_mask
            A_						  =				use_token_type_ids
            A_						  =				use_labels
            A_						  =				vocab_size
            A_						  =				hidden_size
            A_						  =				num_hidden_layers
            A_						  =				num_attention_heads
            A_						  =				intermediate_size
            A_						  =				hidden_act
            A_						  =				hidden_dropout_prob
            A_						  =				attention_probs_dropout_prob
            A_						  =				max_position_embeddings
            A_						  =				type_vocab_size
            A_						  =				type_sequence_label_size
            A_						  =				initializer_range
            A_						  =				num_labels
            A_						  =				num_choices
            A_						  =				scope





      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size	)

            A_						  =				None
            if self.use_input_mask:
                  A_						  =				random_attention_mask([self.batch_size, self.seq_length]	)

            A_						  =				None
            if self.use_token_type_ids:
                  A_						  =				ids_tensor([self.batch_size, self.seq_length]						,			self.type_vocab_size	)

            A_						  =				None
            A_						  =				None
            A_						  =				None
            if self.use_labels:
                  A_						  =				ids_tensor([self.batch_size]						,			self.type_sequence_label_size	)
                  A_						  =				ids_tensor([self.batch_size, self.seq_length]						,			self.num_labels	)
                  A_						  =				ids_tensor([self.batch_size]						,			self.num_choices	)

            A_						  =				self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





      def 		__A	(   self							:				Dict	):
            return BioGptConfig(
                vocab_size=self.vocab_size						,			hidden_size=self.hidden_size						,			num_hidden_layers=self.num_hidden_layers						,			num_attention_heads=self.num_attention_heads						,			intermediate_size=self.intermediate_size						,			hidden_act=self.hidden_act						,			hidden_dropout_prob=self.hidden_dropout_prob						,			attention_probs_dropout_prob=self.attention_probs_dropout_prob						,			max_position_embeddings=self.max_position_embeddings						,			type_vocab_size=self.type_vocab_size						,			is_decoder=UpperCAmelCase						,			initializer_range=self.initializer_range						,			)





      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				List[str]	):
            A_						  =				BioGptModel(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase	)
            A_						  =				model(UpperCAmelCase	)
            self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)	)





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				List[str]						,			):
            A_						  =				BioGptForCausalLM(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase						,			labels=UpperCAmelCase	)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)	)





      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Any						,			*UpperCAmelCase							:				List[Any]	):
            A_						  =				BioGptModel(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()

            # create attention mask
            A_						  =				torch.ones(input_ids.shape						,			dtype=torch.long						,			device=UpperCAmelCase	)
            A_						  =				self.seq_length // 2
            A_						  =				0

            # first forward pass
            A_  ,     A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase	).to_tuple()

            # create hypothetical next token and extent to next_input_ids
            A_						  =				ids_tensor((self.batch_size, 1)						,			config.vocab_size	)

            # change a random masked slice from input_ids
            A_						  =				ids_tensor((1,)						,			UpperCAmelCase	).item() + 1
            A_						  =				ids_tensor((self.batch_size, 1)						,			config.vocab_size	).squeeze(-1	)
            A_						  =				random_other_next_tokens

            # append to next input_ids and attn_mask
            A_						  =				torch.cat([input_ids, next_tokens]						,			dim=-1	)
            A_						  =				torch.cat(
                [attn_mask, torch.ones((attn_mask.shape[0], 1)						,			dtype=torch.long						,			device=UpperCAmelCase	)]						,			dim=1						,			)

            # get two different outputs
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase	)["last_hidden_state"]
            A_						  =				model(UpperCAmelCase						,			past_key_values=UpperCAmelCase						,			attention_mask=UpperCAmelCase	)["last_hidden_state"]

            # select random slice
            A_						  =				ids_tensor((1,)						,			output_from_past.shape[-1]	).item()
            A_						  =				output_from_no_past[:, -1, random_slice_idx].detach()
            A_						  =				output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(UpperCAmelCase						,			UpperCAmelCase						,			atol=1E-3	)	)





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				List[str]						,			*UpperCAmelCase							:				Any	):
            A_						  =				BioGptModel(config=UpperCAmelCase	).to(UpperCAmelCase	).eval()

            A_						  =				torch.ones(input_ids.shape						,			dtype=torch.long						,			device=UpperCAmelCase	)

            # first forward pass
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			use_cache=UpperCAmelCase	)

            A_  ,     A_						  =				outputs.to_tuple()

            # create hypothetical multiple next token and extent to next_input_ids
            A_						  =				ids_tensor((self.batch_size, 3)						,			config.vocab_size	)
            A_						  =				ids_tensor((self.batch_size, 3)						,			2	)

            # append to next input_ids and
            A_						  =				torch.cat([input_ids, next_tokens]						,			dim=-1	)
            A_						  =				torch.cat([attention_mask, next_attn_mask]						,			dim=-1	)

            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase	)["last_hidden_state"]
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			past_key_values=UpperCAmelCase	)[
                "last_hidden_state"
            ]

            # select random slice
            A_						  =				ids_tensor((1,)						,			output_from_past.shape[-1]	).item()
            A_						  =				output_from_no_past[:, -3:, random_slice_idx].detach()
            A_						  =				output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(UpperCAmelCase						,			UpperCAmelCase						,			atol=1E-3	)	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				Any						,			*UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Tuple=False	):
            A_						  =				BioGptForCausalLM(UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            if gradient_checkpointing:
                  model.gradient_checkpointing_enable()

            A_						  =				model(UpperCAmelCase						,			labels=UpperCAmelCase	)
            self.parent.assertEqual(result.loss.shape						,			()	)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)	)
            result.loss.backward()





      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				Tuple						,			*UpperCAmelCase							:				Any	):
            A_						  =				BioGptModel(UpperCAmelCase	)
            A_						  =				model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers	)
            for key in model.state_dict().keys():
                  if "c_proj" in key and "weight" in key:
                        self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]	) - model_std	)						,			0.001	)
                        self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]	) - 0.0	)						,			0.01	)





      def 		__A	(   self							:				Optional[int]						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Dict						,			*UpperCAmelCase							:				str	):
            A_						  =				self.num_labels
            A_						  =				BioGptForTokenClassification(UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase	)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.num_labels)	)







      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				self.prepare_config_and_inputs()
            (
                (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     
            )						  =				config_and_inputs
            A_						  =				{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict




@require_torch
class   _a					(			snake_case_     ,				snake_case_     ,				snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Tuple      		=       (
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      _lowerCamelCase       :			int      		=       (BioGptForCausalLM,) if is_torch_available() else ()
      _lowerCamelCase       :			int      		=       (
          {
              'feature-extraction': BioGptModel,
              'text-classification': BioGptForSequenceClassification,
              'text-generation': BioGptForCausalLM,
              'token-classification': BioGptForTokenClassification,
              'zero-shot': BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _lowerCamelCase       :			Union[str, Any]      		=       False





      def 		__A	(   self							:				Tuple	):
            A_						  =				BioGptModelTester(self	)
            A_						  =				ConfigTester(self						,			config_class=UpperCAmelCase						,			hidden_size=37	)





      def 		__A	(   self							:				int	):
            self.config_tester.run_common_tests()





      def 		__A	(   self							:				List[str]	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  A_						  =				type
                  self.model_tester.create_and_check_model(*UpperCAmelCase	)





      def 		__A	(   self							:				Dict	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCAmelCase	)





      def 		__A	(   self							:				Any	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*UpperCAmelCase						,			gradient_checkpointing=UpperCAmelCase	)





      def 		__A	(   self							:				str	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCAmelCase	)





      def 		__A	(   self							:				str	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCAmelCase	)





      def 		__A	(   self							:				Optional[int]	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCAmelCase	)





      @slow
      def 		__A	(   self							:				List[Any]	):
            A_						  =				BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
            model.to(UpperCAmelCase	)
            A_						  =				BioGptTokenizer.from_pretrained("microsoft/biogpt"	)

            A_						  =				"left"

            # Define PAD Token = EOS Token = 50256
            A_						  =				tokenizer.eos_token
            A_						  =				model.config.eos_token_id

            # use different length sentences to test batching
            A_						  =				[
                "Hello, my dog is a little",
                "Today, I",
            ]

            A_						  =				tokenizer(UpperCAmelCase						,			return_tensors="pt"						,			padding=UpperCAmelCase	)
            A_						  =				inputs["input_ids"].to(UpperCAmelCase	)

            A_						  =				model.generate(
                input_ids=UpperCAmelCase						,			attention_mask=inputs["attention_mask"].to(UpperCAmelCase	)						,			)

            A_						  =				tokenizer(sentences[0]						,			return_tensors="pt"	).input_ids.to(UpperCAmelCase	)
            A_						  =				model.generate(input_ids=UpperCAmelCase	)

            A_						  =				inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
            A_						  =				tokenizer(sentences[1]						,			return_tensors="pt"	).input_ids.to(UpperCAmelCase	)
            A_						  =				model.generate(input_ids=UpperCAmelCase						,			max_length=model.config.max_length - num_paddings	)

            A_						  =				tokenizer.batch_decode(UpperCAmelCase						,			skip_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.decode(output_non_padded[0]						,			skip_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.decode(output_padded[0]						,			skip_special_tokens=UpperCAmelCase	)

            A_						  =				[
                "Hello, my dog is a little bit bigger than a little bit.",
                "Today, I have a good idea of how to use the information",
            ]
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)
            self.assertListEqual(UpperCAmelCase						,			[non_padded_sentence, padded_sentence]	)





      @slow
      def 		__A	(   self							:				Any	):
            for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						  =				BioGptModel.from_pretrained(UpperCAmelCase	)
                  self.assertIsNotNone(UpperCAmelCase	)





      def 		__A	(   self							:				str	):
            A_  ,     A_						  =				self.model_tester.prepare_config_and_inputs_for_common()
            A_						  =				3
            A_						  =				input_dict["input_ids"]
            A_						  =				input_ids.ne(1	).to(UpperCAmelCase	)
            A_						  =				ids_tensor([self.model_tester.batch_size]						,			self.model_tester.type_sequence_label_size	)
            A_						  =				BioGptForSequenceClassification(UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			labels=UpperCAmelCase	)
            self.assertEqual(result.logits.shape						,			(self.model_tester.batch_size, self.model_tester.num_labels)	)







      def 		__A	(   self							:				Optional[Any]	):
            A_  ,     A_						  =				self.model_tester.prepare_config_and_inputs_for_common()
            A_						  =				3
            A_						  =				"multi_label_classification"
            A_						  =				input_dict["input_ids"]
            A_						  =				input_ids.ne(1	).to(UpperCAmelCase	)
            A_						  =				ids_tensor(
                [self.model_tester.batch_size, config.num_labels]						,			self.model_tester.type_sequence_label_size	).to(torch.float	)
            A_						  =				BioGptForSequenceClassification(UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			labels=UpperCAmelCase	)
            self.assertEqual(result.logits.shape						,			(self.model_tester.batch_size, self.model_tester.num_labels)	)




@require_torch
class   _a					(			unittest.TestCase				):




      """simple docstring"""

      @slow
      def 		__A	(   self							:				Dict	):
            A_						  =				BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
            A_						  =				torch.tensor([[2, 4805, 9, 656, 21]]	)
            A_						  =				model(UpperCAmelCase	)[0]

            A_						  =				42384

            A_						  =				torch.Size((1, 5, vocab_size)	)
            self.assertEqual(output.shape						,			UpperCAmelCase	)

            A_						  =				torch.tensor(
                [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]	)

            self.assertTrue(torch.allclose(output[:, :3, :3]						,			UpperCAmelCase						,			atol=1E-4	)	)







      @slow
      def 		__A	(   self							:				Tuple	):
            A_						  =				BioGptTokenizer.from_pretrained("microsoft/biogpt"	)
            A_						  =				BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
            model.to(UpperCAmelCase	)

            torch.manual_seed(0	)
            A_						  =				tokenizer("COVID-19 is"						,			return_tensors="pt"	).to(UpperCAmelCase	)
            A_						  =				model.generate(
                **UpperCAmelCase						,			min_length=100						,			max_length=1024						,			num_beams=5						,			early_stopping=UpperCAmelCase						,			)
            A_						  =				tokenizer.decode(output_ids[0]						,			skip_special_tokens=UpperCAmelCase	)

            A_						  =				(
                "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
                " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
                " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
                " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
                " more than 800,000 deaths."
            )
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)



'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).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(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




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


if __name__ == "__main__":
					write_movies()



