



import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.generation import (
				    FlaxForcedBOSTokenLogitsProcessor,
				    FlaxForcedEOSTokenLogitsProcessor,
				    FlaxLogitsProcessorList,
				    FlaxMinLengthLogitsProcessor,
				    FlaxTemperatureLogitsWarper,
				    FlaxTopKLogitsWarper,
				    FlaxTopPLogitsWarper,
				)



@require_flax
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   jnp.ones((batch_size, length)		) / length
						return scores




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   None
						a					     =   20

						a					     =   self._get_uniform_logits(batch_size=2		,       length=lowerCamelCase_		)

						# tweak scores to not be uniform anymore
						a					     =   scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
						a					     =   scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

						# compute softmax
						a					     =   jax.nn.softmax(lowerCamelCase_		,       axis=-1		)

						a					     =   FlaxTemperatureLogitsWarper(temperature=0.5		)
						a					     =   FlaxTemperatureLogitsWarper(temperature=1.3		)

						a					     =   jax.nn.softmax(temp_dist_warper_sharper(lowerCamelCase_		,       scores.copy()		,       cur_len=lowerCamelCase_		)		,       axis=-1		)
						a					     =   jax.nn.softmax(temp_dist_warper_smoother(lowerCamelCase_		,       scores.copy()		,       cur_len=lowerCamelCase_		)		,       axis=-1		)

						# uniform distribution stays uniform
						self.assertTrue(jnp.allclose(probs[0, :]		,       warped_prob_sharp[0, :]		,       atol=1E-3		)		)
						self.assertTrue(jnp.allclose(probs[0, :]		,       warped_prob_smooth[0, :]		,       atol=1E-3		)		)

						# sharp peaks get higher, valleys get lower
						self.assertLess(probs[1, :].max()		,       warped_prob_sharp[1, :].max()		)
						self.assertGreater(probs[1, :].min()		,       warped_prob_sharp[1, :].min()		)

						# smooth peaks get lower, valleys get higher
						self.assertGreater(probs[1, :].max()		,       warped_prob_smooth[1, :].max()		)
						self.assertLess(probs[1, :].min()		,       warped_prob_smooth[1, :].min()		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   None
						a					     =   10
						a					     =   2

						# create ramp distribution
						a					     =   np.broadcast_to(np.arange(lowerCamelCase_		)[None, :]		,       (batch_size, vocab_size)		).copy()
						a					     =   ramp_logits[1:, : vocab_size // 2] + vocab_size

						a					     =   FlaxTopKLogitsWarper(3		)

						a					     =   top_k_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)

						# check that correct tokens are filtered
						self.assertListEqual(jnp.isinf(scores[0]		).tolist()		,       7 * [True] + 3 * [False]		)
						self.assertListEqual(jnp.isinf(scores[1]		).tolist()		,       2 * [True] + 3 * [False] + 5 * [True]		)

						# check special case
						a					     =   5
						a					     =   FlaxTopKLogitsWarper(top_k=1		,       filter_value=0.0		,       min_tokens_to_keep=3		)

						a					     =   np.broadcast_to(np.arange(lowerCamelCase_		)[None, :]		,       (batch_size, length)		).copy()
						a					     =   top_k_warp_safety_check(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)

						# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
						self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()		,       [2, 2]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   None
						a					     =   10
						a					     =   2

						# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
						a					     =   np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]		)		)

						a					     =   FlaxTopPLogitsWarper(0.8		)
						a					     =   np.exp(top_p_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)		)

						# dist should be filtered to keep min num values so that sum is >= top_p
						# exp (-inf) => 0
						a					     =   np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]		)
						self.assertTrue(np.allclose(lowerCamelCase_		,       lowerCamelCase_		,       atol=1E-3		)		)

						# check edge cases with negative and extreme logits
						a					     =   np.broadcast_to(np.arange(lowerCamelCase_		)[None, :]		,       (batch_size, vocab_size)		).copy() - (
						    vocab_size // 2
						)

						# make ramp_logits more extreme
						a					     =   ramp_logits[1] * 100.0

						# make sure at least 2 tokens are kept
						a					     =   FlaxTopPLogitsWarper(0.9		,       min_tokens_to_keep=2		,       filter_value=0.0		)
						a					     =   top_p_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)

						# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
						self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()		,       [3, 2]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   20
						a					     =   4
						a					     =   0

						a					     =   FlaxMinLengthLogitsProcessor(min_length=10		,       eos_token_id=lowerCamelCase_		)

						# check that min length is applied at length 5
						a					     =   ids_tensor((batch_size, 20)		,       vocab_size=20		)
						a					     =   5
						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   min_dist_processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()		,       4 * [-float("inf"		)]		)

						# check that min length is not applied anymore at length 15
						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   15
						a					     =   min_dist_processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						self.assertFalse(jnp.isinf(lowerCamelCase_		).any()		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   20
						a					     =   4
						a					     =   0

						a					     =   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_		)

						# check that all scores are -inf except the bos_token_id score
						a					     =   ids_tensor((batch_size, 1)		,       vocab_size=20		)
						a					     =   1
						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   logits_processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
						self.assertListEqual(scores[:, bos_token_id].tolist()		,       4 * [0]		)  # score for bos_token_id shold be zero

						# check that bos_token_id is not forced if current length is greater than 1
						a					     =   3
						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   logits_processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						self.assertFalse(jnp.isinf(lowerCamelCase_		).any()		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   20
						a					     =   4
						a					     =   0
						a					     =   5

						a					     =   FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		)

						# check that all scores are -inf except the eos_token_id when max_length is reached
						a					     =   ids_tensor((batch_size, 4)		,       vocab_size=20		)
						a					     =   4
						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   logits_processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
						self.assertListEqual(scores[:, eos_token_id].tolist()		,       4 * [0]		)  # score for eos_token_id should be zero

						# check that eos_token_id is not forced if max_length is not reached
						a					     =   3
						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   logits_processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						self.assertFalse(jnp.isinf(lowerCamelCase_		).any()		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   4
						a					     =   10
						a					     =   15
						a					     =   2
						a					     =   1
						a					     =   15

						# dummy input_ids and scores
						a					     =   ids_tensor((batch_size, sequence_length)		,       lowerCamelCase_		)
						a					     =   input_ids.copy()

						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   scores.copy()

						# instantiate all dist processors
						a					     =   FlaxTemperatureLogitsWarper(temperature=0.5		)
						a					     =   FlaxTopKLogitsWarper(3		)
						a					     =   FlaxTopPLogitsWarper(0.8		)

						# instantiate all logits processors
						a					     =   FlaxMinLengthLogitsProcessor(min_length=10		,       eos_token_id=lowerCamelCase_		)
						a					     =   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_		)
						a					     =   FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		)

						a					     =   10

						# no processor list
						a					     =   temp_dist_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						a					     =   top_k_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						a					     =   top_p_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						a					     =   min_dist_proc(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						a					     =   bos_dist_proc(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
						a					     =   eos_dist_proc(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)

						# with processor list
						a					     =   FlaxLogitsProcessorList(
						    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
						a					     =   processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)

						# scores should be equal
						self.assertTrue(jnp.allclose(lowerCamelCase_		,       lowerCamelCase_		,       atol=1E-3		)		)

						# input_ids should never be changed
						self.assertListEqual(input_ids.tolist()		,       input_ids_comp.tolist()		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   4
						a					     =   10
						a					     =   15
						a					     =   2
						a					     =   1
						a					     =   15

						# dummy input_ids and scores
						a					     =   ids_tensor((batch_size, sequence_length)		,       lowerCamelCase_		)
						a					     =   input_ids.copy()

						a					     =   self._get_uniform_logits(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   scores.copy()

						# instantiate all dist processors
						a					     =   FlaxTemperatureLogitsWarper(temperature=0.5		)
						a					     =   FlaxTopKLogitsWarper(3		)
						a					     =   FlaxTopPLogitsWarper(0.8		)

						# instantiate all logits processors
						a					     =   FlaxMinLengthLogitsProcessor(min_length=10		,       eos_token_id=lowerCamelCase_		)
						a					     =   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_		)
						a					     =   FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		)

						a					     =   10

						# no processor list
						def run_no_processor_list(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   temp_dist_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											a					     =   top_k_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											a					     =   top_p_warp(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											a					     =   min_dist_proc(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											a					     =   bos_dist_proc(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											a					     =   eos_dist_proc(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											return scores

						# with processor list
						def run_processor_list(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   FlaxLogitsProcessorList(
											    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
											a					     =   processor(lowerCamelCase_		,       lowerCamelCase_		,       cur_len=lowerCamelCase_		)
											return scores

						a					     =   jax.jit(lowerCamelCase_		)
						a					     =   jax.jit(lowerCamelCase_		)

						a					     =   jitted_run_no_processor_list(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   jitted_run_processor_list(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						# scores should be equal
						self.assertTrue(jnp.allclose(lowerCamelCase_		,       lowerCamelCase_		,       atol=1E-3		)		)

						# input_ids should never be changed
						self.assertListEqual(input_ids.tolist()		,       input_ids_comp.tolist()		)





'''simple docstring'''




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


a   :			List[str]					  =    get_tests_dir("""fixtures""")

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			int:
								# A mock response for an HTTP head request to emulate server down
								UpperCAmelCase     :		Tuple							     =		mock.Mock()
								UpperCAmelCase     :		List[str]							     =		500
								UpperCAmelCase     :		Any							     =		{}
								UpperCAmelCase     :		List[str]							     =		HTTPError
								UpperCAmelCase     :		str							     =		{}

								# Download this model to make sure it's in the cache.
								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch("""requests.Session.request"""		,		return_value=A    ) as mock_head:
															UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
															# This check we did call the fake head request
															mock_head.assert_called()





	def  _lowercase(  self    )			->			Any:
								# This test is for deprecated behavior and can be removed in v5
								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(
								    """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""    )






	def  _lowercase(  self    )			->			Union[str, Any]:
								with self.assertRaises(A    ):
															# config is in subfolder, the following should not work without specifying the subfolder
															UpperCAmelCase     :		Any							     =		AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    """hf-internal-testing/stable-diffusion-all-variants"""		,		subfolder="""feature_extractor"""    )

								self.assertIsNotNone(A    )




@is_staging_test
class 			UpperCamelCase_ (							unittest.TestCase			):





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		TOKEN
								HfFolder.save_token(A    )





	@classmethod
	def  _lowercase(  cls    )			->			List[str]:
								try:
															delete_repo(token=cls._token		,		repo_id="""test-image-processor"""    )
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,		repo_id="""valid_org/test-image-processor-org"""    )
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,		repo_id="""test-dynamic-image-processor"""    )
								except HTTPError:
															pass





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Union[str, Any]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# Reset repo
								delete_repo(token=self._token		,		repo_id="""test-image-processor"""    )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    A		,		repo_id="""test-image-processor"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		List[str]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""valid_org/test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# Reset repo
								delete_repo(token=self._token		,		repo_id="""valid_org/test-image-processor"""    )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    A		,		repo_id="""valid_org/test-image-processor-org"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		int							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )






	def  _lowercase(  self    )			->			Optional[int]:
								CustomImageProcessor.register_for_auto_class()
								UpperCAmelCase     :		Optional[Any]							     =		CustomImageProcessor.from_pretrained(A    )

								image_processor.push_to_hub("""test-dynamic-image-processor"""		,		use_auth_token=self._token    )

								# This has added the proper auto_map field to the config
								self.assertDictEqual(
								    image_processor.auto_map		,		{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}		,		)

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    f'''{USER}/test-dynamic-image-processor'''		,		trust_remote_code=A    )
								# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
								self.assertEqual(new_image_processor.__class__.__name__		,		"""CustomImageProcessor"""    )

